[2025-11-12 22:10:01,951][mllm.models.large_language_model_local][INFO] - Initializing adapter 'agent_adapter': no initial weights provided or found; starting from scratch. [2025-11-12 22:10:02,757][mllm.models.adapter_training_wrapper][INFO] - Adapter 'agent_adapter': initialized with fresh weights (no initial weights found). [2025-11-12 22:10:02,764][mllm.models.large_language_model_local][INFO] - Initializing adapter 'critic_adapter': no initial weights provided or found; starting from scratch. [2025-11-12 22:10:03,883][mllm.models.adapter_training_wrapper][INFO] - Adapter 'critic_adapter': initialized with fresh weights (no initial weights found). [2025-11-12 22:12:16,595][__main__][INFO] - Starting iteration 0. [2025-11-12 22:12:16,598][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 0 and human policies 1. [2025-11-12 22:12:16,599][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 22:12:30,459][__main__][INFO] - Number of regex retries in iteration 0: 0 [2025-11-12 22:12:30,460][__main__][INFO] - agents played in iteration 0 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 22:12:43,098][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 37.45%, Block Peak % of device VRAM: 18.68%, ΔTime: 00:00:00 [2025-11-12 22:12:43,125][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 37.45%, Block Peak % of device VRAM: 18.68%, ΔTime: 00:00:00 [2025-11-12 22:12:43,151][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 37.45%, Block Peak % of device VRAM: 18.68%, ΔTime: 00:00:00 [2025-11-12 22:12:43,174][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 37.45%, Block Peak % of device VRAM: 18.68%, ΔTime: 00:00:00 [2025-11-12 22:12:43,175][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 22:12:43,175][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 22:12:43,760][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 22:12:44,596][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 22:12:45,106][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 22:12:45,606][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 22:12:46,100][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 22:12:46,592][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 22:12:47,084][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 22:12:47,575][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 22:12:48,083][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 22:12:48,575][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 22:12:49,068][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-12 22:12:49,565][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-12 22:12:50,065][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-12 22:12:50,560][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-12 22:12:51,060][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-12 22:12:51,557][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-12 22:12:52,051][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-12 22:12:52,548][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-12 22:12:53,041][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-12 22:12:53,540][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-12 22:12:54,037][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-12 22:12:54,535][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-12 22:12:55,030][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-12 22:12:55,524][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-12 22:12:56,015][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-12 22:12:56,510][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-12 22:12:57,020][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-12 22:12:57,512][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-12 22:12:58,006][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-12 22:12:58,505][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-12 22:12:59,000][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-12 22:12:59,496][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-12 22:12:59,992][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-12 22:13:00,487][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 22:13:01,009][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 22:13:01,505][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 22:13:02,000][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 22:13:02,531][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 22:13:03,027][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 22:13:03,520][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 22:13:04,020][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 22:13:04,513][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 22:13:05,024][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 22:13:05,518][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 22:13:06,012][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 22:13:06,505][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 22:13:07,003][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 22:13:07,515][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 22:13:08,013][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 22:13:08,507][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 22:13:09,003][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 22:13:09,499][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 22:13:10,003][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 22:13:10,501][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 22:13:10,995][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 22:13:11,505][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 22:13:11,998][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 22:13:12,493][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 22:13:12,995][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 22:13:13,491][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 22:13:13,992][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 22:13:14,486][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 22:13:14,983][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 22:13:15,476][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 22:13:15,971][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 9690 tokens. [2025-11-12 22:13:16,708][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 15.95%, Current % of VRAM taken: 53.40%, Block Peak % of device VRAM: 60.96%, ΔTime: 00:00:32 [2025-11-12 22:13:17,417][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 22:13:17,421][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 22:13:17,423][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 22:13:18,667][__main__][INFO] - Iteration 1 took 1m 2s (22.33% Gen, 75.66% Train). Generation: 13s, Training: 46s. Estimated remaining time: 51h 39m 49s. Estimated total time: 51h 43m 26s. Time estimates for 10 more iterations: 10m 20s, 100 more iterations: 1h 43m 26s, 500 more iterations: 8h 37m 14s. [2025-11-12 22:13:18,669][__main__][INFO] - Starting iteration 1. [2025-11-12 22:13:19,099][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 0 and human policies 1. [2025-11-12 22:13:19,099][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 22:13:32,197][__main__][INFO] - Number of regex retries in iteration 1: 0 [2025-11-12 22:13:32,198][__main__][INFO] - agents played in iteration 1 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 22:13:33,079][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 46.45%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:13:33,106][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 46.45%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:13:33,133][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 46.45%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:13:33,156][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 46.45%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:13:33,157][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 22:13:33,157][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 22:13:33,846][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 22:13:34,295][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 22:13:34,797][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 22:13:35,289][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 22:13:35,780][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 22:13:36,270][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 22:13:36,766][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 22:13:37,255][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 22:13:37,744][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 22:13:38,232][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 22:13:38,720][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-12 22:13:39,211][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-12 22:13:39,699][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-12 22:13:40,214][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-12 22:13:40,705][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-12 22:13:41,195][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-12 22:13:41,686][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-12 22:13:42,174][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-12 22:13:42,663][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-12 22:13:43,153][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-12 22:13:43,645][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-12 22:13:44,137][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-12 22:13:44,627][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-12 22:13:45,117][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-12 22:13:45,609][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-12 22:13:46,101][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-12 22:13:46,590][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-12 22:13:47,081][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-12 22:13:47,573][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-12 22:13:48,064][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-12 22:13:48,554][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-12 22:13:49,048][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-12 22:13:49,542][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-12 22:13:50,031][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 22:13:50,526][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 22:13:51,016][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 22:13:51,508][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 22:13:52,002][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 22:13:52,495][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 22:13:52,985][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 22:13:53,477][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 22:13:53,970][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 22:13:54,460][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 22:13:54,949][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 22:13:55,440][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 22:13:55,937][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 22:13:56,427][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 22:13:56,925][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 22:13:57,416][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 22:13:57,910][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 22:13:58,402][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 22:13:58,902][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 22:13:59,394][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 22:13:59,909][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 22:14:00,410][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 22:14:00,907][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 22:14:01,404][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 22:14:01,897][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 22:14:02,388][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 22:14:02,882][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 22:14:03,374][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 22:14:03,869][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 22:14:04,364][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 22:14:04,860][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 22:14:05,355][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 9622 tokens. [2025-11-12 22:14:05,995][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 13.98%, Current % of VRAM taken: 59.43%, Block Peak % of device VRAM: 61.62%, ΔTime: 00:00:32 [2025-11-12 22:14:06,720][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 22:14:06,722][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 22:14:06,724][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 22:14:07,655][__main__][INFO] - Iteration 2 took 48s (26.97% Gen, 71.10% Train). Generation: 13s, Training: 34s. Estimated remaining time: 40h 23m 25s. Estimated total time: 40h 27m 51s. Time estimates for 10 more iterations: 8m 5s, 100 more iterations: 1h 20m 55s, 500 more iterations: 6h 44m 38s. [2025-11-12 22:14:07,657][__main__][INFO] - Starting iteration 2. [2025-11-12 22:14:08,096][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 0 and human policies 1. [2025-11-12 22:14:08,096][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 22:14:18,176][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 0 books, 20 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 22:14:20,871][__main__][INFO] - Number of regex retries in iteration 2: 1 [2025-11-12 22:14:20,871][__main__][INFO] - agents played in iteration 2 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 22:14:21,738][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.47%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:14:21,765][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.47%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:14:21,791][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.47%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:14:21,813][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.47%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:14:21,814][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 22:14:21,815][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 22:14:22,436][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 22:14:22,886][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 22:14:23,390][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 22:14:23,887][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 22:14:24,385][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 22:14:24,886][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 22:14:25,380][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 22:14:25,872][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 22:14:26,371][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 22:14:26,866][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 22:14:27,361][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-12 22:14:27,854][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-12 22:14:28,352][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-12 22:14:28,850][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-12 22:14:29,352][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-12 22:14:29,847][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-12 22:14:30,342][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-12 22:14:30,835][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-12 22:14:31,340][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-12 22:14:31,836][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-12 22:14:32,329][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-12 22:14:32,823][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-12 22:14:33,314][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-12 22:14:33,816][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-12 22:14:34,309][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-12 22:14:34,800][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-12 22:14:35,295][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-12 22:14:35,787][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-12 22:14:36,279][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-12 22:14:36,769][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-12 22:14:37,260][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-12 22:14:37,754][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-12 22:14:38,245][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-12 22:14:38,736][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 22:14:39,242][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 22:14:39,733][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 22:14:40,224][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 22:14:40,717][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 22:14:41,208][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 22:14:41,708][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 22:14:42,199][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 22:14:42,691][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 22:14:43,182][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 22:14:43,675][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 22:14:44,173][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 22:14:44,663][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 22:14:45,154][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 22:14:45,652][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 22:14:46,146][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 22:14:46,637][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 22:14:47,140][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 22:14:47,632][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 22:14:48,124][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 22:14:48,635][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 22:14:49,128][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 22:14:49,620][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 22:14:50,122][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 22:14:50,615][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 22:14:51,134][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 22:14:51,627][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 22:14:52,118][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 22:14:52,630][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 22:14:53,125][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 22:14:53,618][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 22:14:54,122][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 9661 tokens. [2025-11-12 22:14:54,809][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 13.99%, Current % of VRAM taken: 59.44%, Block Peak % of device VRAM: 61.49%, ΔTime: 00:00:32 [2025-11-12 22:14:55,577][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 22:14:55,578][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 22:14:55,580][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 22:14:56,558][__main__][INFO] - Iteration 3 took 48s (26.36% Gen, 71.62% Train). Generation: 12s, Training: 34s. Estimated remaining time: 40h 17m 55s. Estimated total time: 40h 23m 10s. Time estimates for 10 more iterations: 8m 4s, 100 more iterations: 1h 20m 46s, 500 more iterations: 6h 43m 51s. [2025-11-12 22:14:56,560][__main__][INFO] - Starting iteration 3. [2025-11-12 22:14:57,014][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 0 and human policies 1. [2025-11-12 22:14:57,015][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 22:14:59,416][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 22:15:00,973][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 0 balls This proposal is based on the significant value discrepancy between our per-item values and Alice's. Since I value hats and balls much higher than Alice does, and she values books more, I should propose to take all the hats and balls for myself, leaving the books for her. This maximizes my potential points given the value differences. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 22:15:02,600][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 22:15:09,704][__main__][INFO] - Number of regex retries in iteration 3: 3 [2025-11-12 22:15:09,704][__main__][INFO] - agents played in iteration 3 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 22:15:10,719][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.46%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:15:10,749][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.46%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:15:10,782][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.46%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:15:10,806][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.46%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:15:10,807][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 22:15:10,808][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 22:15:11,451][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 22:15:11,903][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 22:15:12,421][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 22:15:12,925][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 22:15:13,433][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 22:15:13,929][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 22:15:14,424][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 22:15:14,921][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 22:15:15,418][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 22:15:15,910][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 22:15:16,401][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-12 22:15:16,893][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-12 22:15:17,387][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-12 22:15:17,877][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-12 22:15:18,371][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-12 22:15:18,865][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-12 22:15:19,355][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-12 22:15:19,849][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-12 22:15:20,342][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-12 22:15:20,835][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-12 22:15:21,344][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-12 22:15:21,837][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-12 22:15:22,329][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-12 22:15:22,830][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-12 22:15:23,323][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-12 22:15:23,821][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-12 22:15:24,318][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-12 22:15:24,812][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-12 22:15:25,308][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-12 22:15:25,801][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-12 22:15:26,294][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-12 22:15:26,788][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-12 22:15:27,281][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-12 22:15:27,775][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 22:15:28,270][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 22:15:28,763][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 22:15:29,264][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 22:15:29,756][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 22:15:30,251][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 22:15:30,744][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 22:15:31,237][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 22:15:31,744][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 22:15:32,236][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 22:15:32,727][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 22:15:33,225][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 22:15:33,717][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 22:15:34,210][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 22:15:34,701][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 22:15:35,194][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 22:15:35,693][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 22:15:36,188][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 22:15:36,681][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 22:15:37,174][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 22:15:37,666][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 22:15:38,164][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 22:15:38,658][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 22:15:39,152][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 22:15:39,656][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 22:15:40,148][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 22:15:40,637][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 22:15:41,128][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 22:15:41,619][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 22:15:42,118][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 22:15:42,613][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 22:15:43,108][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 9663 tokens. [2025-11-12 22:15:43,804][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 13.90%, Current % of VRAM taken: 59.35%, Block Peak % of device VRAM: 61.68%, ΔTime: 00:00:32 [2025-11-12 22:15:44,567][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 22:15:44,569][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 22:15:44,571][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 22:15:45,472][__main__][INFO] - Iteration 4 took 48s (26.19% Gen, 71.95% Train). Generation: 12s, Training: 34s. Estimated remaining time: 40h 16m 52s. Estimated total time: 40h 22m 55s. Time estimates for 10 more iterations: 8m 4s, 100 more iterations: 1h 20m 45s, 500 more iterations: 6h 43m 49s. [2025-11-12 22:15:45,474][__main__][INFO] - Starting iteration 4. [2025-11-12 22:15:45,910][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 0 and human policies 1. [2025-11-12 22:15:45,911][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 22:15:46,760][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 22:15:50,881][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 0 balls Given Bob's values for each item, he will heavily prioritize books and balls, which means he is likely to propose taking all of the books and balls for himself, leaving me with all the hats. Since my value for hats is 1, and my values for books and balls are both 10, proposing to take all the hats maximizes my points for this round. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 22:15:56,647][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 22:15:59,715][__main__][INFO] - Number of regex retries in iteration 4: 3 [2025-11-12 22:15:59,716][__main__][INFO] - agents played in iteration 4 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 22:16:00,536][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.44%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:16:00,562][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.44%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:16:00,588][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.44%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:16:00,611][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.44%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:16:00,611][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 22:16:00,612][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 22:16:01,351][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 22:16:01,811][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 22:16:02,310][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 22:16:02,810][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 22:16:03,308][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 22:16:03,799][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 22:16:04,293][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 22:16:04,782][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 22:16:05,273][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 22:16:05,767][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 22:16:06,261][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-12 22:16:06,751][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-12 22:16:07,244][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-12 22:16:07,734][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-12 22:16:08,230][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-12 22:16:08,719][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-12 22:16:09,212][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-12 22:16:09,708][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-12 22:16:10,198][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-12 22:16:10,689][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-12 22:16:11,183][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-12 22:16:11,673][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-12 22:16:12,169][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-12 22:16:12,659][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-12 22:16:13,150][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-12 22:16:13,642][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-12 22:16:14,138][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-12 22:16:14,630][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-12 22:16:15,133][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-12 22:16:15,625][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-12 22:16:16,124][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-12 22:16:16,615][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-12 22:16:17,107][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-12 22:16:17,600][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 22:16:18,094][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 22:16:18,585][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 22:16:19,075][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 22:16:19,565][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 22:16:20,062][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 22:16:20,552][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 22:16:21,043][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 22:16:21,559][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 22:16:22,055][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 22:16:22,559][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 22:16:23,053][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 22:16:23,546][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 22:16:24,058][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 22:16:24,552][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 22:16:25,048][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 22:16:25,540][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 22:16:26,037][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 22:16:26,553][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 22:16:27,045][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 22:16:27,538][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 22:16:28,036][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 22:16:28,527][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 22:16:29,019][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 22:16:29,510][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 22:16:30,000][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 22:16:30,493][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 22:16:30,984][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 22:16:31,480][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 22:16:31,973][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 22:16:32,468][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 22:16:32,968][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 9657 tokens. [2025-11-12 22:16:33,614][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 13.96%, Current % of VRAM taken: 59.41%, Block Peak % of device VRAM: 61.72%, ΔTime: 00:00:32 [2025-11-12 22:16:34,404][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 22:16:34,406][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 22:16:34,410][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 22:16:35,322][__main__][INFO] - Iteration 5 took 49s (27.94% Gen, 70.21% Train). Generation: 13s, Training: 34s. Estimated remaining time: 41h 3m 45s. Estimated total time: 41h 10m 39s. Time estimates for 10 more iterations: 8m 14s, 100 more iterations: 1h 22m 21s, 500 more iterations: 6h 51m 46s. [2025-11-12 22:16:35,324][__main__][INFO] - Starting iteration 5. [2025-11-12 22:16:35,771][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 0 and human policies 1. [2025-11-12 22:16:35,771][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 22:16:36,741][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 22:16:41,394][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 22:16:49,051][__main__][INFO] - Number of regex retries in iteration 5: 2 [2025-11-12 22:16:49,052][__main__][INFO] - agents played in iteration 5 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 22:16:49,869][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.48%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:16:49,895][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.48%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:16:49,920][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.48%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:16:49,942][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.48%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:16:49,943][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 22:16:49,943][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 22:16:50,608][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 22:16:51,059][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 22:16:51,556][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 22:16:52,053][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 22:16:52,544][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 22:16:53,034][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 22:16:53,524][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 22:16:54,016][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 22:16:54,508][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 22:16:54,998][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 22:16:55,489][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-12 22:16:55,980][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-12 22:16:56,470][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-12 22:16:56,969][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-12 22:16:57,458][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-12 22:16:57,946][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-12 22:16:58,437][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-12 22:16:58,929][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-12 22:16:59,425][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-12 22:16:59,929][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-12 22:17:00,419][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-12 22:17:00,911][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-12 22:17:01,401][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-12 22:17:01,896][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-12 22:17:02,388][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-12 22:17:02,883][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-12 22:17:03,378][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-12 22:17:03,874][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-12 22:17:04,367][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-12 22:17:04,872][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-12 22:17:05,370][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-12 22:17:05,861][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-12 22:17:06,369][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-12 22:17:06,859][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 22:17:07,349][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 22:17:07,838][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 22:17:08,329][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 22:17:08,820][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 22:17:09,311][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 22:17:09,801][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 22:17:10,293][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 22:17:10,787][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 22:17:11,279][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 22:17:11,770][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 22:17:12,261][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 22:17:12,762][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 22:17:13,256][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 22:17:13,749][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 22:17:14,247][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 22:17:14,739][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 22:17:15,231][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 22:17:15,723][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 22:17:16,213][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 22:17:16,705][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 22:17:17,195][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 22:17:17,684][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 22:17:18,184][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 22:17:18,674][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 22:17:19,164][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 22:17:19,653][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 22:17:20,144][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 22:17:20,644][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 22:17:21,138][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 22:17:21,631][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 22:17:22,134][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 9615 tokens. [2025-11-12 22:17:22,780][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 13.96%, Current % of VRAM taken: 59.41%, Block Peak % of device VRAM: 61.57%, ΔTime: 00:00:32 [2025-11-12 22:17:23,551][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 22:17:23,554][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 22:17:23,556][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 22:17:24,526][__main__][INFO] - Iteration 6 took 48s (27.24% Gen, 70.77% Train). Generation: 13s, Training: 34s. Estimated remaining time: 40h 30m 4s. Estimated total time: 40h 37m 47s. Time estimates for 10 more iterations: 8m 7s, 100 more iterations: 1h 21m 15s, 500 more iterations: 6h 46m 17s. [2025-11-12 22:17:24,528][__main__][INFO] - Starting iteration 6. [2025-11-12 22:17:24,982][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 0 and human policies 1. [2025-11-12 22:17:24,983][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 22:17:34,743][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 0 balls Given the values: - You value hats at 10. - Alice values hats at 1. - You value books at 10. - Alice values books at 10. - You value balls at 1. - Alice values balls at 10. Since you have a much higher value for hats compared to Alice, it makes sense to propose to take all the hats. This way, you can maximize your points from this round by taking full advantage of your high value for the hats. The other items, while valuable to others, are less valuable to you, so it's better to leave them to Alice. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 22:17:37,478][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 22:17:39,769][__main__][INFO] - Number of regex retries in iteration 6: 2 [2025-11-12 22:17:39,769][__main__][INFO] - agents played in iteration 6 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 22:17:40,661][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.45%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:17:40,692][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.45%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:17:40,720][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.45%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:17:40,744][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.45%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:17:40,744][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 22:17:40,745][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 22:17:41,394][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 22:17:41,846][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 22:17:42,344][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 22:17:42,837][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 22:17:43,330][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 22:17:43,825][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 22:17:44,318][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 22:17:44,812][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 22:17:45,310][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 22:17:45,802][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 22:17:46,295][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-12 22:17:46,791][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-12 22:17:47,282][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-12 22:17:47,774][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-12 22:17:48,286][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-12 22:17:48,778][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-12 22:17:49,270][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-12 22:17:49,763][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-12 22:17:50,253][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-12 22:17:50,745][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-12 22:17:51,236][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-12 22:17:51,727][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-12 22:17:52,218][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-12 22:17:52,710][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-12 22:17:53,201][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-12 22:17:53,690][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-12 22:17:54,183][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-12 22:17:54,682][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-12 22:17:55,172][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-12 22:17:55,663][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-12 22:17:56,156][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-12 22:17:56,649][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-12 22:17:57,140][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-12 22:17:57,634][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 22:17:58,126][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 22:17:58,619][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 22:17:59,115][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 22:17:59,611][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 22:18:00,108][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 22:18:00,600][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 22:18:01,093][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 22:18:01,583][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 22:18:02,075][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 22:18:02,579][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 22:18:03,071][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 22:18:03,562][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 22:18:04,056][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 22:18:04,548][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 22:18:05,041][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 22:18:05,533][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 22:18:06,026][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 22:18:06,519][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 22:18:07,012][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 22:18:07,505][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 22:18:07,998][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 22:18:08,490][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 22:18:08,982][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 22:18:09,475][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 22:18:09,966][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 22:18:10,482][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 22:18:10,978][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 22:18:11,472][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 22:18:11,970][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 22:18:12,465][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 22:18:12,961][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 9669 tokens. [2025-11-12 22:18:13,667][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 13.99%, Current % of VRAM taken: 59.44%, Block Peak % of device VRAM: 61.61%, ΔTime: 00:00:32 [2025-11-12 22:18:14,438][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 22:18:14,439][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 22:18:14,441][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 22:18:15,430][__main__][INFO] - Iteration 7 took 50s (29.31% Gen, 68.73% Train). Generation: 14s, Training: 34s. Estimated remaining time: 41h 53m 51s. Estimated total time: 42h 2m 25s. Time estimates for 10 more iterations: 8m 24s, 100 more iterations: 1h 24m 4s, 500 more iterations: 7h 0m 24s. [2025-11-12 22:18:15,433][__main__][INFO] - Starting iteration 7. [2025-11-12 22:18:15,894][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 0 and human policies 1. [2025-11-12 22:18:15,894][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 22:18:28,507][__main__][INFO] - Number of regex retries in iteration 7: 0 [2025-11-12 22:18:28,508][__main__][INFO] - agents played in iteration 7 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 22:18:29,329][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.46%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:18:29,354][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.46%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:18:29,379][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.46%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:18:29,401][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.46%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:18:29,402][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 22:18:29,403][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 22:18:30,049][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 22:18:30,509][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 22:18:31,005][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 22:18:31,501][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 22:18:31,993][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 22:18:32,483][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 22:18:32,991][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 22:18:33,485][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 22:18:33,975][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 22:18:34,473][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 22:18:34,964][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-12 22:18:35,455][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-12 22:18:35,944][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-12 22:18:36,435][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-12 22:18:36,928][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-12 22:18:37,420][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-12 22:18:37,911][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-12 22:18:38,402][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-12 22:18:38,893][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-12 22:18:39,395][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-12 22:18:39,890][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-12 22:18:40,382][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-12 22:18:40,877][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-12 22:18:41,371][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-12 22:18:41,862][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-12 22:18:42,365][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-12 22:18:42,856][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-12 22:18:43,348][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-12 22:18:43,840][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-12 22:18:44,332][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-12 22:18:44,824][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-12 22:18:45,316][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-12 22:18:45,810][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-12 22:18:46,302][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 22:18:46,793][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 22:18:47,294][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 22:18:47,787][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 22:18:48,280][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 22:18:48,790][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 22:18:49,283][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 22:18:49,774][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 22:18:50,271][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 22:18:50,762][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 22:18:51,254][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 22:18:51,746][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 22:18:52,237][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 22:18:52,730][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 22:18:53,221][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 22:18:53,713][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 22:18:54,211][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 22:18:54,703][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 22:18:55,196][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 22:18:55,690][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 22:18:56,186][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 22:18:56,694][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 22:18:57,186][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 22:18:57,679][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 22:18:58,178][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 22:18:58,678][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 22:18:59,187][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 22:18:59,686][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 22:19:00,181][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 22:19:00,679][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 22:19:01,212][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 22:19:01,708][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 9683 tokens. [2025-11-12 22:19:02,396][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.02%, Current % of VRAM taken: 59.47%, Block Peak % of device VRAM: 61.88%, ΔTime: 00:00:32 [2025-11-12 22:19:03,157][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 22:19:03,159][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 22:19:03,161][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 22:19:04,118][__main__][INFO] - Iteration 8 took 48s (26.15% Gen, 71.86% Train). Generation: 12s, Training: 34s. Estimated remaining time: 40h 1m 54s. Estimated total time: 40h 11m 16s. Time estimates for 10 more iterations: 8m 2s, 100 more iterations: 1h 20m 22s, 500 more iterations: 6h 41m 52s. [2025-11-12 22:19:04,120][__main__][INFO] - Starting iteration 8. [2025-11-12 22:19:04,581][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 0 and human policies 1. [2025-11-12 22:19:04,581][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 22:19:17,371][__main__][INFO] - Number of regex retries in iteration 8: 0 [2025-11-12 22:19:17,372][__main__][INFO] - agents played in iteration 8 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 22:19:18,196][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.48%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:19:18,221][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.48%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:19:18,247][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.48%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:19:18,269][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.48%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:19:18,269][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 22:19:18,270][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 22:19:18,935][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 22:19:19,387][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 22:19:19,881][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 22:19:20,374][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 22:19:20,863][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 22:19:21,353][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 22:19:21,846][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 22:19:22,336][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 22:19:22,827][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 22:19:23,323][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 22:19:23,818][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-12 22:19:24,311][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-12 22:19:24,809][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-12 22:19:25,313][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-12 22:19:25,823][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-12 22:19:26,325][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-12 22:19:26,819][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-12 22:19:27,311][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-12 22:19:27,808][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-12 22:19:28,298][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-12 22:19:28,791][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-12 22:19:29,289][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-12 22:19:29,779][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-12 22:19:30,270][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-12 22:19:30,762][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-12 22:19:31,254][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-12 22:19:31,749][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-12 22:19:32,240][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-12 22:19:32,732][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-12 22:19:33,222][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-12 22:19:33,713][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-12 22:19:34,206][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-12 22:19:34,696][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-12 22:19:35,187][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 22:19:35,701][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 22:19:36,192][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 22:19:36,681][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 22:19:37,179][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 22:19:37,671][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 22:19:38,165][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 22:19:38,656][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 22:19:39,146][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 22:19:39,639][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 22:19:40,130][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 22:19:40,623][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 22:19:41,113][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 22:19:41,605][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 22:19:42,113][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 22:19:42,604][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 22:19:43,095][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 22:19:43,589][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 22:19:44,081][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 22:19:44,572][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 22:19:45,062][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 22:19:45,552][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 22:19:46,044][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 22:19:46,536][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 22:19:47,027][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 22:19:47,521][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 22:19:48,014][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 22:19:48,509][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 22:19:49,001][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 22:19:49,496][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 22:19:50,005][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 22:19:50,498][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 9624 tokens. [2025-11-12 22:19:51,215][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 10.97%, Current % of VRAM taken: 56.42%, Block Peak % of device VRAM: 61.76%, ΔTime: 00:00:32 [2025-11-12 22:19:51,962][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 22:19:51,963][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 22:19:51,965][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 22:19:52,924][__main__][INFO] - Iteration 9 took 48s (26.46% Gen, 71.56% Train). Generation: 12s, Training: 34s. Estimated remaining time: 40h 6m 59s. Estimated total time: 40h 17m 10s. Time estimates for 10 more iterations: 8m 3s, 100 more iterations: 1h 20m 34s, 500 more iterations: 6h 42m 51s. [2025-11-12 22:19:52,926][__main__][INFO] - Starting iteration 9. [2025-11-12 22:19:53,387][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 0 and human policies 1. [2025-11-12 22:19:53,388][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 22:19:57,404][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 22:20:06,908][__main__][INFO] - Number of regex retries in iteration 9: 1 [2025-11-12 22:20:06,909][__main__][INFO] - agents played in iteration 9 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 22:20:07,789][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 49.48%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:20:07,811][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 49.48%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:20:07,834][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 49.48%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:20:07,857][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 49.48%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:20:07,858][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 22:20:07,859][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 22:20:08,529][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 22:20:08,978][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 22:20:09,490][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 22:20:09,985][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 22:20:10,478][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 22:20:10,982][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 22:20:11,479][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 22:20:11,971][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 22:20:12,463][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 22:20:12,958][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 22:20:13,452][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-12 22:20:13,944][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-12 22:20:14,464][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-12 22:20:14,958][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-12 22:20:15,452][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-12 22:20:15,948][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-12 22:20:16,440][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-12 22:20:16,933][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-12 22:20:17,427][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-12 22:20:17,919][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-12 22:20:18,414][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-12 22:20:18,907][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-12 22:20:24,844][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 22:20:25,335][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 22:20:25,826][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 22:20:26,322][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 22:20:26,814][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 22:20:27,305][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 22:20:27,815][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 22:20:28,307][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 22:20:28,801][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 22:20:29,296][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 22:20:29,789][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 22:20:30,299][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 22:20:30,791][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 22:20:31,282][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 22:20:31,790][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 22:20:32,282][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 22:20:32,773][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 22:20:33,264][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 22:20:33,755][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 22:20:34,250][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 22:20:34,741][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 22:20:35,235][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 22:20:35,727][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 22:20:36,220][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 22:20:36,722][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 22:20:37,219][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 22:20:37,716][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 22:20:38,214][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 22:20:38,710][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 22:20:39,202][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 22:20:39,698][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 22:20:40,192][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 9750 tokens. [2025-11-12 22:20:40,908][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 13.99%, Current % of VRAM taken: 59.44%, Block Peak % of device VRAM: 61.59%, ΔTime: 00:00:32 [2025-11-12 22:20:41,658][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 22:20:41,660][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 22:20:41,661][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 22:20:42,597][__main__][INFO] - Iteration 10 took 49s (27.48% Gen, 70.62% Train). Generation: 13s, Training: 34s. Estimated remaining time: 40h 49m 28s. Estimated total time: 41h 0m 29s. Time estimates for 10 more iterations: 8m 12s, 100 more iterations: 1h 22m 0s, 500 more iterations: 6h 50m 4s. [2025-11-12 22:20:42,599][__main__][INFO] - Starting iteration 10. [2025-11-12 22:20:43,047][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 0 and human policies 1. [2025-11-12 22:20:43,047][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 22:20:55,764][__main__][INFO] - Number of regex retries in iteration 10: 0 [2025-11-12 22:20:55,765][__main__][INFO] - agents played in iteration 10 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 22:20:56,638][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.47%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:20:56,663][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.47%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:20:56,688][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.47%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:20:56,711][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.47%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:20:56,711][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 22:20:56,713][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 22:20:57,361][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 22:20:57,830][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 22:20:58,333][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 22:20:58,843][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 22:20:59,340][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 22:20:59,842][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 22:21:00,339][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 22:21:00,833][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 22:21:01,327][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 22:21:01,833][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 22:21:02,328][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-12 22:21:02,823][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-12 22:21:03,316][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-12 22:21:03,810][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-12 22:21:04,316][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-12 22:21:04,811][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-12 22:21:05,307][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-12 22:21:05,822][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-12 22:21:06,314][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-12 22:21:06,807][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-12 22:21:07,299][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-12 22:21:07,792][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-12 22:21:08,288][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-12 22:21:08,780][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-12 22:21:09,272][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-12 22:21:09,768][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-12 22:21:10,262][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-12 22:21:10,755][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-12 22:21:11,249][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-12 22:21:11,743][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-12 22:21:12,245][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-12 22:21:12,738][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-12 22:21:13,232][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-12 22:21:13,738][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 22:21:14,232][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 22:21:14,724][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 22:21:15,219][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 22:21:15,712][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 22:21:16,214][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 22:21:16,707][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 22:21:17,199][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 22:21:17,705][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 22:21:18,198][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 22:21:18,691][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 22:21:19,182][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 22:21:19,674][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 22:21:20,166][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 22:21:20,659][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 22:21:21,153][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 22:21:21,652][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 22:21:22,146][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 22:21:22,643][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 22:21:23,133][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 22:21:23,624][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 22:21:24,132][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 22:21:24,623][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 22:21:25,114][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 22:21:25,625][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 22:21:26,119][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 22:21:26,617][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 22:21:27,116][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 22:21:27,613][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 22:21:28,125][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 22:21:28,623][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 22:21:29,121][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 9684 tokens. [2025-11-12 22:21:29,810][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.04%, Current % of VRAM taken: 59.49%, Block Peak % of device VRAM: 61.92%, ΔTime: 00:00:32 [2025-11-12 22:21:30,594][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 22:21:30,596][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 22:21:30,598][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 22:21:32,452][__main__][INFO] - Iteration 11 took 49s (25.74% Gen, 70.50% Train). Generation: 12s, Training: 34s. Estimated remaining time: 40h 58m 26s. Estimated total time: 41h 10m 17s. Time estimates for 10 more iterations: 8m 14s, 100 more iterations: 1h 22m 20s, 500 more iterations: 6h 51m 42s. [2025-11-12 22:21:32,454][__main__][INFO] - Starting iteration 11. [2025-11-12 22:21:32,901][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 1 and human policies 1. [2025-11-12 22:21:32,902][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 22:21:34,451][mllm.models.large_language_model_local][WARNING] - Response Proposition: 0 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 22:21:46,664][__main__][INFO] - Number of regex retries in iteration 11: 1 [2025-11-12 22:21:46,665][__main__][INFO] - agents played in iteration 11 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 22:21:47,478][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.51%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:21:47,507][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.51%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:21:47,534][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.51%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:21:47,571][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.51%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:21:47,572][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 22:21:47,573][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 22:21:48,242][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 22:21:48,694][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 22:21:49,203][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 22:21:49,699][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 22:21:50,194][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 22:21:50,685][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 22:21:51,184][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 22:21:51,681][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 22:21:52,177][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 22:21:52,670][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 22:21:53,162][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-12 22:21:53,654][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-12 22:21:54,149][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-12 22:21:54,639][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-12 22:21:55,131][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-12 22:21:55,633][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-12 22:21:56,126][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-12 22:21:56,626][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-12 22:21:57,122][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-12 22:21:57,616][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-12 22:21:58,113][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-12 22:21:58,610][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-12 22:21:59,106][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-12 22:21:59,613][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-12 22:22:00,109][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-12 22:22:00,629][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-12 22:22:01,138][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-12 22:22:01,630][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-12 22:22:02,125][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-12 22:22:02,619][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-12 22:22:03,113][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-12 22:22:03,621][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-12 22:22:04,114][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-12 22:22:04,608][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 22:22:05,110][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 22:22:05,604][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 22:22:06,098][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 22:22:06,593][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 22:22:07,087][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 22:22:07,584][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 22:22:08,076][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 22:22:08,570][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 22:22:09,060][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 22:22:09,552][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 22:22:10,050][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 22:22:10,542][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 22:22:11,034][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 22:22:11,539][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 22:22:12,031][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 22:22:12,524][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 22:22:13,020][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 22:22:13,512][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 22:22:14,029][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 22:22:14,522][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 22:22:15,016][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 22:22:15,514][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 22:22:16,007][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 22:22:16,498][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 22:22:16,989][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 22:22:17,482][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 22:22:17,978][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 22:22:18,471][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 22:22:18,965][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 22:22:19,459][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 22:22:19,953][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 9722 tokens. [2025-11-12 22:22:20,657][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 13.95%, Current % of VRAM taken: 59.40%, Block Peak % of device VRAM: 61.70%, ΔTime: 00:00:32 [2025-11-12 22:22:21,393][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 22:22:21,394][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 22:22:21,396][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 22:22:22,464][__main__][INFO] - Iteration 12 took 49s (27.77% Gen, 70.07% Train). Generation: 13s, Training: 34s. Estimated remaining time: 41h 5m 28s. Estimated total time: 41h 18m 9s. Time estimates for 10 more iterations: 8m 15s, 100 more iterations: 1h 22m 36s, 500 more iterations: 6h 53m 1s. [2025-11-12 22:22:22,467][__main__][INFO] - Starting iteration 12. [2025-11-12 22:22:22,926][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 1 and human policies 1. [2025-11-12 22:22:22,927][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 22:22:24,254][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 22:22:28,395][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 0 balls Given the values, you have a significant advantage over Alice for hats and a substantial advantage for books. Balls are not as valuable for you, and since Alice values them the most out of the two agents, it’s best to avoid proposing to keep any balls. This proposal ensures that you maximize your share of highly valuable items. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 22:22:37,730][__main__][INFO] - Number of regex retries in iteration 12: 2 [2025-11-12 22:22:37,731][__main__][INFO] - agents played in iteration 12 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 22:22:38,556][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.48%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:22:38,581][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.48%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:22:38,606][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.48%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:22:38,629][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.48%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:22:38,629][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 22:22:38,631][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 22:22:39,307][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 22:22:39,761][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 22:22:40,258][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 22:22:40,755][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 22:22:41,250][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 22:22:41,753][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 22:22:42,243][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 22:22:42,736][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 22:22:43,231][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 22:22:43,726][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 22:22:44,229][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-12 22:22:44,721][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-12 22:22:45,212][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-12 22:22:45,706][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-12 22:22:46,202][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-12 22:22:46,694][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-12 22:22:47,185][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-12 22:22:47,677][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-12 22:22:48,171][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-12 22:22:48,662][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-12 22:22:49,152][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-12 22:22:49,657][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-12 22:22:50,148][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-12 22:22:50,640][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-12 22:22:51,131][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-12 22:22:51,623][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-12 22:22:52,130][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-12 22:22:52,622][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-12 22:22:53,114][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-12 22:22:53,615][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-12 22:22:54,106][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-12 22:22:54,596][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-12 22:22:55,089][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-12 22:22:55,580][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 22:22:56,072][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 22:22:56,565][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 22:22:57,059][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 22:22:57,548][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 22:22:58,039][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 22:22:58,542][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 22:22:59,035][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 22:22:59,526][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 22:23:00,018][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 22:23:00,510][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 22:23:01,001][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 22:23:01,495][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 22:23:01,985][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 22:23:02,476][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 22:23:02,966][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 22:23:03,455][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 22:23:03,954][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 22:23:04,444][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 22:23:04,935][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 22:23:05,426][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 22:23:05,917][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 22:23:06,409][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 22:23:06,898][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 22:23:07,391][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 22:23:07,886][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 22:23:08,381][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 22:23:08,876][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 22:23:09,375][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 22:23:09,870][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 22:23:10,366][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 22:23:10,861][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 9632 tokens. [2025-11-12 22:23:11,532][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 13.92%, Current % of VRAM taken: 59.37%, Block Peak % of device VRAM: 61.49%, ΔTime: 00:00:32 [2025-11-12 22:23:12,276][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 22:23:12,278][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 22:23:12,279][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 22:23:13,274][__main__][INFO] - Iteration 13 took 50s (29.40% Gen, 68.62% Train). Generation: 14s, Training: 34s. Estimated remaining time: 41h 43m 52s. Estimated total time: 41h 57m 24s. Time estimates for 10 more iterations: 8m 23s, 100 more iterations: 1h 23m 54s, 500 more iterations: 6h 59m 34s. [2025-11-12 22:23:13,276][__main__][INFO] - Starting iteration 13. [2025-11-12 22:23:13,749][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 1 and human policies 1. [2025-11-12 22:23:13,750][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 22:23:27,400][__main__][INFO] - Number of regex retries in iteration 13: 0 [2025-11-12 22:23:27,401][__main__][INFO] - agents played in iteration 13 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 22:23:28,266][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.42%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:23:28,297][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.42%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:23:28,324][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.42%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:23:28,348][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.42%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:23:28,349][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 22:23:28,350][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 22:23:29,020][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 22:23:29,468][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 22:23:29,969][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 22:23:30,469][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 22:23:30,965][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 22:23:31,467][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 22:23:31,960][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 22:23:32,453][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 22:23:32,958][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 22:23:33,452][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 22:23:33,945][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-12 22:23:45,360][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 22:23:45,859][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 22:23:46,350][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 22:23:46,843][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 22:23:47,355][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 22:23:47,846][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 22:23:48,337][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 22:23:48,833][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 22:23:49,323][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 22:23:49,828][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 22:23:50,318][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 22:23:50,809][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 22:23:51,303][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 22:23:51,793][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 22:23:52,284][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 22:23:52,773][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 22:23:53,265][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 22:23:53,764][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 22:23:54,256][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 22:23:54,749][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 22:23:55,244][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 22:23:55,737][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 22:23:56,240][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 22:23:56,733][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 22:23:57,224][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 22:23:57,725][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 22:23:58,215][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 22:23:58,705][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 22:23:59,196][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 22:23:59,686][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 22:24:00,180][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 22:24:00,673][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 9635 tokens. [2025-11-12 22:24:01,337][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 10.92%, Current % of VRAM taken: 56.37%, Block Peak % of device VRAM: 61.55%, ΔTime: 00:00:32 [2025-11-12 22:24:02,085][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 22:24:02,086][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 22:24:02,088][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 22:24:03,107][__main__][INFO] - Iteration 14 took 49s (27.66% Gen, 70.28% Train). Generation: 13s, Training: 34s. Estimated remaining time: 40h 53m 34s. Estimated total time: 41h 7m 56s. Time estimates for 10 more iterations: 8m 13s, 100 more iterations: 1h 22m 15s, 500 more iterations: 6h 51m 19s. [2025-11-12 22:24:03,109][__main__][INFO] - Starting iteration 14. [2025-11-12 22:24:03,574][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 1 and human policies 1. [2025-11-12 22:24:03,574][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 22:24:10,014][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 22:24:17,882][__main__][INFO] - Number of regex retries in iteration 14: 1 [2025-11-12 22:24:17,883][__main__][INFO] - agents played in iteration 14 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 22:24:18,749][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 49.45%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:24:18,774][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 49.45%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:24:18,799][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 49.45%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:24:18,822][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 49.45%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:24:18,822][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 22:24:18,823][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 22:24:19,501][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 22:24:19,958][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 22:24:20,456][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 22:24:20,953][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 22:24:21,451][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 22:24:21,949][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 22:24:22,446][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 22:24:22,939][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 22:24:23,434][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 22:24:23,940][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 22:24:24,434][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-12 22:24:35,841][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 22:24:36,335][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 22:24:36,830][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 22:24:37,335][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 22:24:37,830][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 22:24:38,324][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 22:24:38,817][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 22:24:39,313][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 22:24:39,807][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 22:24:40,299][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 22:24:40,793][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 22:24:41,286][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 22:24:41,776][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 22:24:42,276][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 22:24:42,768][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 22:24:43,259][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 22:24:43,764][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 22:24:44,257][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 22:24:44,754][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 22:24:45,250][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 22:24:45,745][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 22:24:46,236][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 22:24:46,728][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 22:24:47,220][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 22:24:47,715][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 22:24:48,207][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 22:24:48,700][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 22:24:49,194][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 22:24:49,687][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 22:24:50,180][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 22:24:50,677][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 22:24:51,173][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 9704 tokens. [2025-11-12 22:24:51,860][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 13.93%, Current % of VRAM taken: 59.38%, Block Peak % of device VRAM: 61.62%, ΔTime: 00:00:32 [2025-11-12 22:24:52,611][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 22:24:52,612][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 22:24:52,618][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 22:24:53,567][__main__][INFO] - Iteration 15 took 49s (28.62% Gen, 69.48% Train). Generation: 14s, Training: 34s. Estimated remaining time: 41h 24m 30s. Estimated total time: 41h 39m 42s. Time estimates for 10 more iterations: 8m 19s, 100 more iterations: 1h 23m 19s, 500 more iterations: 6h 56m 37s. [2025-11-12 22:24:53,569][__main__][INFO] - Starting iteration 15. [2025-11-12 22:24:54,028][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 1 and human policies 1. [2025-11-12 22:24:54,029][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 22:24:55,276][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 22:24:58,316][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 0 balls This proposal maximizes my share of high-value items (books and balls) while taking into account their lower per-item values for me. Since Bob values hats more highly and I value hats less, underproposing hats benefits me by leaving more to potentially be allocated proportionally later. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 22:25:04,775][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 22:25:08,626][__main__][INFO] - Number of regex retries in iteration 15: 3 [2025-11-12 22:25:08,627][__main__][INFO] - agents played in iteration 15 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 22:25:09,448][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.42%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:25:09,476][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.42%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:25:09,502][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.42%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:25:09,526][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.42%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:25:09,527][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 22:25:09,528][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 22:25:10,193][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 22:25:10,644][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 22:25:11,154][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 22:25:11,649][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 22:25:12,149][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 22:25:12,655][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 22:25:13,148][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 22:25:13,641][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 22:25:14,146][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 22:25:14,639][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 22:25:15,153][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-12 22:25:15,646][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-12 22:25:16,139][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-12 22:25:16,643][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-12 22:25:17,135][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-12 22:25:17,628][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-12 22:25:18,121][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-12 22:25:18,615][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-12 22:25:19,112][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-12 22:25:19,606][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-12 22:25:20,102][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-12 22:25:20,595][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-12 22:25:21,090][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-12 22:25:21,587][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-12 22:25:22,083][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-12 22:25:22,573][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-12 22:25:23,067][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-12 22:25:23,558][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-12 22:25:24,049][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-12 22:25:24,542][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-12 22:25:25,035][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-12 22:25:25,531][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-12 22:25:26,023][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-12 22:25:26,517][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 22:25:27,013][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 22:25:27,507][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 22:25:28,004][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 22:25:28,497][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 22:25:28,990][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 22:25:29,486][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 22:25:29,979][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 22:25:30,470][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 22:25:30,977][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 22:25:31,470][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 22:25:31,962][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 22:25:32,459][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 22:25:32,952][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 22:25:33,461][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 22:25:33,957][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 22:25:34,455][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 22:25:34,962][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 22:25:35,456][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 22:25:35,970][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 22:25:36,464][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 22:25:36,957][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 22:25:37,468][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 22:25:37,962][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 22:25:38,455][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 22:25:38,956][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 22:25:39,446][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 22:25:39,938][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 22:25:40,432][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 22:25:40,924][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 22:25:41,421][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 22:25:41,917][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 9712 tokens. [2025-11-12 22:25:42,581][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 13.96%, Current % of VRAM taken: 59.41%, Block Peak % of device VRAM: 61.95%, ΔTime: 00:00:32 [2025-11-12 22:25:43,325][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 22:25:43,326][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 22:25:43,328][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 22:25:44,302][__main__][INFO] - Iteration 16 took 50s (29.04% Gen, 69.02% Train). Generation: 14s, Training: 34s. Estimated remaining time: 41h 37m 42s. Estimated total time: 41h 53m 45s. Time estimates for 10 more iterations: 8m 22s, 100 more iterations: 1h 23m 47s, 500 more iterations: 6h 58m 57s. [2025-11-12 22:25:44,304][__main__][INFO] - Starting iteration 16. [2025-11-12 22:25:44,786][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 1 and human policies 1. [2025-11-12 22:25:44,786][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 22:25:58,780][__main__][INFO] - Number of regex retries in iteration 16: 0 [2025-11-12 22:25:58,781][__main__][INFO] - agents played in iteration 16 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 22:25:59,665][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.46%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:25:59,696][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.46%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:25:59,724][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.46%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:25:59,747][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.46%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:25:59,748][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 22:25:59,748][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 22:26:00,427][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 22:26:00,889][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 22:26:01,390][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 22:26:01,894][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 22:26:02,389][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 22:26:02,886][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 22:26:03,388][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 22:26:03,887][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 22:26:04,380][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 22:26:04,874][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 22:26:05,367][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-12 22:26:05,857][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-12 22:26:06,348][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-12 22:26:06,840][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-12 22:26:07,333][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-12 22:26:07,824][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-12 22:26:08,314][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-12 22:26:08,807][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-12 22:26:09,298][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-12 22:26:09,793][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-12 22:26:10,284][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-12 22:26:10,776][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-12 22:26:16,711][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 22:26:17,206][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 22:26:17,698][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 22:26:18,189][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 22:26:18,682][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 22:26:19,194][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 22:26:19,685][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 22:26:20,175][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 22:26:20,671][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 22:26:21,161][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 22:26:21,653][mllm.training.trainer_common][INFO] - 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Processing mini-batch 54 of 64 [2025-11-12 22:26:27,574][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 22:26:28,067][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 22:26:28,566][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 22:26:29,058][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 22:26:29,551][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 22:26:30,045][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 22:26:30,538][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 22:26:31,030][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 22:26:31,524][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 22:26:32,018][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 9659 tokens. [2025-11-12 22:26:32,644][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.02%, Current % of VRAM taken: 59.47%, Block Peak % of device VRAM: 61.68%, ΔTime: 00:00:32 [2025-11-12 22:26:33,388][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 22:26:33,390][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 22:26:33,391][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 22:26:34,480][__main__][INFO] - Iteration 17 took 49s (28.16% Gen, 69.65% Train). Generation: 13s, Training: 34s. Estimated remaining time: 41h 7m 51s. Estimated total time: 41h 24m 44s. Time estimates for 10 more iterations: 8m 16s, 100 more iterations: 1h 22m 49s, 500 more iterations: 6h 54m 7s. [2025-11-12 22:26:34,482][__main__][INFO] - Starting iteration 17. [2025-11-12 22:26:34,989][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 1 and human policies 1. [2025-11-12 22:26:34,989][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 22:26:42,802][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 0 balls Given the per-item values, I have a significant advantage over Alice for hats and a relatively minor advantage for books and balls. Since Alice values books and balls more highly than I do, and I have a much higher valuation for hats, it makes strategic sense to claim all the hats. This way, I ensure a high return on a highly valued item, even if it means apportioning all the hats to myself, and making no claim on books or balls where my valuation is lower. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 22:26:50,441][__main__][INFO] - Number of regex retries in iteration 17: 1 [2025-11-12 22:26:50,441][__main__][INFO] - agents played in iteration 17 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 22:26:51,262][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.48%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:26:51,290][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.48%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:26:51,317][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.48%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:26:51,339][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.48%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:26:51,340][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 22:26:51,341][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 22:26:52,001][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 22:26:52,453][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 22:26:52,957][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 22:26:53,455][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 22:26:53,955][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 22:26:54,455][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 22:26:54,953][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 22:26:55,449][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 22:26:55,947][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 22:26:56,443][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 22:26:56,937][mllm.training.trainer_common][INFO] - 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Processing mini-batch 43 of 64 [2025-11-12 22:27:13,773][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 22:27:14,265][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 22:27:14,770][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 22:27:15,262][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 22:27:15,755][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 22:27:16,247][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 22:27:16,739][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 22:27:17,233][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 22:27:17,726][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 22:27:18,219][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 22:27:18,712][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 22:27:19,202][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 22:27:19,702][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 22:27:20,196][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 22:27:20,686][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 22:27:21,177][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 22:27:21,669][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 22:27:22,162][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 22:27:22,663][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 22:27:23,156][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 22:27:23,652][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 9657 tokens. [2025-11-12 22:27:24,312][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 12.93%, Current % of VRAM taken: 58.38%, Block Peak % of device VRAM: 61.91%, ΔTime: 00:00:32 [2025-11-12 22:27:25,084][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 22:27:25,086][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 22:27:25,088][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 22:27:26,009][__main__][INFO] - Iteration 18 took 51s (30.29% Gen, 67.90% Train). Generation: 15s, Training: 34s. Estimated remaining time: 42h 13m 18s. Estimated total time: 42h 31m 3s. Time estimates for 10 more iterations: 8m 30s, 100 more iterations: 1h 25m 2s, 500 more iterations: 7h 5m 10s. [2025-11-12 22:27:26,012][__main__][INFO] - Starting iteration 18. [2025-11-12 22:27:26,514][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 1 and human policies 1. [2025-11-12 22:27:26,514][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 22:27:39,339][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 0 books, 20 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 22:27:41,347][__main__][INFO] - Number of regex retries in iteration 18: 1 [2025-11-12 22:27:41,348][__main__][INFO] - agents played in iteration 18 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 22:27:42,202][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 51.45%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:27:42,229][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 51.45%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:27:42,255][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 51.45%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:27:42,278][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 51.45%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:27:42,278][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 22:27:42,279][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 22:27:42,922][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 22:27:43,389][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 22:27:43,892][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 22:27:44,391][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 22:27:44,903][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 22:27:45,397][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 22:27:45,892][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 22:27:46,391][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 22:27:46,884][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 22:27:47,380][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 22:27:47,874][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-12 22:27:59,245][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 22:27:59,736][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 22:28:00,227][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 22:28:00,723][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 22:28:01,216][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 22:28:01,710][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 22:28:02,200][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 22:28:02,693][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 22:28:03,201][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 22:28:03,694][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 22:28:04,186][mllm.training.trainer_common][INFO] - 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Processing mini-batch 54 of 64 [2025-11-12 22:28:10,103][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 22:28:10,595][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 22:28:11,101][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 22:28:11,593][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 22:28:12,083][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 22:28:12,582][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 22:28:13,073][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 22:28:13,566][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 22:28:14,060][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 22:28:14,554][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 9735 tokens. [2025-11-12 22:28:15,228][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 13.97%, Current % of VRAM taken: 59.42%, Block Peak % of device VRAM: 61.81%, ΔTime: 00:00:32 [2025-11-12 22:28:15,976][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 22:28:15,977][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 22:28:15,980][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 22:28:16,880][__main__][INFO] - Iteration 19 took 50s (29.45% Gen, 68.76% Train). Generation: 14s, Training: 34s. Estimated remaining time: 41h 39m 43s. Estimated total time: 41h 58m 18s. Time estimates for 10 more iterations: 8m 23s, 100 more iterations: 1h 23m 56s, 500 more iterations: 6h 59m 43s. [2025-11-12 22:28:16,882][__main__][INFO] - Starting iteration 19. [2025-11-12 22:28:17,382][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 1 and human policies 1. [2025-11-12 22:28:17,382][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 22:28:26,478][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 0 balls Given the per-item values, I see that I value hats the least at 1, while Alice values them the most at 10. Books and balls have a higher value for both of us, but I value them at 10, which is the same as Alice's valuation for balls, and Alice values books at 1, which is the same as my valuation for books. To maximize my points, I should take all the hats since I am guaranteed to get the full value of 1 per hat, while Alice would likely take books and balls to maximize her points. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 22:28:32,203][__main__][INFO] - Number of regex retries in iteration 19: 1 [2025-11-12 22:28:32,204][__main__][INFO] - agents played in iteration 19 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 22:28:33,162][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.48%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:28:33,190][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.48%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:28:33,216][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.48%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:28:33,240][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.48%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:28:33,240][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 22:28:33,242][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 22:28:33,840][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 22:28:34,289][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 22:28:34,798][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 22:28:35,301][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 22:28:35,817][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 22:28:36,309][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 22:28:36,802][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 22:28:37,300][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 22:28:37,793][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 22:28:38,290][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 22:28:38,785][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-12 22:28:50,180][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 22:28:50,671][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 22:28:51,164][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 22:28:51,657][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 22:28:52,149][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 22:28:52,641][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 22:28:53,133][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 22:28:53,624][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 22:28:54,123][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 22:28:54,616][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 22:28:55,109][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 22:28:55,603][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 22:28:56,095][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 22:28:56,592][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 22:28:57,093][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 22:28:57,585][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 22:28:58,077][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 22:28:58,570][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 22:28:59,063][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 22:28:59,557][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 22:29:00,048][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 22:29:00,541][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 22:29:01,032][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 22:29:01,523][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 22:29:02,021][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 22:29:02,516][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 22:29:03,009][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 22:29:03,521][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 22:29:04,014][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 22:29:04,509][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 22:29:05,004][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 22:29:05,500][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 9737 tokens. [2025-11-12 22:29:06,182][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 13.96%, Current % of VRAM taken: 59.41%, Block Peak % of device VRAM: 61.68%, ΔTime: 00:00:32 [2025-11-12 22:29:06,932][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 22:29:06,934][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 22:29:06,935][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 22:29:07,874][__main__][INFO] - Iteration 20 took 50s (29.35% Gen, 68.78% Train). Generation: 14s, Training: 34s. Estimated remaining time: 41h 45m 13s. Estimated total time: 42h 4m 39s. Time estimates for 10 more iterations: 8m 24s, 100 more iterations: 1h 24m 9s, 500 more iterations: 7h 0m 46s. [2025-11-12 22:29:07,877][__main__][INFO] - Starting iteration 20. [2025-11-12 22:29:08,375][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 1 and human policies 1. [2025-11-12 22:29:08,376][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 22:29:18,215][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 22:29:22,519][__main__][INFO] - Number of regex retries in iteration 20: 1 [2025-11-12 22:29:22,520][__main__][INFO] - agents played in iteration 20 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 22:29:23,382][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.45%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:29:23,411][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.45%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:29:23,438][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.45%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:29:23,462][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.45%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:29:23,463][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 22:29:23,464][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 22:29:24,103][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 22:29:24,559][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 22:29:25,060][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 22:29:25,555][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 22:29:26,050][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 22:29:26,547][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 22:29:27,047][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 22:29:27,542][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 22:29:28,036][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 22:29:28,532][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 22:29:29,024][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-12 22:29:40,409][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 22:29:40,903][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 22:29:41,395][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 22:29:41,887][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 22:29:42,380][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 22:29:42,884][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 22:29:43,376][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 22:29:43,868][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 22:29:44,360][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 22:29:44,853][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 22:29:45,346][mllm.training.trainer_common][INFO] - 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Processing mini-batch 54 of 64 [2025-11-12 22:29:51,275][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 22:29:51,768][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 22:29:52,257][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 22:29:52,749][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 22:29:53,244][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 22:29:53,735][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 22:29:54,227][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 22:29:54,722][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 22:29:55,217][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 22:29:55,720][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 9700 tokens. [2025-11-12 22:29:56,388][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 13.97%, Current % of VRAM taken: 59.42%, Block Peak % of device VRAM: 61.95%, ΔTime: 00:00:32 [2025-11-12 22:29:57,170][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 22:29:57,171][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 22:29:57,174][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 22:29:59,251][__main__][INFO] - Iteration 21 took 50s (27.80% Gen, 68.11% Train). Generation: 14s, Training: 34s. Estimated remaining time: 42h 3m 31s. Estimated total time: 42h 23m 48s. Time estimates for 10 more iterations: 8m 28s, 100 more iterations: 1h 24m 47s, 500 more iterations: 7h 3m 58s. [2025-11-12 22:29:59,253][__main__][INFO] - Starting iteration 21. [2025-11-12 22:29:59,766][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 2 and human policies 1. [2025-11-12 22:29:59,767][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 22:30:14,267][__main__][INFO] - Number of regex retries in iteration 21: 0 [2025-11-12 22:30:14,267][__main__][INFO] - agents played in iteration 21 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 22:30:15,102][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.49%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:30:15,127][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.49%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:30:15,166][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.49%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:30:15,189][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.49%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:30:15,190][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 22:30:15,191][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 22:30:15,831][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 22:30:16,281][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 22:30:16,794][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 22:30:17,295][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 22:30:17,801][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 22:30:18,297][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 22:30:18,794][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 22:30:19,290][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 22:30:19,785][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 22:30:20,277][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 22:30:20,774][mllm.training.trainer_common][INFO] - 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Processing mini-batch 21 of 64 [2025-11-12 22:30:26,716][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-12 22:30:27,210][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-12 22:30:27,703][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-12 22:30:28,196][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-12 22:30:28,685][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-12 22:30:29,177][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-12 22:30:29,668][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-12 22:30:30,160][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-12 22:30:30,651][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-12 22:30:31,144][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-12 22:30:31,636][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-12 22:30:32,129][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 22:30:32,619][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 22:30:33,114][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 22:30:33,631][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 22:30:34,123][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 22:30:34,615][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 22:30:35,113][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 22:30:35,606][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 22:30:36,095][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 22:30:36,585][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 22:30:37,075][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 22:30:37,570][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 22:30:38,059][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 22:30:38,551][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 22:30:39,047][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 22:30:39,537][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 22:30:40,029][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 22:30:40,522][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 22:30:41,016][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 22:30:41,527][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 22:30:42,025][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 22:30:42,524][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 22:30:43,019][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 22:30:43,514][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 22:30:44,010][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 22:30:44,503][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 22:30:44,994][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 22:30:45,486][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 22:30:45,979][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 22:30:46,474][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 22:30:46,971][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 22:30:47,465][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 9708 tokens. [2025-11-12 22:30:48,141][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.00%, Current % of VRAM taken: 59.45%, Block Peak % of device VRAM: 61.92%, ΔTime: 00:00:32 [2025-11-12 22:30:48,897][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 22:30:48,898][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 22:30:48,900][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 22:30:49,909][__main__][INFO] - Iteration 22 took 50s (28.92% Gen, 69.07% Train). Generation: 14s, Training: 34s. Estimated remaining time: 41h 26m 2s. Estimated total time: 41h 47m 10s. Time estimates for 10 more iterations: 8m 21s, 100 more iterations: 1h 23m 34s, 500 more iterations: 6h 57m 51s. [2025-11-12 22:30:49,911][__main__][INFO] - Starting iteration 22. [2025-11-12 22:30:50,380][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 2 and human policies 1. [2025-11-12 22:30:50,380][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 22:31:04,330][__main__][INFO] - Number of regex retries in iteration 22: 0 [2025-11-12 22:31:04,330][__main__][INFO] - agents played in iteration 22 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 22:31:05,169][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.49%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:31:05,197][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.49%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:31:05,225][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.49%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:31:05,249][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.49%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:31:05,249][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 22:31:05,250][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 22:31:05,892][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 22:31:06,351][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 22:31:06,851][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 22:31:07,351][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 22:31:07,845][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 22:31:08,348][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 22:31:08,846][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 22:31:09,344][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 22:31:09,839][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 22:31:10,338][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 22:31:10,832][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-12 22:31:11,325][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-12 22:31:11,820][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-12 22:31:12,316][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-12 22:31:12,824][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-12 22:31:13,318][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-12 22:31:13,814][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-12 22:31:14,307][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-12 22:31:14,805][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-12 22:31:15,317][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-12 22:31:15,812][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-12 22:31:16,304][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-12 22:31:16,798][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-12 22:31:17,294][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-12 22:31:17,790][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-12 22:31:18,282][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-12 22:31:18,775][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-12 22:31:19,272][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-12 22:31:19,765][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-12 22:31:20,257][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-12 22:31:20,753][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-12 22:31:21,244][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-12 22:31:21,740][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-12 22:31:22,236][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 22:31:22,729][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 22:31:23,238][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 22:31:23,733][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 22:31:24,226][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 22:31:24,724][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 22:31:25,217][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 22:31:25,714][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 22:31:26,208][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 22:31:26,701][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 22:31:27,201][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 22:31:27,695][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 22:31:28,196][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 22:31:28,689][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 22:31:29,183][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 22:31:29,678][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 22:31:30,170][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 22:31:30,661][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 22:31:31,161][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 22:31:31,655][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 22:31:32,147][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 22:31:32,640][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 22:31:33,132][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 22:31:33,625][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 22:31:34,122][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 22:31:34,616][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 22:31:35,109][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 22:31:35,601][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 22:31:36,095][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 22:31:36,588][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 22:31:37,084][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 22:31:37,581][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 9771 tokens. [2025-11-12 22:31:38,246][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.06%, Current % of VRAM taken: 59.51%, Block Peak % of device VRAM: 61.69%, ΔTime: 00:00:32 [2025-11-12 22:31:39,031][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 22:31:39,035][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 22:31:39,037][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 22:31:40,279][__main__][INFO] - Iteration 23 took 49s (27.96% Gen, 69.55% Train). Generation: 13s, Training: 34s. Estimated remaining time: 41h 13m 1s. Estimated total time: 41h 35m 0s. Time estimates for 10 more iterations: 8m 19s, 100 more iterations: 1h 23m 10s, 500 more iterations: 6h 55m 50s. [2025-11-12 22:31:40,282][__main__][INFO] - Starting iteration 23. [2025-11-12 22:31:40,761][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 2 and human policies 1. [2025-11-12 22:31:40,761][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 22:31:41,850][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 22:31:54,706][__main__][INFO] - Number of regex retries in iteration 23: 1 [2025-11-12 22:31:54,707][__main__][INFO] - agents played in iteration 23 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 22:31:55,544][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.48%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:31:55,572][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.48%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:31:55,599][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.48%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:31:55,624][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.48%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:31:55,625][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 22:31:55,625][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 22:31:56,269][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 22:31:56,724][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 22:31:57,224][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 22:31:57,731][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 22:31:58,225][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 22:31:58,722][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 22:31:59,224][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 22:31:59,721][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 22:32:00,214][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 22:32:00,708][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 22:32:01,206][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-12 22:32:01,703][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-12 22:32:02,197][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-12 22:32:02,694][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-12 22:32:03,189][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-12 22:32:03,681][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-12 22:32:04,176][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-12 22:32:04,672][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-12 22:32:05,165][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-12 22:32:05,658][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-12 22:32:06,150][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-12 22:32:06,644][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-12 22:32:07,155][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-12 22:32:07,647][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-12 22:32:08,141][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-12 22:32:08,637][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-12 22:32:09,131][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-12 22:32:09,660][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-12 22:32:10,154][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-12 22:32:10,648][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-12 22:32:11,144][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-12 22:32:11,639][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-12 22:32:12,135][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-12 22:32:12,628][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 22:32:13,121][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 22:32:13,633][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 22:32:14,126][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 22:32:14,620][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 22:32:15,123][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 22:32:15,616][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 22:32:16,115][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 22:32:16,619][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 22:32:17,112][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 22:32:17,614][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 22:32:18,111][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 22:32:18,604][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 22:32:19,106][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 22:32:19,598][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 22:32:20,092][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 22:32:20,586][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 22:32:21,079][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 22:32:21,572][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 22:32:22,063][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 22:32:22,558][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 22:32:23,047][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 22:32:23,538][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 22:32:24,039][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 22:32:24,531][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 22:32:25,023][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 22:32:25,516][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 22:32:26,010][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 22:32:26,503][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 22:32:27,000][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 22:32:27,496][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 22:32:27,994][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 9724 tokens. [2025-11-12 22:32:28,640][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.01%, Current % of VRAM taken: 59.46%, Block Peak % of device VRAM: 61.62%, ΔTime: 00:00:32 [2025-11-12 22:32:29,426][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 22:32:29,428][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 22:32:29,432][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 22:32:30,380][__main__][INFO] - Iteration 24 took 49s (28.11% Gen, 69.98% Train). Generation: 13s, Training: 34s. Estimated remaining time: 40h 58m 9s. Estimated total time: 41h 20m 58s. Time estimates for 10 more iterations: 8m 16s, 100 more iterations: 1h 22m 41s, 500 more iterations: 6h 53m 29s. [2025-11-12 22:32:30,382][__main__][INFO] - Starting iteration 24. [2025-11-12 22:32:30,847][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 2 and human policies 1. [2025-11-12 22:32:30,848][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 22:32:31,950][mllm.models.large_language_model_local][WARNING] - Response Proposal: 5 hats, 5 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 22:32:43,407][__main__][INFO] - Number of regex retries in iteration 24: 1 [2025-11-12 22:32:43,408][__main__][INFO] - agents played in iteration 24 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 22:32:44,332][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.48%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:32:44,366][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.48%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:32:44,395][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.48%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:32:44,419][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.48%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:32:44,420][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 22:32:44,421][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 22:32:45,044][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 22:32:45,496][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 22:32:45,994][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 22:32:46,494][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 22:32:46,995][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 22:32:47,490][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 22:32:47,983][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 22:32:48,477][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 22:32:48,980][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 22:32:49,481][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 22:32:49,981][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-12 22:32:50,476][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-12 22:32:50,968][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-12 22:32:51,462][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-12 22:32:51,955][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-12 22:32:52,448][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-12 22:32:52,956][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-12 22:32:53,456][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-12 22:32:53,951][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-12 22:32:54,446][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-12 22:32:54,945][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-12 22:32:55,442][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-12 22:32:55,941][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-12 22:32:56,440][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-12 22:32:56,935][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-12 22:32:57,427][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-12 22:32:57,919][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-12 22:32:58,412][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-12 22:32:58,904][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-12 22:32:59,398][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-12 22:32:59,889][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-12 22:33:00,380][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-12 22:33:00,878][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-12 22:33:01,371][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 22:33:01,865][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 22:33:02,357][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 22:33:02,850][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 22:33:03,365][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 22:33:03,856][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 22:33:04,350][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 22:33:04,850][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 22:33:05,341][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 22:33:05,833][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 22:33:06,325][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 22:33:06,817][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 22:33:07,316][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 22:33:07,808][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 22:33:08,302][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 22:33:08,796][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 22:33:09,289][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 22:33:09,785][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 22:33:10,278][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 22:33:10,770][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 22:33:11,280][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 22:33:11,772][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 22:33:12,264][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 22:33:12,757][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 22:33:13,250][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 22:33:13,757][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 22:33:14,250][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 22:33:14,743][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 22:33:15,248][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 22:33:15,742][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 22:33:16,237][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 22:33:16,732][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 9656 tokens. [2025-11-12 22:33:17,409][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 13.95%, Current % of VRAM taken: 59.40%, Block Peak % of device VRAM: 61.96%, ΔTime: 00:00:32 [2025-11-12 22:33:18,183][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 22:33:18,184][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 22:33:18,186][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 22:33:19,122][__main__][INFO] - Iteration 25 took 48s (26.02% Gen, 72.04% Train). Generation: 12s, Training: 34s. Estimated remaining time: 39h 50m 8s. Estimated total time: 40h 13m 45s. Time estimates for 10 more iterations: 8m 2s, 100 more iterations: 1h 20m 27s, 500 more iterations: 6h 42m 17s. [2025-11-12 22:33:19,124][__main__][INFO] - Starting iteration 25. [2025-11-12 22:33:19,591][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 2 and human policies 1. [2025-11-12 22:33:19,591][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 22:33:33,557][__main__][INFO] - Number of regex retries in iteration 25: 0 [2025-11-12 22:33:33,558][__main__][INFO] - agents played in iteration 25 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 22:33:34,403][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.43%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:33:34,427][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.43%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:33:34,449][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.43%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:33:34,470][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.43%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:33:34,471][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 22:33:34,472][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 22:33:35,096][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 22:33:35,765][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 22:33:36,260][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 22:33:36,760][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 22:33:37,257][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 22:33:37,755][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 22:33:38,251][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 22:33:38,759][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 22:33:39,257][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 22:33:39,758][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 22:33:40,260][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-12 22:33:51,656][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 22:33:52,149][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 22:33:52,642][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 22:33:53,138][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 22:33:53,630][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 22:33:54,124][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 22:33:54,617][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 22:33:55,110][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 22:33:55,602][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 22:33:56,095][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 22:33:56,586][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 22:33:57,084][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 22:33:57,577][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 22:33:58,069][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 22:33:58,581][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 22:33:59,074][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 22:33:59,565][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 22:34:00,060][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 22:34:00,551][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 22:34:01,042][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 22:34:01,534][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 22:34:02,025][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 22:34:02,517][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 22:34:03,007][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 22:34:03,499][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 22:34:03,992][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 22:34:04,491][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 22:34:04,994][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 22:34:05,491][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 22:34:05,988][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 22:34:06,492][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 22:34:06,990][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 9707 tokens. [2025-11-12 22:34:07,656][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.00%, Current % of VRAM taken: 59.45%, Block Peak % of device VRAM: 61.64%, ΔTime: 00:00:32 [2025-11-12 22:34:08,390][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 22:34:08,392][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 22:34:08,393][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 22:34:09,359][__main__][INFO] - Iteration 26 took 49s (28.06% Gen, 70.00% Train). Generation: 13s, Training: 34s. Estimated remaining time: 41h 3m 58s. Estimated total time: 41h 28m 25s. Time estimates for 10 more iterations: 8m 17s, 100 more iterations: 1h 22m 56s, 500 more iterations: 6h 54m 44s. [2025-11-12 22:34:09,361][__main__][INFO] - Starting iteration 26. [2025-11-12 22:34:09,852][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 2 and human policies 1. [2025-11-12 22:34:09,853][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 22:34:23,645][__main__][INFO] - Number of regex retries in iteration 26: 0 [2025-11-12 22:34:23,646][__main__][INFO] - agents played in iteration 26 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 22:34:24,481][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.46%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:34:24,507][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.46%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:34:24,534][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.46%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:34:24,557][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.46%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:34:24,557][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 22:34:24,558][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 22:34:25,199][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 22:34:25,650][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 22:34:26,147][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 22:34:26,640][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 22:34:27,139][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 22:34:27,636][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 22:34:28,132][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 22:34:28,630][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 22:34:29,125][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 22:34:29,627][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 22:34:30,130][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-12 22:34:30,632][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-12 22:34:31,129][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-12 22:34:31,625][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-12 22:34:32,120][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-12 22:34:32,617][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-12 22:34:33,112][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-12 22:34:33,605][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-12 22:34:34,099][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-12 22:34:34,592][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-12 22:34:35,086][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-12 22:34:35,579][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-12 22:34:41,528][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 22:34:42,023][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 22:34:42,517][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 22:34:43,012][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 22:34:43,505][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 22:34:44,001][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 22:34:44,496][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 22:34:44,990][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 22:34:45,484][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 22:34:45,981][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 22:34:46,475][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 22:34:46,983][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 22:34:47,478][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 22:34:47,972][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 22:34:48,471][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 22:34:48,964][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 22:34:49,458][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 22:34:49,953][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 22:34:50,446][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 22:34:50,941][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 22:34:51,436][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 22:34:51,931][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 22:34:52,426][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 22:34:52,920][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 22:34:53,417][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 22:34:53,909][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 22:34:54,403][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 22:34:54,905][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 22:34:55,403][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 22:34:55,904][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 22:34:56,399][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 22:34:56,897][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 9742 tokens. [2025-11-12 22:34:57,546][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 13.96%, Current % of VRAM taken: 59.41%, Block Peak % of device VRAM: 61.71%, ΔTime: 00:00:32 [2025-11-12 22:34:58,287][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 22:34:58,289][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 22:34:58,292][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 22:34:59,312][__main__][INFO] - Iteration 27 took 49s (27.89% Gen, 70.05% Train). Generation: 13s, Training: 34s. Estimated remaining time: 40h 47m 45s. Estimated total time: 41h 13m 3s. Time estimates for 10 more iterations: 8m 14s, 100 more iterations: 1h 22m 26s, 500 more iterations: 6h 52m 10s. [2025-11-12 22:34:59,314][__main__][INFO] - Starting iteration 27. [2025-11-12 22:34:59,788][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 2 and human policies 1. [2025-11-12 22:34:59,789][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 22:35:13,416][__main__][INFO] - Number of regex retries in iteration 27: 0 [2025-11-12 22:35:13,417][__main__][INFO] - agents played in iteration 27 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 22:35:14,253][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.44%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:35:14,281][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.44%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:35:14,307][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.44%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:35:14,332][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.44%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:35:14,333][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 22:35:14,334][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 22:35:14,949][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 22:35:15,400][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 22:35:15,901][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 22:35:16,397][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 22:35:16,902][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 22:35:17,397][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 22:35:17,896][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 22:35:18,393][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 22:35:18,888][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 22:35:19,381][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 22:35:19,881][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-12 22:35:20,375][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-12 22:35:20,883][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-12 22:35:21,382][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-12 22:35:21,876][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-12 22:35:22,376][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-12 22:35:22,869][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-12 22:35:23,362][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-12 22:35:23,854][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-12 22:35:24,351][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-12 22:35:24,844][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-12 22:35:25,337][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-12 22:35:25,829][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-12 22:35:26,329][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-12 22:35:26,821][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-12 22:35:27,317][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-12 22:35:27,809][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-12 22:35:28,300][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-12 22:35:28,808][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-12 22:35:29,300][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-12 22:35:29,794][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-12 22:35:30,287][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-12 22:35:30,779][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-12 22:35:31,270][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 22:35:31,762][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 22:35:32,254][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 22:35:32,747][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 22:35:33,240][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 22:35:33,733][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 22:35:34,225][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 22:35:34,719][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 22:35:35,220][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 22:35:35,712][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 22:35:36,202][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 22:35:36,714][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 22:35:37,205][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 22:35:37,699][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 22:35:38,193][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 22:35:38,686][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 22:35:39,185][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 22:35:39,678][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 22:35:40,171][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 22:35:40,670][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 22:35:41,163][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 22:35:41,657][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 22:35:42,151][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 22:35:42,645][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 22:35:43,139][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 22:35:43,635][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 22:35:44,130][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 22:35:44,631][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 22:35:45,129][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 22:35:45,625][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 22:35:46,122][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 22:35:46,618][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 9794 tokens. [2025-11-12 22:35:47,328][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.03%, Current % of VRAM taken: 59.48%, Block Peak % of device VRAM: 61.66%, ΔTime: 00:00:32 [2025-11-12 22:35:48,089][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 22:35:48,090][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 22:35:48,092][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 22:35:49,079][__main__][INFO] - Iteration 28 took 49s (27.65% Gen, 70.35% Train). Generation: 13s, Training: 34s. Estimated remaining time: 40h 38m 25s. Estimated total time: 41h 4m 32s. Time estimates for 10 more iterations: 8m 12s, 100 more iterations: 1h 22m 9s, 500 more iterations: 6h 50m 45s. [2025-11-12 22:35:49,081][__main__][INFO] - Starting iteration 28. [2025-11-12 22:35:49,575][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 2 and human policies 1. [2025-11-12 22:35:49,575][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 22:35:57,939][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 0 balls Given the per-item values, you can see that you have a higher valuation for hats compared to Alice, and a relatively low valuation for both books and balls. Since Alice values books and balls much higher than you do, proposing to take all 10 hats would maximize your points for this round. You have no incentive to share hats in this round, as Alice's valuation is far lower. Thus, proposing to keep all 10 hats and none of the books or balls would be the optimal strategy. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 22:36:04,743][__main__][INFO] - Number of regex retries in iteration 28: 1 [2025-11-12 22:36:04,744][__main__][INFO] - agents played in iteration 28 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 22:36:05,590][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.47%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:36:05,617][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.47%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:36:05,644][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.47%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:36:05,667][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.47%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:36:05,667][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 22:36:05,669][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 22:36:06,278][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 22:36:06,732][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 22:36:07,230][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 22:36:07,726][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 22:36:08,225][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 22:36:08,721][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 22:36:09,219][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 22:36:09,715][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 22:36:10,209][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 22:36:10,720][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 22:36:11,216][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-12 22:36:11,714][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-12 22:36:12,213][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-12 22:36:12,711][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-12 22:36:13,207][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-12 22:36:13,699][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-12 22:36:14,192][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-12 22:36:14,700][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-12 22:36:15,196][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-12 22:36:15,691][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-12 22:36:16,189][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-12 22:36:16,682][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-12 22:36:17,178][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-12 22:36:17,671][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-12 22:36:18,162][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-12 22:36:18,657][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-12 22:36:19,149][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-12 22:36:19,643][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-12 22:36:20,134][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-12 22:36:20,626][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-12 22:36:21,122][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-12 22:36:21,614][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-12 22:36:22,105][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-12 22:36:22,599][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 22:36:23,090][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 22:36:23,583][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 22:36:24,075][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 22:36:24,566][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 22:36:25,059][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 22:36:25,551][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 22:36:26,041][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 22:36:26,542][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 22:36:27,036][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 22:36:27,530][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 22:36:28,023][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 22:36:28,517][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 22:36:29,020][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 22:36:29,512][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 22:36:30,004][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 22:36:30,501][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 22:36:30,994][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 22:36:31,491][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 22:36:31,982][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 22:36:32,474][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 22:36:32,994][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 22:36:33,485][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 22:36:33,978][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 22:36:34,486][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 22:36:34,979][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 22:36:35,479][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 22:36:35,973][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 22:36:36,465][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 22:36:36,959][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 22:36:37,456][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 22:36:37,949][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 9716 tokens. [2025-11-12 22:36:38,594][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.00%, Current % of VRAM taken: 59.45%, Block Peak % of device VRAM: 61.89%, ΔTime: 00:00:32 [2025-11-12 22:36:39,381][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 22:36:39,385][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 22:36:39,388][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 22:36:40,343][__main__][INFO] - Iteration 29 took 50s (29.88% Gen, 68.24% Train). Generation: 15s, Training: 34s. Estimated remaining time: 41h 51m 28s. Estimated total time: 42h 18m 27s. Time estimates for 10 more iterations: 8m 27s, 100 more iterations: 1h 24m 36s, 500 more iterations: 7h 3m 4s. [2025-11-12 22:36:40,345][__main__][INFO] - Starting iteration 29. [2025-11-12 22:36:40,831][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 2 and human policies 1. [2025-11-12 22:36:40,831][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 22:36:54,777][__main__][INFO] - Number of regex retries in iteration 29: 0 [2025-11-12 22:36:54,778][__main__][INFO] - agents played in iteration 29 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 22:36:55,713][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.48%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:36:55,742][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.48%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:36:55,768][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.48%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:36:55,791][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.48%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:36:55,792][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 22:36:55,793][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 22:36:56,409][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 22:36:56,860][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 22:36:57,360][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 22:36:57,856][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 22:36:58,350][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 22:36:58,846][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 22:36:59,342][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 22:36:59,836][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 22:37:00,331][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 22:37:00,828][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 22:37:01,322][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-12 22:37:01,828][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-12 22:37:02,326][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-12 22:37:02,825][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-12 22:37:03,325][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-12 22:37:03,822][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-12 22:37:04,327][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-12 22:37:04,822][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-12 22:37:05,320][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-12 22:37:05,824][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-12 22:37:06,318][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-12 22:37:06,811][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-12 22:37:07,307][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-12 22:37:07,800][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-12 22:37:08,299][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-12 22:37:08,792][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-12 22:37:09,286][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-12 22:37:09,781][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-12 22:37:10,276][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-12 22:37:10,769][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-12 22:37:11,263][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-12 22:37:11,760][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-12 22:37:12,258][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-12 22:37:12,749][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 22:37:13,241][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 22:37:13,737][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 22:37:14,229][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 22:37:14,723][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 22:37:15,217][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 22:37:15,709][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 22:37:16,213][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 22:37:16,706][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 22:37:17,199][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 22:37:17,710][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 22:37:18,201][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 22:37:18,695][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 22:37:19,188][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 22:37:19,681][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 22:37:20,176][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 22:37:20,667][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 22:37:21,158][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 22:37:21,651][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 22:37:22,145][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 22:37:22,638][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 22:37:23,130][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 22:37:23,628][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 22:37:24,136][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 22:37:24,630][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 22:37:25,126][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 22:37:25,623][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 22:37:26,118][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 22:37:26,624][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 22:37:27,117][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 22:37:27,611][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 22:37:28,106][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 9754 tokens. [2025-11-12 22:37:28,734][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.00%, Current % of VRAM taken: 59.45%, Block Peak % of device VRAM: 61.75%, ΔTime: 00:00:32 [2025-11-12 22:37:29,561][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 22:37:29,563][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 22:37:29,565][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 22:37:30,554][__main__][INFO] - Iteration 30 took 49s (28.05% Gen, 69.96% Train). Generation: 13s, Training: 34s. Estimated remaining time: 40h 58m 21s. Estimated total time: 41h 26m 10s. Time estimates for 10 more iterations: 8m 17s, 100 more iterations: 1h 22m 52s, 500 more iterations: 6h 54m 21s. [2025-11-12 22:37:30,556][__main__][INFO] - Starting iteration 30. [2025-11-12 22:37:31,030][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 2 and human policies 1. [2025-11-12 22:37:31,031][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 22:37:38,930][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 0 books, 20 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 22:37:44,640][__main__][INFO] - Number of regex retries in iteration 30: 1 [2025-11-12 22:37:44,640][__main__][INFO] - agents played in iteration 30 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 22:37:45,476][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.47%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:37:45,501][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.47%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:37:45,525][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.47%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:37:45,547][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.47%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:37:45,548][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 22:37:45,548][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 22:37:46,158][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 22:37:46,614][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 22:37:47,115][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 22:37:47,613][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 22:37:48,107][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 22:37:48,602][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 22:37:49,108][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 22:37:49,605][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 22:37:50,103][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 22:37:50,612][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 22:37:51,107][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-12 22:37:51,604][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-12 22:37:52,109][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-12 22:37:52,608][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-12 22:37:53,107][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-12 22:37:53,603][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-12 22:37:54,098][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-12 22:37:54,608][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-12 22:37:55,104][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-12 22:37:55,601][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-12 22:37:56,095][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-12 22:37:56,589][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-12 22:37:57,082][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-12 22:37:57,575][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-12 22:37:58,067][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-12 22:37:58,562][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-12 22:37:59,056][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-12 22:37:59,551][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-12 22:38:00,044][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-12 22:38:00,539][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-12 22:38:01,047][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-12 22:38:01,540][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-12 22:38:02,035][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-12 22:38:02,531][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 22:38:03,026][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 22:38:03,520][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 22:38:04,015][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 22:38:04,509][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 22:38:05,026][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 22:38:05,520][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 22:38:06,016][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 22:38:06,516][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 22:38:07,012][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 22:38:07,511][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 22:38:08,005][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 22:38:08,499][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 22:38:08,994][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 22:38:09,487][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 22:38:09,981][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 22:38:10,479][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 22:38:10,973][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 22:38:11,467][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 22:38:11,963][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 22:38:12,461][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 22:38:12,960][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 22:38:13,457][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 22:38:13,951][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 22:38:14,449][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 22:38:14,949][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 22:38:15,450][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 22:38:15,952][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 22:38:16,447][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 22:38:16,944][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 22:38:17,442][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 22:38:17,940][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 9853 tokens. [2025-11-12 22:38:18,630][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 13.99%, Current % of VRAM taken: 59.44%, Block Peak % of device VRAM: 61.83%, ΔTime: 00:00:32 [2025-11-12 22:38:19,402][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 22:38:19,403][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 22:38:19,407][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 22:38:21,268][__main__][INFO] - Iteration 31 took 50s (27.09% Gen, 69.20% Train). Generation: 13s, Training: 34s. Estimated remaining time: 41h 23m 15s. Estimated total time: 41h 51m 55s. Time estimates for 10 more iterations: 8m 22s, 100 more iterations: 1h 23m 43s, 500 more iterations: 6h 58m 39s. [2025-11-12 22:38:21,270][__main__][INFO] - Starting iteration 31. [2025-11-12 22:38:21,742][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 3 and human policies 1. [2025-11-12 22:38:21,744][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 22:38:23,413][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 22:38:26,323][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 22:38:36,274][__main__][INFO] - Number of regex retries in iteration 31: 2 [2025-11-12 22:38:36,275][__main__][INFO] - agents played in iteration 31 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 22:38:37,052][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.47%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:38:37,080][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.47%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:38:37,106][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.47%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:38:37,129][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.47%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:38:37,129][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 22:38:37,130][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 22:38:37,762][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 22:38:38,215][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 22:38:38,714][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 22:38:39,211][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 22:38:39,706][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 22:38:40,201][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 22:38:40,694][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 22:38:41,187][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 22:38:41,687][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 22:38:42,183][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 22:38:42,675][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-12 22:38:43,170][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-12 22:38:43,663][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-12 22:38:44,158][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-12 22:38:44,651][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-12 22:38:45,146][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-12 22:38:45,651][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-12 22:38:46,147][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-12 22:38:46,642][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-12 22:38:47,152][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-12 22:38:47,645][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-12 22:38:48,140][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-12 22:38:48,632][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-12 22:38:49,128][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-12 22:38:49,646][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-12 22:38:50,161][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-12 22:38:50,656][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-12 22:38:51,153][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-12 22:38:51,647][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-12 22:38:52,142][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-12 22:38:52,638][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-12 22:38:53,140][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-12 22:38:53,648][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-12 22:38:54,142][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 22:38:54,636][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 22:38:55,132][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 22:38:55,625][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 22:38:56,126][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 22:38:56,620][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 22:38:57,116][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 22:38:57,611][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 22:38:58,104][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 22:38:58,597][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 22:38:59,090][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 22:38:59,587][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 22:39:00,081][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 22:39:00,574][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 22:39:01,066][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 22:39:01,561][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 22:39:02,054][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 22:39:02,548][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 22:39:03,042][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 22:39:03,537][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 22:39:04,043][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 22:39:04,538][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 22:39:05,037][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 22:39:05,534][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 22:39:06,029][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 22:39:06,526][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 22:39:07,021][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 22:39:07,520][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 22:39:08,031][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 22:39:08,527][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 22:39:09,021][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 22:39:09,516][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 9841 tokens. [2025-11-12 22:39:10,144][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.02%, Current % of VRAM taken: 59.48%, Block Peak % of device VRAM: 61.98%, ΔTime: 00:00:32 [2025-11-12 22:39:10,921][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 22:39:10,923][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 22:39:10,924][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 22:39:11,945][__main__][INFO] - Iteration 32 took 50s (28.94% Gen, 69.02% Train). Generation: 14s, Training: 34s. Estimated remaining time: 41h 20m 40s. Estimated total time: 41h 50m 10s. Time estimates for 10 more iterations: 8m 22s, 100 more iterations: 1h 23m 40s, 500 more iterations: 6h 58m 21s. [2025-11-12 22:39:11,948][__main__][INFO] - Starting iteration 32. [2025-11-12 22:39:12,432][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 3 and human policies 1. [2025-11-12 22:39:12,432][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 22:39:13,774][mllm.models.large_language_model_local][WARNING] - Response Proposal: 5 hats, 0 books, 5 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 22:39:25,844][__main__][INFO] - Number of regex retries in iteration 32: 1 [2025-11-12 22:39:25,845][__main__][INFO] - agents played in iteration 32 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 22:39:26,620][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.49%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:39:26,643][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.49%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:39:26,666][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.49%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:39:26,688][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.49%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:39:26,689][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 22:39:26,689][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 22:39:27,319][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 22:39:27,771][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 22:39:28,272][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 22:39:28,768][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 22:39:29,264][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 22:39:29,759][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 22:39:30,254][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 22:39:30,747][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 22:39:31,263][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 22:39:31,763][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 22:39:32,259][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-12 22:39:32,760][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-12 22:39:33,258][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-12 22:39:33,764][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-12 22:39:34,261][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-12 22:39:34,761][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-12 22:39:35,267][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-12 22:39:35,765][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-12 22:39:36,263][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-12 22:39:36,759][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-12 22:39:37,256][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-12 22:39:37,758][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-12 22:39:38,254][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-12 22:39:38,749][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-12 22:39:39,242][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-12 22:39:39,738][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-12 22:39:40,260][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-12 22:39:40,757][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-12 22:39:41,254][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-12 22:39:41,760][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-12 22:39:42,255][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-12 22:39:42,750][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-12 22:39:43,244][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-12 22:39:43,737][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 22:39:44,251][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 22:39:44,745][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 22:39:45,241][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 22:39:45,738][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 22:39:46,231][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 22:39:46,727][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 22:39:47,223][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 22:39:47,717][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 22:39:48,214][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 22:39:48,707][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 22:39:49,202][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 22:39:49,698][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 22:39:50,192][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 22:39:50,684][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 22:39:51,177][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 22:39:51,671][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 22:39:52,171][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 22:39:52,664][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 22:39:53,159][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 22:39:53,652][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 22:39:54,146][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 22:39:54,655][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 22:39:55,150][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 22:39:55,648][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 22:39:56,147][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 22:39:56,648][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 22:39:57,148][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 22:39:57,645][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 22:39:58,141][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 22:39:58,651][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 22:39:59,145][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 9741 tokens. [2025-11-12 22:39:59,794][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.08%, Current % of VRAM taken: 59.53%, Block Peak % of device VRAM: 61.95%, ΔTime: 00:00:32 [2025-11-12 22:40:00,528][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 22:40:00,530][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 22:40:00,531][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 22:40:01,470][__main__][INFO] - Iteration 33 took 49s (27.35% Gen, 70.73% Train). Generation: 13s, Training: 34s. Estimated remaining time: 40h 21m 37s. Estimated total time: 40h 51m 57s. Time estimates for 10 more iterations: 8m 10s, 100 more iterations: 1h 21m 43s, 500 more iterations: 6h 48m 39s. [2025-11-12 22:40:01,472][__main__][INFO] - Starting iteration 33. [2025-11-12 22:40:01,951][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 3 and human policies 1. [2025-11-12 22:40:01,952][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 22:40:16,149][__main__][INFO] - Number of regex retries in iteration 33: 0 [2025-11-12 22:40:16,150][__main__][INFO] - agents played in iteration 33 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 22:40:16,934][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.54%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:40:16,961][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.54%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:40:16,987][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.54%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:40:17,009][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.54%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:40:17,010][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 22:40:17,011][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 22:40:17,630][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 22:40:18,081][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 22:40:18,580][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 22:40:19,079][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 22:40:19,577][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 22:40:20,072][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 22:40:20,566][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 22:40:21,070][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 22:40:21,564][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 22:40:22,060][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 22:40:22,564][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-12 22:40:23,061][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-12 22:40:23,558][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-12 22:40:24,056][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-12 22:40:24,554][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-12 22:40:25,072][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-12 22:40:25,569][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-12 22:40:26,067][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-12 22:40:26,566][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-12 22:40:27,064][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-12 22:40:27,567][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-12 22:40:28,065][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-12 22:40:28,560][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-12 22:40:29,056][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-12 22:40:29,551][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-12 22:40:30,050][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-12 22:40:30,547][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-12 22:40:31,042][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-12 22:40:31,566][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-12 22:40:32,092][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-12 22:40:32,587][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-12 22:40:33,081][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-12 22:40:33,577][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-12 22:40:34,075][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 22:40:34,569][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 22:40:35,065][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 22:40:35,562][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 22:40:36,058][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 22:40:36,554][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 22:40:37,049][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 22:40:37,544][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 22:40:38,057][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 22:40:38,551][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 22:40:39,047][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 22:40:39,545][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 22:40:40,040][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 22:40:40,542][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 22:40:41,039][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 22:40:41,534][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 22:40:42,030][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 22:40:42,525][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 22:40:43,018][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 22:40:43,521][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 22:40:44,016][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 22:40:44,508][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 22:40:45,001][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 22:40:45,497][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 22:40:45,996][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 22:40:46,492][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 22:40:46,986][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 22:40:47,482][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 22:40:47,978][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 22:40:48,475][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 22:40:48,970][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 22:40:49,466][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 9878 tokens. [2025-11-12 22:40:50,145][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.01%, Current % of VRAM taken: 59.46%, Block Peak % of device VRAM: 61.84%, ΔTime: 00:00:32 [2025-11-12 22:40:51,042][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 22:40:51,045][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 22:40:51,048][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 22:40:51,983][__main__][INFO] - Iteration 34 took 50s (28.38% Gen, 69.75% Train). Generation: 14s, Training: 34s. Estimated remaining time: 41h 10m 26s. Estimated total time: 41h 41m 37s. Time estimates for 10 more iterations: 8m 20s, 100 more iterations: 1h 23m 23s, 500 more iterations: 6h 56m 56s. [2025-11-12 22:40:51,985][__main__][INFO] - Starting iteration 34. [2025-11-12 22:40:52,472][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 3 and human policies 1. [2025-11-12 22:40:52,472][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 22:41:07,196][__main__][INFO] - Number of regex retries in iteration 34: 0 [2025-11-12 22:41:07,197][__main__][INFO] - agents played in iteration 34 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 22:41:08,020][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.51%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:41:08,048][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.51%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:41:08,075][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.51%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:41:08,098][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.51%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:41:08,099][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 22:41:08,100][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 22:41:08,731][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 22:41:09,181][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 22:41:09,684][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 22:41:10,183][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 22:41:10,679][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 22:41:11,176][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 22:41:11,672][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 22:41:12,169][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 22:41:12,665][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 22:41:13,160][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 22:41:13,677][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-12 22:41:14,173][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-12 22:41:14,669][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-12 22:41:15,169][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-12 22:41:15,667][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-12 22:41:16,166][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-12 22:41:16,665][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-12 22:41:17,161][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-12 22:41:17,672][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-12 22:41:18,166][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-12 22:41:18,659][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-12 22:41:19,159][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-12 22:41:19,653][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-12 22:41:20,151][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-12 22:41:20,644][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-12 22:41:21,137][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-12 22:41:21,631][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-12 22:41:22,125][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-12 22:41:22,619][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-12 22:41:23,112][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-12 22:41:23,605][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-12 22:41:24,100][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-12 22:41:24,593][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-12 22:41:25,088][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 22:41:25,598][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 22:41:26,097][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 22:41:26,591][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 22:41:27,085][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 22:41:27,583][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 22:41:28,088][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 22:41:28,581][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 22:41:29,074][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 22:41:29,577][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 22:41:30,072][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 22:41:30,575][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 22:41:31,068][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 22:41:31,562][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 22:41:32,075][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 22:41:32,573][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 22:41:33,067][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 22:41:33,560][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 22:41:34,052][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 22:41:34,558][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 22:41:35,051][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 22:41:35,541][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 22:41:36,044][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 22:41:36,537][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 22:41:37,029][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 22:41:37,523][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 22:41:38,020][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 22:41:38,554][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 22:41:39,050][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 22:41:39,547][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 22:41:40,041][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 22:41:40,540][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 9841 tokens. [2025-11-12 22:41:41,196][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.14%, Current % of VRAM taken: 59.60%, Block Peak % of device VRAM: 61.98%, ΔTime: 00:00:32 [2025-11-12 22:41:41,941][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 22:41:41,945][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 22:41:41,947][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 22:41:42,957][__main__][INFO] - Iteration 35 took 50s (29.17% Gen, 68.83% Train). Generation: 14s, Training: 34s. Estimated remaining time: 41h 32m 16s. Estimated total time: 42h 4m 17s. Time estimates for 10 more iterations: 8m 24s, 100 more iterations: 1h 24m 8s, 500 more iterations: 7h 0m 42s. [2025-11-12 22:41:42,959][__main__][INFO] - Starting iteration 35. [2025-11-12 22:41:43,446][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 3 and human policies 1. [2025-11-12 22:41:43,447][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 22:41:45,780][mllm.models.large_language_model_local][WARNING] - Response Proposal: 5 hats, 5 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 22:41:57,962][__main__][INFO] - Number of regex retries in iteration 35: 1 [2025-11-12 22:41:57,962][__main__][INFO] - agents played in iteration 35 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 22:41:58,777][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:41:58,800][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:41:58,822][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:41:58,843][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:41:58,844][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 22:41:58,844][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 22:41:59,470][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 22:41:59,922][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 22:42:00,425][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 22:42:00,924][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 22:42:01,419][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 22:42:01,915][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 22:42:02,410][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 22:42:02,907][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 22:42:03,408][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 22:42:03,914][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 22:42:04,411][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-12 22:42:04,906][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-12 22:42:05,403][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-12 22:42:05,901][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-12 22:42:06,399][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-12 22:42:06,900][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-12 22:42:07,400][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-12 22:42:07,898][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-12 22:42:08,394][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-12 22:42:08,885][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-12 22:42:09,380][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-12 22:42:09,881][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-12 22:42:10,374][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-12 22:42:10,867][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-12 22:42:11,364][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-12 22:42:11,858][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-12 22:42:12,352][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-12 22:42:12,846][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-12 22:42:13,340][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-12 22:42:13,841][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-12 22:42:14,336][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-12 22:42:14,832][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-12 22:42:15,329][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-12 22:42:15,824][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 22:42:16,318][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 22:42:16,813][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 22:42:17,306][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 22:42:17,802][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 22:42:18,293][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 22:42:18,785][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 22:42:19,280][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 22:42:19,773][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 22:42:20,267][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 22:42:20,763][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 22:42:21,258][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 22:42:21,760][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 22:42:22,254][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 22:42:22,748][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 22:42:23,243][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 22:42:23,736][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 22:42:24,226][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 22:42:24,719][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 22:42:25,211][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 22:42:25,704][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 22:42:26,198][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 22:42:26,689][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 22:42:27,182][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 22:42:27,678][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 22:42:28,171][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 22:42:28,670][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 22:42:29,167][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 22:42:29,682][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 22:42:30,179][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 22:42:30,672][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 22:42:31,184][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10001 tokens. [2025-11-12 22:42:31,841][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.11%, Current % of VRAM taken: 59.56%, Block Peak % of device VRAM: 61.84%, ΔTime: 00:00:32 [2025-11-12 22:42:32,631][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 22:42:32,632][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 22:42:32,634][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 22:42:33,704][__main__][INFO] - Iteration 36 took 50s (28.88% Gen, 68.99% Train). Generation: 14s, Training: 34s. Estimated remaining time: 41h 20m 3s. Estimated total time: 41h 52m 55s. Time estimates for 10 more iterations: 8m 22s, 100 more iterations: 1h 23m 45s, 500 more iterations: 6h 58m 49s. [2025-11-12 22:42:33,706][__main__][INFO] - Starting iteration 36. [2025-11-12 22:42:34,175][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 3 and human policies 1. [2025-11-12 22:42:34,176][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 22:42:35,506][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 22:42:48,248][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 22:42:49,083][__main__][INFO] - Number of regex retries in iteration 36: 2 [2025-11-12 22:42:49,084][__main__][INFO] - agents played in iteration 36 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 22:42:49,930][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.53%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:42:49,959][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.53%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:42:49,985][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.53%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:42:50,008][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.53%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:42:50,009][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 22:42:50,010][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 22:42:50,627][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 22:42:51,095][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 22:42:51,597][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 22:42:52,101][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 22:42:52,599][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 22:42:53,106][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 22:42:53,606][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 22:42:54,106][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 22:42:54,604][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 22:42:55,105][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 22:42:55,602][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-12 22:42:56,103][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-12 22:42:56,604][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-12 22:42:57,102][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-12 22:42:57,608][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-12 22:42:58,110][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-12 22:42:58,617][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-12 22:42:59,119][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-12 22:42:59,626][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-12 22:43:00,124][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-12 22:43:00,624][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-12 22:43:01,121][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-12 22:43:01,624][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-12 22:43:02,122][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-12 22:43:02,620][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-12 22:43:03,116][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-12 22:43:03,612][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-12 22:43:04,110][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-12 22:43:04,605][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-12 22:43:05,103][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-12 22:43:05,600][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-12 22:43:06,096][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-12 22:43:06,592][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-12 22:43:07,086][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 22:43:07,581][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 22:43:08,094][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 22:43:08,588][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 22:43:09,084][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 22:43:09,578][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 22:43:10,072][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 22:43:10,582][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 22:43:11,077][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 22:43:11,570][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 22:43:12,067][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 22:43:12,565][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 22:43:13,065][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 22:43:13,562][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 22:43:14,058][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 22:43:14,572][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 22:43:15,065][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 22:43:15,559][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 22:43:16,062][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 22:43:16,556][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 22:43:17,060][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 22:43:17,554][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 22:43:18,049][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 22:43:18,554][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 22:43:19,048][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 22:43:19,545][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 22:43:20,045][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 22:43:20,542][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 22:43:21,042][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 22:43:21,539][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 22:43:22,038][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 22:43:22,537][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 9990 tokens. [2025-11-12 22:43:23,178][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.07%, Current % of VRAM taken: 59.52%, Block Peak % of device VRAM: 62.08%, ΔTime: 00:00:32 [2025-11-12 22:43:23,961][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 22:43:23,963][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 22:43:23,964][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 22:43:25,090][__main__][INFO] - Iteration 37 took 50s (29.28% Gen, 68.51% Train). Generation: 14s, Training: 34s. Estimated remaining time: 41h 52m 2s. Estimated total time: 42h 25m 45s. Time estimates for 10 more iterations: 8m 29s, 100 more iterations: 1h 24m 51s, 500 more iterations: 7h 4m 17s. [2025-11-12 22:43:25,092][__main__][INFO] - Starting iteration 37. [2025-11-12 22:43:25,562][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 3 and human policies 1. [2025-11-12 22:43:25,563][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 22:43:40,126][__main__][INFO] - Number of regex retries in iteration 37: 0 [2025-11-12 22:43:40,126][__main__][INFO] - agents played in iteration 37 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 22:43:40,940][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.50%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:43:40,962][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.50%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:43:40,987][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.50%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:43:41,008][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.50%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:43:41,009][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 22:43:41,009][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 22:43:41,623][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 22:43:42,073][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 22:43:42,577][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 22:43:43,089][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 22:43:43,584][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 22:43:44,079][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 22:43:44,577][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 22:43:45,079][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 22:43:45,573][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 22:43:46,075][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 22:43:46,581][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-12 22:43:47,101][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-12 22:43:47,625][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-12 22:43:48,125][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-12 22:43:48,624][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-12 22:43:49,122][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-12 22:43:49,621][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-12 22:43:50,119][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-12 22:43:50,617][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-12 22:43:51,117][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-12 22:43:51,611][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-12 22:43:52,106][mllm.training.trainer_common][INFO] - 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Processing mini-batch 43 of 64 [2025-11-12 22:44:03,571][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 22:44:04,089][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 22:44:04,593][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 22:44:05,095][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 22:44:05,593][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 22:44:06,087][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 22:44:06,593][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 22:44:07,088][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 22:44:07,584][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 22:44:08,080][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 22:44:08,576][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 22:44:09,073][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 22:44:09,571][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 22:44:10,069][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 22:44:10,584][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 22:44:11,082][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 22:44:11,582][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 22:44:12,081][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 22:44:12,578][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 22:44:13,077][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 22:44:13,578][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 9941 tokens. [2025-11-12 22:44:14,205][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.11%, Current % of VRAM taken: 59.56%, Block Peak % of device VRAM: 62.06%, ΔTime: 00:00:32 [2025-11-12 22:44:14,978][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 22:44:14,979][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 22:44:14,982][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 22:44:16,025][__main__][INFO] - Iteration 38 took 50s (28.86% Gen, 69.07% Train). Generation: 14s, Training: 34s. Estimated remaining time: 41h 28m 37s. Estimated total time: 42h 3m 11s. Time estimates for 10 more iterations: 8m 24s, 100 more iterations: 1h 24m 6s, 500 more iterations: 7h 0m 31s. [2025-11-12 22:44:16,028][__main__][INFO] - Starting iteration 38. [2025-11-12 22:44:16,517][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 3 and human policies 1. [2025-11-12 22:44:16,518][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 22:44:30,883][__main__][INFO] - Number of regex retries in iteration 38: 0 [2025-11-12 22:44:30,884][__main__][INFO] - agents played in iteration 38 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 22:44:31,673][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.52%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:44:31,702][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.52%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:44:31,729][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.52%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:44:31,752][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.52%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:44:31,753][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 22:44:31,754][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 22:44:32,370][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 22:44:32,831][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 22:44:33,335][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 22:44:33,832][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 22:44:34,330][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 22:44:34,826][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 22:44:35,326][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 22:44:35,824][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 22:44:36,324][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 22:44:36,824][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 22:44:37,324][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-12 22:44:48,811][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 22:44:49,306][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 22:44:49,802][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 22:44:50,303][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 22:44:50,807][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 22:44:51,316][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 22:44:51,811][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 22:44:52,309][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 22:44:52,814][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 22:44:53,309][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 22:44:53,811][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 22:44:54,306][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 22:44:54,803][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 22:44:55,302][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 22:44:55,802][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 22:44:56,301][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 22:44:56,797][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 22:44:57,290][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 22:44:57,798][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 22:44:58,292][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 22:44:58,785][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 22:44:59,280][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 22:44:59,775][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 22:45:00,273][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 22:45:00,770][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 22:45:01,269][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 22:45:01,781][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 22:45:02,278][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 22:45:02,774][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 22:45:03,282][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 22:45:03,777][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 22:45:04,278][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10002 tokens. [2025-11-12 22:45:04,922][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.10%, Current % of VRAM taken: 59.55%, Block Peak % of device VRAM: 61.97%, ΔTime: 00:00:32 [2025-11-12 22:45:05,692][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 22:45:05,695][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 22:45:05,698][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 22:45:06,596][__main__][INFO] - Iteration 39 took 50s (28.68% Gen, 69.52% Train). Generation: 14s, Training: 34s. Estimated remaining time: 41h 8m 31s. Estimated total time: 41h 43m 55s. Time estimates for 10 more iterations: 8m 20s, 100 more iterations: 1h 23m 27s, 500 more iterations: 6h 57m 19s. [2025-11-12 22:45:06,598][__main__][INFO] - Starting iteration 39. [2025-11-12 22:45:07,073][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 3 and human policies 1. [2025-11-12 22:45:07,073][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 22:45:20,852][__main__][INFO] - Number of regex retries in iteration 39: 0 [2025-11-12 22:45:20,853][__main__][INFO] - agents played in iteration 39 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 22:45:21,637][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.50%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:45:21,665][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.50%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:45:21,691][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.50%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:45:21,713][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.50%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:45:21,714][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 22:45:21,714][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 22:45:22,332][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 22:45:22,804][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 22:45:23,305][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 22:45:23,805][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 22:45:24,307][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 22:45:24,804][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 22:45:25,312][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 22:45:25,813][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 22:45:26,309][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 22:45:26,814][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 22:45:27,309][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-12 22:45:27,804][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-12 22:45:28,302][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-12 22:45:28,798][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-12 22:45:29,292][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-12 22:45:29,792][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-12 22:45:30,289][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-12 22:45:30,809][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-12 22:45:31,306][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-12 22:45:31,805][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-12 22:45:32,304][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-12 22:45:32,798][mllm.training.trainer_common][INFO] - 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Processing mini-batch 43 of 64 [2025-11-12 22:45:44,188][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 22:45:44,683][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 22:45:45,187][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 22:45:45,682][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 22:45:46,176][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 22:45:46,672][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 22:45:47,167][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 22:45:47,669][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 22:45:48,165][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 22:45:48,662][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 22:45:49,159][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 22:45:49,652][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 22:45:50,151][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 22:45:50,650][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 22:45:51,148][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 22:45:51,668][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 22:45:52,165][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 22:45:52,661][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 22:45:53,162][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 22:45:53,658][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 22:45:54,154][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 9831 tokens. [2025-11-12 22:45:54,780][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.12%, Current % of VRAM taken: 59.57%, Block Peak % of device VRAM: 61.94%, ΔTime: 00:00:32 [2025-11-12 22:45:55,544][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 22:45:55,548][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 22:45:55,550][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 22:45:56,497][__main__][INFO] - Iteration 40 took 49s (27.88% Gen, 70.20% Train). Generation: 13s, Training: 34s. Estimated remaining time: 40h 34m 59s. Estimated total time: 41h 11m 13s. Time estimates for 10 more iterations: 8m 14s, 100 more iterations: 1h 22m 22s, 500 more iterations: 6h 51m 52s. [2025-11-12 22:45:56,499][__main__][INFO] - Starting iteration 40. [2025-11-12 22:45:56,973][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 3 and human policies 1. [2025-11-12 22:45:56,973][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 22:46:11,114][__main__][INFO] - Number of regex retries in iteration 40: 0 [2025-11-12 22:46:11,114][__main__][INFO] - agents played in iteration 40 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 22:46:11,899][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.53%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:46:11,922][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.53%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:46:11,944][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.53%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:46:11,965][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.53%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:46:11,966][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 22:46:11,967][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 22:46:12,588][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 22:46:13,042][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 22:46:13,547][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 22:46:14,048][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 22:46:14,548][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 22:46:15,046][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 22:46:15,549][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 22:46:16,044][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 22:46:16,548][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 22:46:17,043][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 22:46:17,565][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-12 22:46:29,032][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 22:46:29,528][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 22:46:30,024][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 22:46:30,520][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 22:46:31,029][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 22:46:31,522][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 22:46:32,018][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 22:46:32,529][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 22:46:33,025][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 22:46:33,524][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 22:46:34,022][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 22:46:34,517][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 22:46:35,011][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 22:46:35,517][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 22:46:36,011][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 22:46:36,520][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 22:46:37,014][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 22:46:37,512][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 22:46:38,017][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 22:46:38,511][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 22:46:39,005][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 22:46:39,500][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 22:46:39,994][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 22:46:40,493][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 22:46:40,989][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 22:46:41,489][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 22:46:41,987][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 22:46:42,488][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 22:46:42,987][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 22:46:43,485][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 22:46:43,986][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 22:46:44,485][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10002 tokens. [2025-11-12 22:46:45,153][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.10%, Current % of VRAM taken: 59.55%, Block Peak % of device VRAM: 61.97%, ΔTime: 00:00:32 [2025-11-12 22:46:45,937][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 22:46:45,939][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 22:46:45,941][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 22:46:47,778][__main__][INFO] - Iteration 41 took 50s (27.83% Gen, 68.55% Train). Generation: 14s, Training: 34s. Estimated remaining time: 41h 43m 11s. Estimated total time: 42h 20m 18s. Time estimates for 10 more iterations: 8m 28s, 100 more iterations: 1h 24m 40s, 500 more iterations: 7h 3m 23s. [2025-11-12 22:46:47,781][__main__][INFO] - Starting iteration 41. [2025-11-12 22:46:48,263][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 4 and human policies 1. [2025-11-12 22:46:48,264][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 22:47:03,183][__main__][INFO] - Number of regex retries in iteration 41: 0 [2025-11-12 22:47:03,183][__main__][INFO] - agents played in iteration 41 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 22:47:03,965][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.51%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:47:03,990][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.51%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:47:04,016][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.51%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:47:04,038][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.51%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:47:04,038][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 22:47:04,039][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 22:47:04,670][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 22:47:05,120][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 22:47:05,629][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 22:47:06,126][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 22:47:06,622][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 22:47:07,124][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 22:47:07,621][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 22:47:08,119][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 22:47:08,614][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 22:47:09,108][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 22:47:09,604][mllm.training.trainer_common][INFO] - 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Processing mini-batch 21 of 64 [2025-11-12 22:47:15,568][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-12 22:47:16,066][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-12 22:47:16,566][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-12 22:47:17,062][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-12 22:47:17,580][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-12 22:47:18,082][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-12 22:47:18,577][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-12 22:47:19,072][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-12 22:47:19,566][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-12 22:47:20,065][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-12 22:47:20,560][mllm.training.trainer_common][INFO] - 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Processing mini-batch 43 of 64 [2025-11-12 22:47:26,527][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 22:47:27,024][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 22:47:27,521][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 22:47:28,043][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 22:47:28,541][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 22:47:29,039][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 22:47:29,542][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 22:47:30,037][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 22:47:30,544][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 22:47:31,043][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 22:47:31,541][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 22:47:32,041][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 22:47:32,546][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 22:47:33,042][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 22:47:33,540][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 22:47:34,035][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 22:47:34,548][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 22:47:35,044][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 22:47:35,541][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 22:47:36,055][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 22:47:36,552][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10142 tokens. [2025-11-12 22:47:37,177][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.07%, Current % of VRAM taken: 59.52%, Block Peak % of device VRAM: 62.10%, ΔTime: 00:00:32 [2025-11-12 22:47:37,923][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 22:47:37,927][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 22:47:37,929][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 22:47:38,832][__main__][INFO] - Iteration 42 took 50s (29.50% Gen, 68.71% Train). Generation: 14s, Training: 34s. Estimated remaining time: 41h 30m 30s. Estimated total time: 42h 8m 27s. Time estimates for 10 more iterations: 8m 25s, 100 more iterations: 1h 24m 16s, 500 more iterations: 7h 1m 24s. [2025-11-12 22:47:38,834][__main__][INFO] - Starting iteration 42. [2025-11-12 22:47:39,316][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 4 and human policies 1. [2025-11-12 22:47:39,317][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 22:47:41,216][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 22:47:54,056][__main__][INFO] - Number of regex retries in iteration 42: 1 [2025-11-12 22:47:54,057][__main__][INFO] - agents played in iteration 42 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 22:47:54,840][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.52%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:47:54,868][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.52%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:47:54,895][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.52%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:47:54,918][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.52%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:47:54,919][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 22:47:54,921][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 22:47:55,533][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 22:47:56,100][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 22:47:56,601][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 22:47:57,097][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 22:47:57,591][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 22:47:58,087][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 22:47:58,582][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 22:47:59,076][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 22:47:59,576][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 22:48:00,075][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 22:48:00,573][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-12 22:48:12,038][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 22:48:12,536][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 22:48:13,031][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 22:48:13,524][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 22:48:14,021][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 22:48:14,514][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 22:48:15,008][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 22:48:15,504][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 22:48:16,000][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 22:48:16,499][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 22:48:16,995][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 22:48:17,490][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 22:48:17,986][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 22:48:18,481][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 22:48:18,978][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 22:48:19,470][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 22:48:19,963][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 22:48:20,465][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 22:48:20,960][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 22:48:21,453][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 22:48:21,950][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 22:48:22,444][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 22:48:22,937][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 22:48:23,435][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 22:48:23,932][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 22:48:24,429][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 22:48:24,927][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 22:48:25,426][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 22:48:25,926][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 22:48:26,423][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 22:48:26,922][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 22:48:27,417][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 9948 tokens. [2025-11-12 22:48:28,051][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.03%, Current % of VRAM taken: 59.48%, Block Peak % of device VRAM: 61.79%, ΔTime: 00:00:32 [2025-11-12 22:48:28,819][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 22:48:28,820][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 22:48:28,822][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 22:48:30,104][__main__][INFO] - Iteration 43 took 50s (29.02% Gen, 68.45% Train). Generation: 14s, Training: 34s. Estimated remaining time: 41h 40m 36s. Estimated total time: 42h 19m 24s. Time estimates for 10 more iterations: 8m 27s, 100 more iterations: 1h 24m 38s, 500 more iterations: 7h 3m 14s. [2025-11-12 22:48:30,106][__main__][INFO] - Starting iteration 43. [2025-11-12 22:48:30,601][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 4 and human policies 1. [2025-11-12 22:48:30,601][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 22:48:45,802][__main__][INFO] - Number of regex retries in iteration 43: 0 [2025-11-12 22:48:45,803][__main__][INFO] - agents played in iteration 43 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 22:48:46,635][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.48%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:48:46,663][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.48%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:48:46,690][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.48%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:48:46,712][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.48%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:48:46,713][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 22:48:46,713][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 22:48:47,324][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 22:48:47,777][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 22:48:48,282][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 22:48:48,777][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 22:48:49,273][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 22:48:49,769][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 22:48:50,263][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 22:48:50,772][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 22:48:51,266][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 22:48:51,763][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 22:48:52,267][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-12 22:49:03,767][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 22:49:04,268][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 22:49:04,764][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 22:49:05,270][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 22:49:05,765][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 22:49:06,263][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 22:49:06,779][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 22:49:07,300][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 22:49:07,801][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 22:49:08,296][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 22:49:08,789][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 22:49:09,288][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 22:49:09,781][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 22:49:10,276][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 22:49:10,772][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 22:49:11,266][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 22:49:11,760][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 22:49:12,259][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 22:49:12,753][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 22:49:13,249][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 22:49:13,742][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 22:49:14,236][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 22:49:14,736][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 22:49:15,236][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 22:49:15,738][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 22:49:16,235][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 22:49:16,735][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 22:49:17,235][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 22:49:17,730][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 22:49:18,228][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 22:49:18,729][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 22:49:19,224][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10131 tokens. [2025-11-12 22:49:19,856][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.16%, Current % of VRAM taken: 59.61%, Block Peak % of device VRAM: 62.19%, ΔTime: 00:00:32 [2025-11-12 22:49:20,588][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 22:49:20,592][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 22:49:20,594][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 22:49:21,652][__main__][INFO] - Iteration 44 took 51s (29.78% Gen, 68.15% Train). Generation: 15s, Training: 34s. Estimated remaining time: 41h 52m 55s. Estimated total time: 42h 32m 35s. Time estimates for 10 more iterations: 8m 30s, 100 more iterations: 1h 25m 5s, 500 more iterations: 7h 5m 25s. [2025-11-12 22:49:21,654][__main__][INFO] - Starting iteration 44. [2025-11-12 22:49:22,118][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 4 and human policies 1. [2025-11-12 22:49:22,119][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 22:49:37,227][__main__][INFO] - Number of regex retries in iteration 44: 0 [2025-11-12 22:49:37,227][__main__][INFO] - agents played in iteration 44 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 22:49:38,055][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:49:38,078][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:49:38,100][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:49:38,122][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:49:38,123][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 22:49:38,123][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 22:49:38,734][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 22:49:39,187][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 22:49:39,688][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 22:49:40,186][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 22:49:40,684][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 22:49:41,181][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 22:49:41,678][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 22:49:42,191][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 22:49:42,688][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 22:49:43,184][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 22:49:43,684][mllm.training.trainer_common][INFO] - 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Processing mini-batch 21 of 64 [2025-11-12 22:49:49,676][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-12 22:49:50,186][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-12 22:49:50,682][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-12 22:49:51,177][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-12 22:49:51,671][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-12 22:49:52,163][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-12 22:49:52,672][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-12 22:49:53,166][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-12 22:49:53,660][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-12 22:49:54,163][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-12 22:49:54,657][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-12 22:49:55,162][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 22:49:55,663][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 22:49:56,160][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 22:49:56,657][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 22:49:57,177][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 22:49:57,672][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 22:49:58,172][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 22:49:58,670][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 22:49:59,188][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 22:49:59,682][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 22:50:00,176][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 22:50:00,671][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 22:50:01,165][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 22:50:01,659][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 22:50:02,152][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 22:50:02,646][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 22:50:03,145][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 22:50:03,642][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 22:50:04,135][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 22:50:04,631][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 22:50:05,125][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 22:50:05,623][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 22:50:06,123][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 22:50:06,626][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 22:50:07,132][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 22:50:07,629][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 22:50:08,125][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 22:50:08,623][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 22:50:09,125][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 22:50:09,623][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 22:50:10,122][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 22:50:10,615][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10118 tokens. [2025-11-12 22:50:11,259][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.03%, Current % of VRAM taken: 59.49%, Block Peak % of device VRAM: 62.14%, ΔTime: 00:00:32 [2025-11-12 22:50:12,025][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 22:50:12,027][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 22:50:12,029][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 22:50:12,965][__main__][INFO] - Iteration 45 took 50s (29.71% Gen, 68.44% Train). Generation: 15s, Training: 34s. Estimated remaining time: 41h 41m 50s. Estimated total time: 42h 22m 21s. Time estimates for 10 more iterations: 8m 28s, 100 more iterations: 1h 24m 44s, 500 more iterations: 7h 3m 43s. [2025-11-12 22:50:12,967][__main__][INFO] - Starting iteration 45. [2025-11-12 22:50:13,443][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 4 and human policies 1. [2025-11-12 22:50:13,443][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 22:50:14,895][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 22:50:18,624][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 22:50:26,559][__main__][INFO] - Number of regex retries in iteration 45: 2 [2025-11-12 22:50:26,559][__main__][INFO] - agents played in iteration 45 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 22:50:27,335][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.51%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:50:27,358][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.51%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:50:27,379][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.51%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:50:27,401][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.51%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:50:27,402][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 22:50:27,403][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 22:50:28,036][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 22:50:28,488][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 22:50:28,988][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 22:50:29,500][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 22:50:29,999][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 22:50:30,494][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 22:50:30,991][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 22:50:31,488][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 22:50:31,987][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 22:50:32,484][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 22:50:32,981][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-12 22:50:44,501][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 22:50:44,995][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 22:50:45,496][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 22:50:45,989][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 22:50:46,483][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 22:50:46,992][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 22:50:47,485][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 22:50:47,979][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 22:50:48,476][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 22:50:48,969][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 22:50:49,476][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 22:50:49,969][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 22:50:50,461][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 22:50:50,969][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 22:50:51,462][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 22:50:51,962][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 22:50:52,478][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 22:50:52,979][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 22:50:53,488][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 22:50:53,984][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 22:50:54,481][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 22:50:54,983][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 22:50:55,480][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 22:50:55,978][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 22:50:56,491][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 22:50:56,985][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 22:50:57,482][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 22:50:57,981][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 22:50:58,478][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 22:50:58,995][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 22:50:59,497][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 22:50:59,993][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10030 tokens. [2025-11-12 22:51:00,648][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.04%, Current % of VRAM taken: 59.49%, Block Peak % of device VRAM: 62.12%, ΔTime: 00:00:32 [2025-11-12 22:51:01,407][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 22:51:01,409][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 22:51:01,410][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 22:51:02,349][__main__][INFO] - Iteration 46 took 48s (26.82% Gen, 71.26% Train). Generation: 13s, Training: 34s. Estimated remaining time: 40h 4m 2s. Estimated total time: 40h 45m 23s. Time estimates for 10 more iterations: 8m 9s, 100 more iterations: 1h 21m 30s, 500 more iterations: 6h 47m 33s. [2025-11-12 22:51:02,351][__main__][INFO] - Starting iteration 46. [2025-11-12 22:51:02,826][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 4 and human policies 1. [2025-11-12 22:51:02,827][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 22:51:17,398][__main__][INFO] - Number of regex retries in iteration 46: 0 [2025-11-12 22:51:17,399][__main__][INFO] - agents played in iteration 46 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 22:51:18,189][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.50%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:51:18,215][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.50%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:51:18,240][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.50%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:51:18,262][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.50%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:51:18,263][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 22:51:18,264][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 22:51:18,895][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 22:51:19,349][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 22:51:19,851][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 22:51:20,369][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 22:51:20,865][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 22:51:21,366][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 22:51:21,872][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 22:51:22,369][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 22:51:22,871][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 22:51:23,367][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 22:51:23,868][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-12 22:51:35,393][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 22:51:35,890][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 22:51:36,389][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 22:51:36,883][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 22:51:37,396][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 22:51:37,895][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 22:51:38,393][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 22:51:38,890][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 22:51:39,384][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 22:51:39,879][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 22:51:40,374][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 22:51:40,869][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 22:51:41,372][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 22:51:41,867][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 22:51:42,367][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 22:51:42,873][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 22:51:43,368][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 22:51:43,866][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 22:51:44,361][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 22:51:44,859][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 22:51:45,361][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 22:51:45,854][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 22:51:46,348][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 22:51:46,843][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 22:51:47,339][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 22:51:47,835][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 22:51:48,336][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 22:51:48,834][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 22:51:49,335][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 22:51:49,834][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 22:51:50,331][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 22:51:50,827][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10103 tokens. [2025-11-12 22:51:51,500][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.07%, Current % of VRAM taken: 59.52%, Block Peak % of device VRAM: 62.02%, ΔTime: 00:00:32 [2025-11-12 22:51:52,221][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 22:51:52,223][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 22:51:52,225][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 22:51:53,243][__main__][INFO] - Iteration 47 took 50s (28.90% Gen, 69.08% Train). Generation: 14s, Training: 34s. Estimated remaining time: 41h 18m 38s. Estimated total time: 42h 0m 50s. Time estimates for 10 more iterations: 8m 24s, 100 more iterations: 1h 24m 1s, 500 more iterations: 7h 0m 8s. [2025-11-12 22:51:53,245][__main__][INFO] - Starting iteration 47. [2025-11-12 22:51:53,746][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 4 and human policies 1. [2025-11-12 22:51:53,747][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 22:52:08,136][__main__][INFO] - Number of regex retries in iteration 47: 0 [2025-11-12 22:52:08,137][__main__][INFO] - agents played in iteration 47 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 22:52:08,938][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.51%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:52:08,960][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.51%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:52:08,982][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.51%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:52:09,003][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.51%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:52:09,004][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 22:52:09,005][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 22:52:09,608][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 22:52:10,058][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 22:52:10,570][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 22:52:11,067][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 22:52:11,566][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 22:52:12,064][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 22:52:12,561][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 22:52:13,063][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 22:52:13,559][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 22:52:14,054][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 22:52:14,548][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-12 22:52:15,044][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-12 22:52:15,549][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-12 22:52:16,045][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-12 22:52:16,541][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-12 22:52:17,039][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-12 22:52:17,535][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-12 22:52:18,033][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-12 22:52:18,527][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-12 22:52:19,025][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-12 22:52:19,529][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-12 22:52:20,028][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-12 22:52:26,040][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 22:52:26,536][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 22:52:27,031][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 22:52:27,531][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 22:52:28,027][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 22:52:28,528][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 22:52:29,025][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 22:52:29,521][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 22:52:30,026][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 22:52:30,522][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 22:52:31,017][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 22:52:31,511][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 22:52:32,006][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 22:52:32,512][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 22:52:33,007][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 22:52:33,502][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 22:52:34,008][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 22:52:34,501][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 22:52:34,994][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 22:52:35,493][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 22:52:35,986][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 22:52:36,480][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 22:52:36,972][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 22:52:37,465][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 22:52:37,960][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 22:52:38,456][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 22:52:38,953][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 22:52:39,452][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 22:52:39,949][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 22:52:40,447][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 22:52:40,946][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 22:52:41,442][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10075 tokens. [2025-11-12 22:52:42,099][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.15%, Current % of VRAM taken: 59.60%, Block Peak % of device VRAM: 62.07%, ΔTime: 00:00:32 [2025-11-12 22:52:42,819][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 22:52:42,820][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 22:52:42,822][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 22:52:43,794][__main__][INFO] - Iteration 48 took 50s (28.75% Gen, 69.30% Train). Generation: 14s, Training: 34s. Estimated remaining time: 40h 59m 22s. Estimated total time: 41h 42m 24s. Time estimates for 10 more iterations: 8m 20s, 100 more iterations: 1h 23m 24s, 500 more iterations: 6h 57m 4s. [2025-11-12 22:52:43,796][__main__][INFO] - Starting iteration 48. [2025-11-12 22:52:44,288][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 4 and human policies 1. [2025-11-12 22:52:44,288][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 22:52:58,887][__main__][INFO] - Number of regex retries in iteration 48: 0 [2025-11-12 22:52:58,888][__main__][INFO] - agents played in iteration 48 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 22:52:59,664][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:52:59,689][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:52:59,713][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:52:59,736][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:52:59,736][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 22:52:59,737][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 22:53:00,364][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 22:53:00,818][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 22:53:01,324][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 22:53:01,821][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 22:53:02,320][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 22:53:02,824][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 22:53:03,319][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 22:53:03,814][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 22:53:04,309][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 22:53:04,809][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 22:53:05,314][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-12 22:53:05,809][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-12 22:53:06,304][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-12 22:53:06,803][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-12 22:53:07,299][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-12 22:53:07,794][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-12 22:53:08,289][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-12 22:53:08,784][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-12 22:53:09,292][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-12 22:53:09,787][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-12 22:53:10,282][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-12 22:53:10,789][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-12 22:53:11,287][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-12 22:53:11,786][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-12 22:53:12,292][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-12 22:53:12,788][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-12 22:53:13,292][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-12 22:53:13,792][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-12 22:53:14,285][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-12 22:53:14,794][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-12 22:53:15,289][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-12 22:53:15,785][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-12 22:53:16,290][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-12 22:53:16,792][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 22:53:17,320][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 22:53:17,817][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 22:53:18,315][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 22:53:18,812][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 22:53:19,314][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 22:53:19,811][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 22:53:20,310][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 22:53:20,807][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 22:53:21,323][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 22:53:21,816][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 22:53:22,311][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 22:53:22,807][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 22:53:23,302][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 22:53:23,796][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 22:53:24,290][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 22:53:24,786][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 22:53:25,284][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 22:53:25,775][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 22:53:26,267][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 22:53:26,763][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 22:53:27,255][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 22:53:27,750][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 22:53:28,246][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 22:53:28,740][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 22:53:29,235][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 22:53:29,730][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 22:53:30,233][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 22:53:30,726][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 22:53:31,224][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 22:53:31,725][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 22:53:32,222][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 9933 tokens. [2025-11-12 22:53:32,889][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.08%, Current % of VRAM taken: 59.53%, Block Peak % of device VRAM: 62.05%, ΔTime: 00:00:32 [2025-11-12 22:53:33,611][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 22:53:33,613][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 22:53:33,614][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 22:53:34,528][__main__][INFO] - Iteration 49 took 50s (29.06% Gen, 69.12% Train). Generation: 14s, Training: 34s. Estimated remaining time: 41h 8m 9s. Estimated total time: 41h 52m 2s. Time estimates for 10 more iterations: 8m 22s, 100 more iterations: 1h 23m 44s, 500 more iterations: 6h 58m 40s. [2025-11-12 22:53:34,530][__main__][INFO] - Starting iteration 49. [2025-11-12 22:53:35,043][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 4 and human policies 1. [2025-11-12 22:53:35,044][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 22:53:37,772][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 22:53:38,009][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 22:53:42,063][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 0 balls Given the per-item values, I value balls the highest at 10, while Alice values books the highest at 10. To maximize my points, I will propose to keep all the hats since my value for hats is the same as Alice's, and I will not propose to keep any books or balls to avoid the proportional allocation risk when the total proposed amount exceeds the item quantity. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 22:53:48,507][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 22:53:51,235][__main__][INFO] - Number of regex retries in iteration 49: 4 [2025-11-12 22:53:51,235][__main__][INFO] - agents played in iteration 49 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 22:53:52,066][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.54%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:53:52,095][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.54%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:53:52,121][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.54%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:53:52,144][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.54%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:53:52,144][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 22:53:52,145][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 22:53:52,764][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 22:53:53,217][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 22:53:53,723][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 22:53:54,236][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 22:53:54,733][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 22:53:55,236][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 22:53:55,735][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 22:53:56,231][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 22:53:56,734][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 22:53:57,230][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 22:53:57,727][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-12 22:53:58,232][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-12 22:53:58,733][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-12 22:53:59,229][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-12 22:53:59,735][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-12 22:54:00,231][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-12 22:54:00,731][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-12 22:54:01,226][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-12 22:54:01,721][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-12 22:54:02,218][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-12 22:54:02,715][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-12 22:54:03,211][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-12 22:54:03,712][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-12 22:54:04,208][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-12 22:54:04,705][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-12 22:54:05,209][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-12 22:54:05,706][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-12 22:54:06,224][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-12 22:54:06,719][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-12 22:54:07,214][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-12 22:54:07,740][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-12 22:54:08,236][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-12 22:54:08,738][mllm.training.trainer_common][INFO] - 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Processing mini-batch 43 of 64 [2025-11-12 22:54:14,708][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 22:54:15,206][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 22:54:15,699][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 22:54:16,194][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 22:54:16,689][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 22:54:17,182][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 22:54:17,676][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 22:54:18,173][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 22:54:18,667][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 22:54:19,163][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 22:54:19,657][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 22:54:20,151][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 22:54:20,648][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 22:54:21,145][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 22:54:21,644][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 22:54:22,139][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 22:54:22,635][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 22:54:23,145][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 22:54:23,641][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 22:54:24,138][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 22:54:24,636][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10031 tokens. [2025-11-12 22:54:25,261][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.08%, Current % of VRAM taken: 59.53%, Block Peak % of device VRAM: 62.03%, ΔTime: 00:00:32 [2025-11-12 22:54:26,035][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 22:54:26,037][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 22:54:26,038][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 22:54:26,963][__main__][INFO] - Iteration 50 took 51s (31.16% Gen, 66.99% Train). Generation: 16s, Training: 34s. Estimated remaining time: 42h 33m 1s. Estimated total time: 43h 17m 47s. Time estimates for 10 more iterations: 8m 39s, 100 more iterations: 1h 26m 35s, 500 more iterations: 7h 12m 57s. [2025-11-12 22:54:26,965][__main__][INFO] - Starting iteration 50. [2025-11-12 22:54:27,430][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 4 and human policies 1. [2025-11-12 22:54:27,431][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 22:54:28,932][mllm.models.large_language_model_local][WARNING] - Response Proposal: 5 hats, 5 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 22:54:41,983][__main__][INFO] - Number of regex retries in iteration 50: 1 [2025-11-12 22:54:41,983][__main__][INFO] - agents played in iteration 50 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 22:54:42,803][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.53%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:54:42,828][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.53%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:54:42,852][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.53%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:54:42,874][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.53%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:54:42,875][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 22:54:42,876][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 22:54:43,481][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 22:54:43,939][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 22:54:44,440][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 22:54:44,943][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 22:54:45,441][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 22:54:45,935][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 22:54:46,429][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 22:54:46,926][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 22:54:47,421][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 22:54:47,935][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 22:54:48,430][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-12 22:54:48,928][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-12 22:54:49,427][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-12 22:54:49,923][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-12 22:54:50,429][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-12 22:54:50,925][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-12 22:54:51,422][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-12 22:54:51,934][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-12 22:54:52,430][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-12 22:54:52,926][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-12 22:54:53,427][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-12 22:54:53,923][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-12 22:54:54,428][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-12 22:54:54,927][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-12 22:54:55,424][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-12 22:54:55,925][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-12 22:54:56,427][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-12 22:54:56,924][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-12 22:54:57,419][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-12 22:54:57,911][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-12 22:54:58,407][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-12 22:54:58,902][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-12 22:54:59,396][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-12 22:54:59,892][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 22:55:00,384][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 22:55:00,878][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 22:55:01,373][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 22:55:01,867][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 22:55:02,362][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 22:55:02,856][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 22:55:03,350][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 22:55:03,857][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 22:55:04,351][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 22:55:04,850][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 22:55:05,355][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 22:55:05,849][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 22:55:06,346][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 22:55:06,839][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 22:55:07,333][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 22:55:07,853][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 22:55:08,349][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 22:55:08,846][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 22:55:09,341][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 22:55:09,835][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 22:55:10,341][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 22:55:10,838][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 22:55:11,333][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 22:55:11,836][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 22:55:12,335][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 22:55:12,831][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 22:55:13,329][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 22:55:13,827][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 22:55:14,339][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 22:55:14,840][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 22:55:15,336][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 9986 tokens. [2025-11-12 22:55:16,018][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.12%, Current % of VRAM taken: 59.57%, Block Peak % of device VRAM: 62.11%, ΔTime: 00:00:32 [2025-11-12 22:55:16,776][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 22:55:16,777][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 22:55:16,780][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 22:55:19,019][__main__][INFO] - Iteration 51 took 51s (28.21% Gen, 67.45% Train). Generation: 14s, Training: 34s. Estimated remaining time: 42h 13m 51s. Estimated total time: 42h 59m 28s. Time estimates for 10 more iterations: 8m 35s, 100 more iterations: 1h 25m 58s, 500 more iterations: 7h 9m 54s. [2025-11-12 22:55:19,021][__main__][INFO] - Starting iteration 51. [2025-11-12 22:55:19,516][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 5 and human policies 1. [2025-11-12 22:55:19,516][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 22:55:21,575][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 22:55:34,289][__main__][INFO] - Number of regex retries in iteration 51: 1 [2025-11-12 22:55:34,289][__main__][INFO] - agents played in iteration 51 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 22:55:35,065][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.51%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:55:35,091][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.51%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:55:35,117][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.51%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:55:35,140][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.51%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:55:35,141][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 22:55:35,141][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 22:55:35,785][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 22:55:36,236][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 22:55:36,752][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 22:55:37,249][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 22:55:37,747][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 22:55:38,246][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 22:55:38,741][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 22:55:39,246][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 22:55:39,742][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 22:55:40,239][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 22:55:40,741][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-12 22:55:41,244][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-12 22:55:41,741][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-12 22:55:42,236][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-12 22:55:42,731][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-12 22:55:43,240][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-12 22:55:43,740][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-12 22:55:44,241][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-12 22:55:44,772][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-12 22:55:45,272][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-12 22:55:45,774][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-12 22:55:46,275][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-12 22:55:46,779][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-12 22:55:47,279][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-12 22:55:47,780][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-12 22:55:48,279][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-12 22:55:48,778][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-12 22:55:49,279][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-12 22:55:49,779][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-12 22:55:50,276][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-12 22:55:50,774][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-12 22:55:51,267][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-12 22:55:51,763][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-12 22:55:52,264][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 22:55:52,761][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 22:55:53,255][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 22:55:53,767][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 22:55:54,263][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 22:55:54,759][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 22:55:55,253][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 22:55:55,751][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 22:55:56,258][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 22:55:56,754][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 22:55:57,248][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 22:55:57,744][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 22:55:58,242][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 22:55:58,737][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 22:55:59,234][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 22:55:59,731][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 22:56:00,228][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 22:56:00,733][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 22:56:01,228][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 22:56:01,728][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 22:56:02,224][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 22:56:02,719][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 22:56:03,227][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 22:56:03,726][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 22:56:04,223][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 22:56:04,719][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 22:56:05,221][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 22:56:05,723][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 22:56:06,220][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 22:56:06,715][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 22:56:07,212][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 22:56:07,705][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10176 tokens. [2025-11-12 22:56:08,354][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.04%, Current % of VRAM taken: 59.49%, Block Peak % of device VRAM: 62.21%, ΔTime: 00:00:32 [2025-11-12 22:56:09,100][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 22:56:09,102][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 22:56:09,104][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 22:56:10,050][__main__][INFO] - Iteration 52 took 50s (29.23% Gen, 68.89% Train). Generation: 14s, Training: 34s. Estimated remaining time: 41h 20m 16s. Estimated total time: 42h 6m 44s. Time estimates for 10 more iterations: 8m 25s, 100 more iterations: 1h 24m 13s, 500 more iterations: 7h 1m 7s. [2025-11-12 22:56:10,052][__main__][INFO] - Starting iteration 52. [2025-11-12 22:56:10,516][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 5 and human policies 1. [2025-11-12 22:56:10,517][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 22:56:25,058][__main__][INFO] - Number of regex retries in iteration 52: 0 [2025-11-12 22:56:25,058][__main__][INFO] - agents played in iteration 52 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 22:56:25,848][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.54%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:56:25,877][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.54%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:56:25,903][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.54%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:56:25,926][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.54%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:56:25,927][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 22:56:25,927][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 22:56:26,545][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 22:56:26,997][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 22:56:27,499][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 22:56:27,997][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 22:56:28,491][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 22:56:28,987][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 22:56:29,488][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 22:56:29,987][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 22:56:30,486][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 22:56:30,984][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 22:56:31,482][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-12 22:56:42,995][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 22:56:43,489][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 22:56:43,986][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 22:56:44,481][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 22:56:44,977][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 22:56:45,472][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 22:56:45,966][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 22:56:46,464][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 22:56:46,959][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 22:56:47,457][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 22:56:47,953][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 22:56:48,449][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 22:56:48,960][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 22:56:49,455][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 22:56:49,950][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 22:56:50,451][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 22:56:50,946][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 22:56:51,448][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 22:56:51,944][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 22:56:52,440][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 22:56:52,947][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 22:56:53,445][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 22:56:53,946][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 22:56:54,450][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 22:56:54,950][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 22:56:55,447][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 22:56:55,947][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 22:56:56,444][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 22:56:56,964][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 22:56:57,462][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 22:56:57,960][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 22:56:58,457][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10066 tokens. [2025-11-12 22:56:59,092][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.10%, Current % of VRAM taken: 59.55%, Block Peak % of device VRAM: 62.07%, ΔTime: 00:00:32 [2025-11-12 22:56:59,871][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 22:56:59,887][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 22:56:59,891][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 22:57:00,853][__main__][INFO] - Iteration 53 took 50s (28.89% Gen, 69.20% Train). Generation: 14s, Training: 34s. Estimated remaining time: 41h 9m 30s. Estimated total time: 41h 56m 49s. Time estimates for 10 more iterations: 8m 23s, 100 more iterations: 1h 23m 53s, 500 more iterations: 6h 59m 28s. [2025-11-12 22:57:00,855][__main__][INFO] - Starting iteration 53. [2025-11-12 22:57:01,347][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 5 and human policies 1. [2025-11-12 22:57:01,347][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 22:57:15,366][__main__][INFO] - Number of regex retries in iteration 53: 0 [2025-11-12 22:57:15,367][__main__][INFO] - agents played in iteration 53 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 22:57:16,189][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.54%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:57:16,211][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.54%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:57:16,234][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.54%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:57:16,255][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.54%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:57:16,256][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 22:57:16,257][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 22:57:16,906][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 22:57:17,360][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 22:57:17,862][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 22:57:18,357][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 22:57:18,854][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 22:57:19,367][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 22:57:19,869][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 22:57:20,367][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 22:57:20,867][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 22:57:21,367][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 22:57:21,874][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-12 22:57:22,371][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-12 22:57:22,869][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-12 22:57:23,367][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-12 22:57:23,865][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-12 22:57:24,363][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-12 22:57:24,863][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-12 22:57:25,369][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-12 22:57:25,875][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-12 22:57:26,371][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-12 22:57:26,869][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-12 22:57:27,368][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-12 22:57:33,376][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 22:57:33,886][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 22:57:34,385][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 22:57:34,879][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 22:57:35,385][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 22:57:35,880][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 22:57:36,375][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 22:57:36,869][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 22:57:37,365][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 22:57:37,872][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 22:57:38,366][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 22:57:38,861][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 22:57:39,372][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 22:57:39,868][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 22:57:40,362][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 22:57:40,861][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 22:57:41,357][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 22:57:41,857][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 22:57:42,351][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 22:57:42,845][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 22:57:43,345][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 22:57:43,842][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 22:57:44,337][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 22:57:44,844][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 22:57:45,345][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 22:57:45,858][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 22:57:46,356][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 22:57:46,854][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 22:57:47,362][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 22:57:47,859][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 22:57:48,361][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 22:57:48,859][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10037 tokens. [2025-11-12 22:57:49,512][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.07%, Current % of VRAM taken: 59.53%, Block Peak % of device VRAM: 61.91%, ΔTime: 00:00:32 [2025-11-12 22:57:50,274][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 22:57:50,275][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 22:57:50,277][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 22:57:51,232][__main__][INFO] - Iteration 54 took 49s (28.10% Gen, 69.98% Train). Generation: 14s, Training: 34s. Estimated remaining time: 40h 46m 9s. Estimated total time: 41h 34m 18s. Time estimates for 10 more iterations: 8m 18s, 100 more iterations: 1h 23m 8s, 500 more iterations: 6h 55m 43s. [2025-11-12 22:57:51,234][__main__][INFO] - Starting iteration 54. [2025-11-12 22:57:51,730][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 5 and human policies 1. [2025-11-12 22:57:51,731][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 22:58:06,681][__main__][INFO] - Number of regex retries in iteration 54: 0 [2025-11-12 22:58:06,682][__main__][INFO] - agents played in iteration 54 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 22:58:07,498][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.51%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:58:07,520][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.51%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:58:07,542][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.51%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:58:07,565][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.51%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:58:07,565][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 22:58:07,566][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 22:58:08,212][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 22:58:08,668][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 22:58:09,167][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 22:58:09,670][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 22:58:10,167][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 22:58:10,673][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 22:58:11,170][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 22:58:11,666][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 22:58:12,182][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 22:58:12,684][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 22:58:13,183][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-12 22:58:24,691][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 22:58:25,193][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 22:58:25,689][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 22:58:26,190][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 22:58:26,687][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 22:58:27,204][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 22:58:27,703][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 22:58:28,202][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 22:58:28,697][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 22:58:29,205][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 22:58:29,702][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 22:58:30,196][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 22:58:30,692][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 22:58:31,190][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 22:58:31,694][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 22:58:32,191][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 22:58:32,688][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 22:58:33,189][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 22:58:33,688][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 22:58:34,184][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 22:58:34,686][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 22:58:35,185][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 22:58:35,700][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 22:58:36,198][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 22:58:36,694][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 22:58:37,204][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 22:58:37,700][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 22:58:38,196][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 22:58:38,691][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 22:58:39,187][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 22:58:39,695][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 22:58:40,192][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10295 tokens. [2025-11-12 22:58:40,836][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.11%, Current % of VRAM taken: 59.56%, Block Peak % of device VRAM: 62.09%, ΔTime: 00:00:32 [2025-11-12 22:58:41,580][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 22:58:41,582][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 22:58:41,584][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 22:58:42,523][__main__][INFO] - Iteration 55 took 50s (29.43% Gen, 68.72% Train). Generation: 14s, Training: 34s. Estimated remaining time: 41h 30m 37s. Estimated total time: 42h 19m 38s. Time estimates for 10 more iterations: 8m 27s, 100 more iterations: 1h 24m 39s, 500 more iterations: 7h 3m 16s. [2025-11-12 22:58:42,525][__main__][INFO] - Starting iteration 55. [2025-11-12 22:58:43,020][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 5 and human policies 1. [2025-11-12 22:58:43,020][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 22:58:58,157][__main__][INFO] - Number of regex retries in iteration 55: 0 [2025-11-12 22:58:58,157][__main__][INFO] - agents played in iteration 55 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 22:58:59,019][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:58:59,041][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:58:59,064][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:58:59,086][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:58:59,086][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 22:58:59,087][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 22:58:59,720][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 22:59:00,171][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 22:59:00,691][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 22:59:01,187][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 22:59:01,683][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 22:59:02,178][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 22:59:02,673][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 22:59:03,185][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 22:59:03,680][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 22:59:04,179][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 22:59:04,686][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-12 22:59:05,183][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-12 22:59:05,690][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-12 22:59:06,193][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-12 22:59:06,693][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-12 22:59:07,202][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-12 22:59:07,701][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-12 22:59:08,201][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-12 22:59:08,696][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-12 22:59:09,195][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-12 22:59:09,698][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-12 22:59:10,197][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-12 22:59:10,694][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-12 22:59:11,191][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-12 22:59:11,691][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-12 22:59:12,191][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-12 22:59:12,692][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-12 22:59:13,189][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-12 22:59:13,692][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-12 22:59:14,188][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-12 22:59:14,687][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-12 22:59:15,191][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-12 22:59:15,689][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-12 22:59:16,185][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 22:59:16,680][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 22:59:17,175][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 22:59:17,692][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 22:59:18,186][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 22:59:18,683][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 22:59:19,181][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 22:59:19,678][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 22:59:20,181][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 22:59:20,674][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 22:59:21,170][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 22:59:21,665][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 22:59:22,158][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 22:59:22,654][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 22:59:23,148][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 22:59:23,646][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 22:59:24,144][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 22:59:24,638][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 22:59:25,133][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 22:59:25,629][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 22:59:26,124][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 22:59:26,619][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 22:59:27,116][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 22:59:27,615][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 22:59:28,117][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 22:59:28,619][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 22:59:29,115][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 22:59:29,618][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 22:59:30,120][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 22:59:30,617][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 22:59:31,114][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 22:59:31,609][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10059 tokens. [2025-11-12 22:59:32,251][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.09%, Current % of VRAM taken: 59.54%, Block Peak % of device VRAM: 62.09%, ΔTime: 00:00:32 [2025-11-12 22:59:33,000][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 22:59:33,001][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 22:59:33,003][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 22:59:33,964][__main__][INFO] - Iteration 56 took 50s (29.71% Gen, 68.40% Train). Generation: 15s, Training: 34s. Estimated remaining time: 41h 37m 21s. Estimated total time: 42h 27m 13s. Time estimates for 10 more iterations: 8m 29s, 100 more iterations: 1h 24m 54s, 500 more iterations: 7h 4m 32s. [2025-11-12 22:59:33,966][__main__][INFO] - Starting iteration 56. [2025-11-12 22:59:34,442][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 5 and human policies 1. [2025-11-12 22:59:34,443][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 22:59:49,072][__main__][INFO] - Number of regex retries in iteration 56: 0 [2025-11-12 22:59:49,072][__main__][INFO] - agents played in iteration 56 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 22:59:49,922][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.54%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:59:49,947][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.54%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:59:49,971][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.54%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:59:49,993][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.54%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 22:59:49,994][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 22:59:49,994][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 22:59:50,650][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 22:59:51,104][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 22:59:51,610][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 22:59:52,106][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 22:59:52,605][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 22:59:53,113][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 22:59:53,610][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 22:59:54,106][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 22:59:54,604][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 22:59:55,102][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 22:59:55,610][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-12 22:59:56,113][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-12 22:59:56,611][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-12 22:59:57,120][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-12 22:59:57,616][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-12 22:59:58,114][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-12 22:59:58,611][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-12 22:59:59,109][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-12 22:59:59,617][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-12 23:00:00,117][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-12 23:00:00,615][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-12 23:00:01,112][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-12 23:00:01,611][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-12 23:00:02,116][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-12 23:00:02,612][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-12 23:00:03,110][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-12 23:00:03,611][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-12 23:00:04,114][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-12 23:00:04,614][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-12 23:00:05,136][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-12 23:00:05,638][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-12 23:00:06,137][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-12 23:00:06,635][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-12 23:00:07,132][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 23:00:07,635][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 23:00:08,131][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 23:00:08,625][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 23:00:09,121][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 23:00:09,612][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 23:00:10,116][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 23:00:10,610][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 23:00:11,106][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 23:00:11,603][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 23:00:12,097][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 23:00:12,600][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 23:00:13,095][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 23:00:13,588][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 23:00:14,096][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 23:00:14,589][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 23:00:15,086][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 23:00:15,587][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 23:00:16,086][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 23:00:16,583][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 23:00:17,076][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 23:00:17,578][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 23:00:18,077][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 23:00:18,577][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 23:00:19,073][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 23:00:19,572][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 23:00:20,070][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 23:00:20,573][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 23:00:21,072][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 23:00:21,572][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 23:00:22,079][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 23:00:22,577][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10163 tokens. [2025-11-12 23:00:23,218][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.13%, Current % of VRAM taken: 59.58%, Block Peak % of device VRAM: 62.17%, ΔTime: 00:00:32 [2025-11-12 23:00:23,951][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 23:00:23,953][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 23:00:23,955][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 23:00:25,011][__main__][INFO] - Iteration 57 took 50s (28.93% Gen, 68.98% Train). Generation: 14s, Training: 34s. Estimated remaining time: 41h 17m 44s. Estimated total time: 42h 8m 27s. Time estimates for 10 more iterations: 8m 25s, 100 more iterations: 1h 24m 16s, 500 more iterations: 7h 1m 24s. [2025-11-12 23:00:25,013][__main__][INFO] - Starting iteration 57. [2025-11-12 23:00:25,493][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 5 and human policies 1. [2025-11-12 23:00:25,493][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 23:00:39,948][__main__][INFO] - Number of regex retries in iteration 57: 0 [2025-11-12 23:00:39,949][__main__][INFO] - agents played in iteration 57 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 23:00:40,812][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:00:40,840][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:00:40,864][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:00:40,887][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:00:40,888][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 23:00:40,889][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 23:00:41,555][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 23:00:42,014][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 23:00:42,518][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 23:00:43,023][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 23:00:43,522][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 23:00:44,018][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 23:00:44,513][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 23:00:45,009][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 23:00:45,521][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 23:00:46,017][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 23:00:46,512][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-12 23:00:47,008][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-12 23:00:47,503][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-12 23:00:48,009][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-12 23:00:48,505][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-12 23:00:49,002][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-12 23:00:49,498][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-12 23:00:49,994][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-12 23:00:50,490][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-12 23:00:50,993][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-12 23:00:51,489][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-12 23:00:51,998][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-12 23:00:52,493][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-12 23:00:52,992][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-12 23:00:53,499][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-12 23:00:53,996][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-12 23:00:54,495][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-12 23:00:54,995][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-12 23:00:55,492][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-12 23:00:55,995][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-12 23:00:56,491][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-12 23:00:56,988][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-12 23:00:57,496][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-12 23:00:57,992][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 23:00:58,494][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 23:00:58,993][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 23:00:59,491][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 23:01:00,029][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 23:01:00,527][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 23:01:01,024][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 23:01:01,527][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 23:01:02,022][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 23:01:02,515][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 23:01:03,010][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 23:01:03,509][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 23:01:04,015][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 23:01:04,509][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 23:01:05,006][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 23:01:05,524][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 23:01:06,020][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 23:01:06,520][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 23:01:07,016][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 23:01:07,516][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 23:01:08,021][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 23:01:08,520][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 23:01:09,018][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 23:01:09,525][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 23:01:10,024][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 23:01:10,521][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 23:01:11,020][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 23:01:11,514][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 23:01:12,028][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 23:01:12,525][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 23:01:13,025][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 23:01:13,535][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10196 tokens. [2025-11-12 23:01:14,170][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.22%, Current % of VRAM taken: 59.67%, Block Peak % of device VRAM: 62.15%, ΔTime: 00:00:32 [2025-11-12 23:01:14,943][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 23:01:14,945][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 23:01:14,947][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 23:01:16,069][__main__][INFO] - Iteration 58 took 50s (28.58% Gen, 69.20% Train). Generation: 14s, Training: 34s. Estimated remaining time: 41h 17m 17s. Estimated total time: 42h 8m 52s. Time estimates for 10 more iterations: 8m 25s, 100 more iterations: 1h 24m 17s, 500 more iterations: 7h 1m 28s. [2025-11-12 23:01:16,071][__main__][INFO] - Starting iteration 58. [2025-11-12 23:01:16,539][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 5 and human policies 1. [2025-11-12 23:01:16,540][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 23:01:18,212][mllm.models.large_language_model_local][WARNING] - Response Proposal: 5 hats, 5 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 23:01:18,287][mllm.models.large_language_model_local][WARNING] - Response Proposal: 5 hats, 5 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 23:01:19,412][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 23:01:31,478][__main__][INFO] - Number of regex retries in iteration 58: 3 [2025-11-12 23:01:31,479][__main__][INFO] - agents played in iteration 58 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 23:01:32,322][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:01:32,345][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:01:32,367][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:01:32,389][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:01:32,390][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 23:01:32,391][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 23:01:33,062][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 23:01:33,519][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 23:01:34,024][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 23:01:34,525][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 23:01:35,025][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 23:01:35,647][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 23:01:36,143][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 23:01:36,641][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 23:01:37,139][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 23:01:37,636][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 23:01:38,138][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-12 23:01:38,637][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-12 23:01:39,139][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-12 23:01:39,640][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-12 23:01:40,137][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-12 23:01:40,635][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-12 23:01:41,137][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-12 23:01:41,632][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-12 23:01:42,130][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-12 23:01:42,627][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-12 23:01:43,127][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-12 23:01:43,637][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-12 23:01:44,134][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-12 23:01:44,631][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-12 23:01:45,131][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-12 23:01:45,631][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-12 23:01:46,135][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-12 23:01:46,632][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-12 23:01:47,131][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-12 23:01:47,636][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-12 23:01:48,139][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-12 23:01:48,637][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-12 23:01:49,135][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-12 23:01:49,632][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 23:01:50,137][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 23:01:50,638][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 23:01:51,135][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 23:01:51,630][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 23:01:52,126][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 23:01:52,623][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 23:01:53,119][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 23:01:53,614][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 23:01:54,130][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 23:01:54,624][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 23:01:55,122][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 23:01:55,619][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 23:01:56,114][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 23:01:56,616][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 23:01:57,113][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 23:01:57,632][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 23:01:58,138][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 23:01:58,637][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 23:01:59,135][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 23:01:59,637][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 23:02:00,136][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 23:02:00,647][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 23:02:01,149][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 23:02:01,652][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 23:02:02,149][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 23:02:02,647][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 23:02:03,151][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 23:02:03,648][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 23:02:04,148][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 23:02:04,647][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 23:02:05,146][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10327 tokens. [2025-11-12 23:02:05,787][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.17%, Current % of VRAM taken: 59.62%, Block Peak % of device VRAM: 62.13%, ΔTime: 00:00:32 [2025-11-12 23:02:06,531][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 23:02:06,533][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 23:02:06,534][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 23:02:07,448][__main__][INFO] - Iteration 59 took 50s (29.34% Gen, 68.86% Train). Generation: 14s, Training: 35s. Estimated remaining time: 41h 33m 4s. Estimated total time: 42h 25m 29s. Time estimates for 10 more iterations: 8m 29s, 100 more iterations: 1h 24m 50s, 500 more iterations: 7h 4m 14s. [2025-11-12 23:02:07,451][__main__][INFO] - Starting iteration 59. [2025-11-12 23:02:07,916][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 5 and human policies 1. [2025-11-12 23:02:07,917][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 23:02:09,614][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 23:02:09,692][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 23:02:22,707][__main__][INFO] - Number of regex retries in iteration 59: 2 [2025-11-12 23:02:22,708][__main__][INFO] - agents played in iteration 59 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 23:02:23,548][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:02:23,571][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:02:23,594][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:02:23,617][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:02:23,618][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 23:02:23,618][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 23:02:24,298][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 23:02:24,750][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 23:02:25,254][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 23:02:25,752][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 23:02:26,250][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 23:02:26,752][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 23:02:27,248][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 23:02:27,746][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 23:02:28,248][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 23:02:28,747][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 23:02:29,250][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-12 23:02:29,749][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-12 23:02:30,246][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-12 23:02:30,748][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-12 23:02:31,244][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-12 23:02:31,740][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-12 23:02:32,237][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-12 23:02:32,734][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-12 23:02:33,241][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-12 23:02:33,747][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-12 23:02:34,246][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-12 23:02:34,749][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-12 23:02:35,247][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-12 23:02:35,766][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-12 23:02:36,267][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-12 23:02:36,770][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-12 23:02:37,273][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-12 23:02:37,774][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-12 23:02:38,273][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-12 23:02:38,772][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-12 23:02:39,274][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-12 23:02:39,773][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-12 23:02:40,278][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-12 23:02:40,778][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 23:02:41,275][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 23:02:41,775][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 23:02:42,277][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 23:02:42,774][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 23:02:43,268][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 23:02:43,765][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 23:02:44,259][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 23:02:44,757][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 23:02:45,256][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 23:02:45,753][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 23:02:46,256][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 23:02:46,784][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 23:02:47,285][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 23:02:47,786][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 23:02:48,289][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 23:02:48,791][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 23:02:49,299][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 23:02:49,796][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 23:02:50,295][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 23:02:50,797][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 23:02:51,295][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 23:02:51,791][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 23:02:52,288][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 23:02:52,785][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 23:02:53,282][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 23:02:53,782][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 23:02:54,278][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 23:02:54,777][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 23:02:55,275][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 23:02:55,773][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 23:02:56,269][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10284 tokens. [2025-11-12 23:02:56,906][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.16%, Current % of VRAM taken: 59.61%, Block Peak % of device VRAM: 62.22%, ΔTime: 00:00:32 [2025-11-12 23:02:57,816][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 23:02:57,817][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 23:02:57,819][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 23:02:58,755][__main__][INFO] - Iteration 60 took 50s (29.09% Gen, 69.06% Train). Generation: 14s, Training: 35s. Estimated remaining time: 41h 28m 43s. Estimated total time: 42h 22m 0s. Time estimates for 10 more iterations: 8m 28s, 100 more iterations: 1h 24m 44s, 500 more iterations: 7h 3m 40s. [2025-11-12 23:02:58,758][__main__][INFO] - Starting iteration 60. [2025-11-12 23:02:59,221][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 5 and human policies 1. [2025-11-12 23:02:59,222][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 23:03:02,703][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 23:03:13,805][__main__][INFO] - Number of regex retries in iteration 60: 1 [2025-11-12 23:03:13,806][__main__][INFO] - agents played in iteration 60 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 23:03:14,626][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:03:14,651][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:03:14,677][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:03:14,700][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:03:14,700][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 23:03:14,701][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 23:03:15,399][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 23:03:15,861][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 23:03:16,365][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 23:03:16,861][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 23:03:17,369][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 23:03:17,866][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 23:03:18,366][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 23:03:18,864][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 23:03:19,368][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 23:03:19,864][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 23:03:20,360][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-12 23:03:20,854][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-12 23:03:21,356][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-12 23:03:21,852][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-12 23:03:22,351][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-12 23:03:22,846][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-12 23:03:23,348][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-12 23:03:23,853][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-12 23:03:24,352][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-12 23:03:24,854][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-12 23:03:25,357][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-12 23:03:25,854][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-12 23:03:26,351][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-12 23:03:26,851][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-12 23:03:27,371][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-12 23:03:27,870][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-12 23:03:28,366][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-12 23:03:28,861][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-12 23:03:29,361][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-12 23:03:29,855][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-12 23:03:30,353][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-12 23:03:30,856][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-12 23:03:31,351][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-12 23:03:31,859][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 23:03:32,363][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 23:03:32,861][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 23:03:33,360][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 23:03:33,858][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 23:03:34,359][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 23:03:34,854][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 23:03:35,351][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 23:03:35,844][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 23:03:36,338][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 23:03:36,832][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 23:03:37,329][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 23:03:37,822][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 23:03:38,337][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 23:03:38,835][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 23:03:39,332][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 23:03:39,831][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 23:03:40,326][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 23:03:40,823][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 23:03:41,319][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 23:03:41,812][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 23:03:42,313][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 23:03:42,809][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 23:03:43,306][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 23:03:43,803][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 23:03:44,298][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 23:03:44,795][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 23:03:45,290][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 23:03:45,789][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 23:03:46,289][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 23:03:46,785][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 23:03:47,281][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10083 tokens. [2025-11-12 23:03:47,910][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.09%, Current % of VRAM taken: 59.55%, Block Peak % of device VRAM: 62.18%, ΔTime: 00:00:32 [2025-11-12 23:03:48,661][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 23:03:48,664][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 23:03:48,666][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 23:03:50,737][__main__][INFO] - Iteration 61 took 51s (28.31% Gen, 67.67% Train). Generation: 14s, Training: 34s. Estimated remaining time: 42h 1m 39s. Estimated total time: 42h 55m 48s. Time estimates for 10 more iterations: 8m 35s, 100 more iterations: 1h 25m 51s, 500 more iterations: 7h 9m 18s. [2025-11-12 23:03:50,739][__main__][INFO] - Starting iteration 61. [2025-11-12 23:03:51,219][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 6 and human policies 1. [2025-11-12 23:03:51,220][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 23:04:06,618][__main__][INFO] - Number of regex retries in iteration 61: 0 [2025-11-12 23:04:06,619][__main__][INFO] - agents played in iteration 61 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 23:04:07,484][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.53%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:04:07,509][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.53%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:04:07,534][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.53%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:04:07,557][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.53%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:04:07,558][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 23:04:07,559][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 23:04:08,212][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 23:04:08,677][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 23:04:09,184][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 23:04:09,688][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 23:04:10,189][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 23:04:10,692][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 23:04:11,192][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 23:04:11,693][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 23:04:12,191][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 23:04:12,691][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 23:04:13,187][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-12 23:04:13,688][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-12 23:04:14,184][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-12 23:04:14,680][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-12 23:04:15,176][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-12 23:04:15,673][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-12 23:04:16,171][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-12 23:04:16,675][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-12 23:04:17,173][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-12 23:04:17,679][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-12 23:04:18,180][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-12 23:04:18,679][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-12 23:04:19,180][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-12 23:04:19,679][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-12 23:04:20,179][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-12 23:04:20,676][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-12 23:04:21,173][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-12 23:04:21,674][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-12 23:04:22,171][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-12 23:04:22,670][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-12 23:04:23,171][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-12 23:04:23,668][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-12 23:04:24,171][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-12 23:04:24,668][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 23:04:25,168][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 23:04:25,672][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 23:04:26,173][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 23:04:26,672][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 23:04:27,175][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 23:04:27,673][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 23:04:28,168][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 23:04:28,667][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 23:04:29,162][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 23:04:29,659][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 23:04:30,154][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 23:04:30,655][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 23:04:31,153][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 23:04:31,654][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 23:04:32,151][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 23:04:32,653][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 23:04:33,150][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 23:04:33,663][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 23:04:34,162][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 23:04:34,660][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 23:04:35,162][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 23:04:35,660][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 23:04:36,162][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 23:04:36,660][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 23:04:37,155][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 23:04:37,655][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 23:04:38,149][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 23:04:38,648][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 23:04:39,150][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 23:04:39,649][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 23:04:40,168][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10265 tokens. [2025-11-12 23:04:40,830][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.15%, Current % of VRAM taken: 59.61%, Block Peak % of device VRAM: 62.17%, ΔTime: 00:00:32 [2025-11-12 23:04:41,605][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 23:04:41,607][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 23:04:41,609][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 23:04:42,511][__main__][INFO] - Iteration 62 took 51s (30.02% Gen, 68.22% Train). Generation: 15s, Training: 34s. Estimated remaining time: 41h 49m 37s. Estimated total time: 42h 44m 38s. Time estimates for 10 more iterations: 8m 32s, 100 more iterations: 1h 25m 29s, 500 more iterations: 7h 7m 26s. [2025-11-12 23:04:42,513][__main__][INFO] - Starting iteration 62. [2025-11-12 23:04:43,009][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 6 and human policies 1. [2025-11-12 23:04:43,009][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 23:04:51,154][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 23:04:58,332][__main__][INFO] - Number of regex retries in iteration 62: 1 [2025-11-12 23:04:58,333][__main__][INFO] - agents played in iteration 62 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 23:04:59,218][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.54%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:04:59,241][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.54%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:04:59,264][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.54%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:04:59,286][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.54%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:04:59,286][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 23:04:59,288][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 23:04:59,972][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 23:05:00,427][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 23:05:00,933][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 23:05:01,435][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 23:05:01,937][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 23:05:02,441][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 23:05:02,938][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 23:05:03,435][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 23:05:03,947][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 23:05:04,445][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 23:05:04,943][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-12 23:05:05,445][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-12 23:05:05,943][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-12 23:05:06,451][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-12 23:05:06,948][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-12 23:05:07,444][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-12 23:05:07,947][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-12 23:05:08,444][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-12 23:05:08,941][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-12 23:05:09,438][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-12 23:05:09,934][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-12 23:05:10,458][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-12 23:05:10,956][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-12 23:05:11,454][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-12 23:05:11,952][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-12 23:05:12,474][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-12 23:05:12,976][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-12 23:05:13,478][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-12 23:05:13,977][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-12 23:05:14,482][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-12 23:05:14,982][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-12 23:05:15,479][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-12 23:05:15,980][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-12 23:05:16,481][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 23:05:16,984][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 23:05:17,484][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 23:05:17,982][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 23:05:18,482][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 23:05:18,981][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 23:05:19,477][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 23:05:19,974][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 23:05:20,469][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 23:05:20,974][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 23:05:21,471][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 23:05:21,965][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 23:05:22,473][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 23:05:22,968][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 23:05:23,461][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 23:05:23,959][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 23:05:24,456][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 23:05:24,969][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 23:05:25,465][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 23:05:25,960][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 23:05:26,455][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 23:05:26,951][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 23:05:27,459][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 23:05:27,954][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 23:05:28,449][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 23:05:28,949][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 23:05:29,446][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 23:05:29,944][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 23:05:30,449][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 23:05:30,947][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 23:05:31,459][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 23:05:31,959][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10143 tokens. [2025-11-12 23:05:32,602][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.20%, Current % of VRAM taken: 59.65%, Block Peak % of device VRAM: 62.09%, ΔTime: 00:00:32 [2025-11-12 23:05:33,338][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 23:05:33,340][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 23:05:33,341][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 23:05:34,267][__main__][INFO] - Iteration 63 took 51s (29.89% Gen, 68.30% Train). Generation: 15s, Training: 35s. Estimated remaining time: 41h 47m 5s. Estimated total time: 42h 42m 58s. Time estimates for 10 more iterations: 8m 32s, 100 more iterations: 1h 25m 25s, 500 more iterations: 7h 7m 9s. [2025-11-12 23:05:34,270][__main__][INFO] - Starting iteration 63. [2025-11-12 23:05:34,734][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 6 and human policies 1. [2025-11-12 23:05:34,734][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 23:05:49,724][__main__][INFO] - Number of regex retries in iteration 63: 0 [2025-11-12 23:05:49,725][__main__][INFO] - agents played in iteration 63 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 23:05:50,614][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:05:50,644][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:05:50,670][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:05:50,694][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:05:50,695][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 23:05:50,695][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 23:05:51,363][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 23:05:51,823][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 23:05:52,334][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 23:05:52,840][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 23:05:53,339][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 23:05:53,854][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 23:05:54,355][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 23:05:54,852][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 23:05:55,355][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 23:05:55,854][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 23:05:56,362][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-12 23:06:07,871][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 23:06:08,372][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 23:06:08,870][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 23:06:09,369][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 23:06:09,874][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 23:06:10,374][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 23:06:10,888][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 23:06:11,390][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 23:06:11,892][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 23:06:12,394][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 23:06:12,896][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 23:06:13,399][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 23:06:13,898][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 23:06:14,398][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 23:06:14,897][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 23:06:15,396][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 23:06:15,891][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 23:06:16,387][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 23:06:16,881][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 23:06:17,383][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 23:06:17,877][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 23:06:18,370][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 23:06:18,865][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 23:06:19,360][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 23:06:19,868][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 23:06:20,363][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 23:06:20,858][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 23:06:21,358][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 23:06:21,854][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 23:06:22,350][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 23:06:22,849][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 23:06:23,345][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10317 tokens. [2025-11-12 23:06:23,990][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.07%, Current % of VRAM taken: 59.52%, Block Peak % of device VRAM: 62.22%, ΔTime: 00:00:32 [2025-11-12 23:06:24,740][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 23:06:24,742][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 23:06:24,743][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 23:06:25,669][__main__][INFO] - Iteration 64 took 50s (29.43% Gen, 68.75% Train). Generation: 14s, Training: 35s. Estimated remaining time: 41h 30m 1s. Estimated total time: 42h 26m 45s. Time estimates for 10 more iterations: 8m 29s, 100 more iterations: 1h 24m 53s, 500 more iterations: 7h 4m 27s. [2025-11-12 23:06:25,671][__main__][INFO] - Starting iteration 64. [2025-11-12 23:06:26,143][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 6 and human policies 1. [2025-11-12 23:06:26,143][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 23:06:41,151][__main__][INFO] - Number of regex retries in iteration 64: 0 [2025-11-12 23:06:41,151][__main__][INFO] - agents played in iteration 64 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 23:06:41,927][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.53%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:06:41,949][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.53%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:06:41,971][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.53%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:06:41,993][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.53%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:06:41,993][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 23:06:41,994][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 23:06:42,602][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 23:06:43,059][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 23:06:43,557][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 23:06:44,058][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 23:06:44,560][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 23:06:45,075][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 23:06:45,574][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 23:06:46,073][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 23:06:46,580][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 23:06:47,079][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 23:06:47,588][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-12 23:06:48,087][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-12 23:06:48,588][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-12 23:06:49,086][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-12 23:06:49,588][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-12 23:06:50,087][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-12 23:06:50,582][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-12 23:06:51,079][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-12 23:06:51,590][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-12 23:06:52,089][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-12 23:06:52,586][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-12 23:06:53,088][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-12 23:06:59,104][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 23:06:59,605][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 23:07:00,106][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 23:07:00,608][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 23:07:01,108][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 23:07:01,605][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 23:07:02,107][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 23:07:02,614][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 23:07:03,115][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 23:07:03,619][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 23:07:04,119][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 23:07:04,620][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 23:07:05,115][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 23:07:05,616][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 23:07:06,115][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 23:07:06,611][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 23:07:07,110][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 23:07:07,610][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 23:07:08,108][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 23:07:08,606][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 23:07:09,101][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 23:07:09,598][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 23:07:10,104][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 23:07:10,599][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 23:07:11,095][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 23:07:11,606][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 23:07:12,107][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 23:07:12,607][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 23:07:13,101][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 23:07:13,601][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 23:07:14,104][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 23:07:14,600][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10193 tokens. [2025-11-12 23:07:15,245][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.12%, Current % of VRAM taken: 59.58%, Block Peak % of device VRAM: 62.18%, ΔTime: 00:00:32 [2025-11-12 23:07:15,992][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 23:07:15,994][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 23:07:15,997][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 23:07:16,929][__main__][INFO] - Iteration 65 took 50s (29.55% Gen, 68.61% Train). Generation: 15s, Training: 34s. Estimated remaining time: 41h 21m 46s. Estimated total time: 42h 19m 22s. Time estimates for 10 more iterations: 8m 27s, 100 more iterations: 1h 24m 38s, 500 more iterations: 7h 3m 13s. [2025-11-12 23:07:16,931][__main__][INFO] - Starting iteration 65. [2025-11-12 23:07:17,406][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 6 and human policies 1. [2025-11-12 23:07:17,406][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 23:07:27,185][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 0 books, 20 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 23:07:32,754][__main__][INFO] - Number of regex retries in iteration 65: 1 [2025-11-12 23:07:32,755][__main__][INFO] - agents played in iteration 65 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 23:07:33,534][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.53%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:07:33,562][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.53%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:07:33,587][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.53%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:07:33,609][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.53%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:07:33,610][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 23:07:33,611][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 23:07:34,220][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 23:07:34,678][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 23:07:35,182][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 23:07:35,700][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 23:07:36,201][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 23:07:36,705][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 23:07:37,209][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 23:07:37,716][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 23:07:38,224][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 23:07:38,727][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 23:07:39,229][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-12 23:07:50,736][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 23:07:51,235][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 23:07:51,735][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 23:07:52,236][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 23:07:52,738][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 23:07:53,241][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 23:07:53,742][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 23:07:54,245][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 23:07:54,751][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 23:07:55,255][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 23:07:55,760][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 23:07:56,266][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 23:07:56,776][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 23:07:57,280][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 23:07:57,790][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 23:07:58,291][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 23:07:58,786][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 23:07:59,284][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 23:07:59,776][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 23:08:00,275][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 23:08:00,780][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 23:08:01,274][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 23:08:01,781][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 23:08:02,277][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 23:08:02,776][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 23:08:03,283][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 23:08:03,783][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 23:08:04,297][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 23:08:04,795][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 23:08:05,294][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 23:08:05,815][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 23:08:06,314][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10513 tokens. [2025-11-12 23:08:06,977][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.23%, Current % of VRAM taken: 59.68%, Block Peak % of device VRAM: 62.28%, ΔTime: 00:00:32 [2025-11-12 23:08:07,729][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 23:08:07,731][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 23:08:07,733][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 23:08:08,723][__main__][INFO] - Iteration 66 took 51s (29.91% Gen, 68.16% Train). Generation: 15s, Training: 34s. Estimated remaining time: 41h 47m 24s. Estimated total time: 42h 45m 51s. Time estimates for 10 more iterations: 8m 33s, 100 more iterations: 1h 25m 31s, 500 more iterations: 7h 7m 38s. [2025-11-12 23:08:08,725][__main__][INFO] - Starting iteration 66. [2025-11-12 23:08:09,195][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 6 and human policies 1. [2025-11-12 23:08:09,196][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 23:08:23,960][__main__][INFO] - Number of regex retries in iteration 66: 0 [2025-11-12 23:08:23,961][__main__][INFO] - agents played in iteration 66 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 23:08:24,743][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:08:24,779][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:08:24,808][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:08:24,833][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:08:24,834][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 23:08:24,835][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 23:08:25,439][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 23:08:25,893][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 23:08:26,399][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 23:08:26,915][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 23:08:27,413][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 23:08:27,909][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 23:08:28,413][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 23:08:28,920][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 23:08:29,426][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 23:08:29,930][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 23:08:30,434][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-12 23:08:30,935][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-12 23:08:31,435][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-12 23:08:31,935][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-12 23:08:32,439][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-12 23:08:32,939][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-12 23:08:33,438][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-12 23:08:33,937][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-12 23:08:34,437][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-12 23:08:34,939][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-12 23:08:35,439][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-12 23:08:35,936][mllm.training.trainer_common][INFO] - 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Processing mini-batch 43 of 64 [2025-11-12 23:08:47,462][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 23:08:47,970][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 23:08:48,470][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 23:08:48,973][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 23:08:49,488][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 23:08:49,984][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 23:08:50,505][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 23:08:51,001][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 23:08:51,496][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 23:08:51,993][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 23:08:52,488][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 23:08:52,987][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 23:08:53,483][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 23:08:53,981][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 23:08:54,482][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 23:08:54,981][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 23:08:55,481][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 23:08:55,982][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 23:08:56,481][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 23:08:56,979][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 23:08:57,477][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10404 tokens. [2025-11-12 23:08:58,122][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.12%, Current % of VRAM taken: 59.58%, Block Peak % of device VRAM: 62.34%, ΔTime: 00:00:32 [2025-11-12 23:08:58,887][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 23:08:58,888][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 23:08:58,890][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 23:08:59,797][__main__][INFO] - Iteration 67 took 50s (29.18% Gen, 69.03% Train). Generation: 14s, Training: 34s. Estimated remaining time: 41h 10m 49s. Estimated total time: 42h 10m 7s. Time estimates for 10 more iterations: 8m 26s, 100 more iterations: 1h 24m 20s, 500 more iterations: 7h 1m 41s. [2025-11-12 23:08:59,800][__main__][INFO] - Starting iteration 67. [2025-11-12 23:09:00,270][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 6 and human policies 1. [2025-11-12 23:09:00,270][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 23:09:03,103][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 23:09:15,633][__main__][INFO] - Number of regex retries in iteration 67: 1 [2025-11-12 23:09:15,633][__main__][INFO] - agents played in iteration 67 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 23:09:16,408][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.53%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:09:16,433][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.53%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:09:16,457][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.53%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:09:16,479][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.53%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:09:16,479][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 23:09:16,480][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 23:09:17,066][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 23:09:17,520][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 23:09:18,031][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 23:09:18,531][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 23:09:19,032][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 23:09:19,533][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 23:09:20,032][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 23:09:20,540][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 23:09:21,042][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 23:09:21,540][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 23:09:22,039][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-12 23:09:33,535][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 23:09:34,033][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 23:09:34,536][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 23:09:35,039][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 23:09:35,539][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 23:09:36,039][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 23:09:36,542][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 23:09:37,044][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 23:09:37,543][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 23:09:38,046][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 23:09:38,548][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 23:09:39,051][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 23:09:39,551][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 23:09:40,051][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 23:09:40,552][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 23:09:41,051][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 23:09:41,553][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 23:09:42,056][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 23:09:42,559][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 23:09:43,062][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 23:09:43,556][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 23:09:44,055][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 23:09:44,556][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 23:09:45,057][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 23:09:45,556][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 23:09:46,054][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 23:09:46,552][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 23:09:47,051][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 23:09:47,548][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 23:09:48,046][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 23:09:48,544][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 23:09:49,041][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10276 tokens. [2025-11-12 23:09:49,688][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.16%, Current % of VRAM taken: 59.61%, Block Peak % of device VRAM: 62.45%, ΔTime: 00:00:32 [2025-11-12 23:09:50,434][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 23:09:50,435][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 23:09:50,437][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 23:09:51,518][__main__][INFO] - Iteration 68 took 51s (29.98% Gen, 67.91% Train). Generation: 15s, Training: 34s. Estimated remaining time: 41h 42m 17s. Estimated total time: 42h 42m 26s. Time estimates for 10 more iterations: 8m 32s, 100 more iterations: 1h 25m 24s, 500 more iterations: 7h 7m 4s. [2025-11-12 23:09:51,521][__main__][INFO] - Starting iteration 68. [2025-11-12 23:09:52,027][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 6 and human policies 1. [2025-11-12 23:09:52,028][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 23:10:08,286][__main__][INFO] - Number of regex retries in iteration 68: 0 [2025-11-12 23:10:08,287][__main__][INFO] - agents played in iteration 68 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 23:10:09,068][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:10:09,095][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:10:09,122][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:10:09,145][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:10:09,145][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 23:10:09,146][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 23:10:09,754][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 23:10:10,207][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 23:10:10,708][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 23:10:11,209][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 23:10:11,725][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 23:10:12,229][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 23:10:12,733][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 23:10:13,240][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 23:10:13,765][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 23:10:14,272][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 23:10:14,773][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-12 23:10:26,337][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 23:10:26,836][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 23:10:27,341][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 23:10:27,840][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 23:10:28,341][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 23:10:28,845][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 23:10:29,342][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 23:10:29,847][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 23:10:30,347][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 23:10:30,845][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 23:10:31,350][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 23:10:31,850][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 23:10:32,349][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 23:10:32,846][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 23:10:33,344][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 23:10:33,845][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 23:10:34,343][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 23:10:34,839][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 23:10:35,336][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 23:10:35,830][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 23:10:36,328][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 23:10:36,822][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 23:10:37,319][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 23:10:37,837][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 23:10:38,334][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 23:10:38,832][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 23:10:39,332][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 23:10:39,831][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 23:10:40,327][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 23:10:40,826][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 23:10:41,326][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 23:10:41,841][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10397 tokens. [2025-11-12 23:10:42,475][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.17%, Current % of VRAM taken: 59.62%, Block Peak % of device VRAM: 62.13%, ΔTime: 00:00:32 [2025-11-12 23:10:43,219][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 23:10:43,221][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 23:10:43,222][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 23:10:44,134][__main__][INFO] - Iteration 69 took 52s (31.20% Gen, 67.05% Train). Generation: 16s, Training: 34s. Estimated remaining time: 42h 24m 19s. Estimated total time: 43h 25m 21s. Time estimates for 10 more iterations: 8m 41s, 100 more iterations: 1h 26m 50s, 500 more iterations: 7h 14m 13s. [2025-11-12 23:10:44,136][__main__][INFO] - Starting iteration 69. [2025-11-12 23:10:44,631][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 6 and human policies 1. [2025-11-12 23:10:44,632][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 23:11:00,955][__main__][INFO] - Number of regex retries in iteration 69: 0 [2025-11-12 23:11:00,956][__main__][INFO] - agents played in iteration 69 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 23:11:01,804][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.54%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:11:01,832][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.54%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:11:01,858][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.54%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:11:01,881][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.54%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:11:01,882][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 23:11:01,883][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 23:11:02,477][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 23:11:02,936][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 23:11:03,437][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 23:11:03,939][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 23:11:04,443][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 23:11:04,942][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 23:11:05,450][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 23:11:05,949][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 23:11:06,446][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 23:11:06,947][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 23:11:07,453][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-12 23:11:07,954][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-12 23:11:08,456][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-12 23:11:08,954][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-12 23:11:09,454][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-12 23:11:09,955][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-12 23:11:10,455][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-12 23:11:10,959][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-12 23:11:11,454][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-12 23:11:11,956][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-12 23:11:12,459][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-12 23:11:12,958][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-12 23:11:13,458][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-12 23:11:13,958][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-12 23:11:14,454][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-12 23:11:14,952][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-12 23:11:15,450][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-12 23:11:15,949][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-12 23:11:16,444][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-12 23:11:16,941][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-12 23:11:17,439][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-12 23:11:17,934][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-12 23:11:18,436][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-12 23:11:18,938][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 23:11:19,437][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 23:11:19,949][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 23:11:20,474][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 23:11:20,976][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 23:11:21,480][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 23:11:21,979][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 23:11:22,485][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 23:11:22,986][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 23:11:23,487][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 23:11:23,988][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 23:11:24,487][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 23:11:24,983][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 23:11:25,479][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 23:11:25,974][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 23:11:26,473][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 23:11:26,972][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 23:11:27,470][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 23:11:27,971][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 23:11:28,471][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 23:11:28,971][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 23:11:29,468][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 23:11:29,964][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 23:11:30,461][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 23:11:30,959][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 23:11:31,456][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 23:11:31,956][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 23:11:32,451][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 23:11:32,953][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 23:11:33,453][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 23:11:33,952][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 23:11:34,453][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10341 tokens. [2025-11-12 23:11:35,098][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.18%, Current % of VRAM taken: 59.63%, Block Peak % of device VRAM: 62.12%, ΔTime: 00:00:32 [2025-11-12 23:11:35,858][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 23:11:35,860][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 23:11:35,862][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 23:11:36,791][__main__][INFO] - Iteration 70 took 52s (31.29% Gen, 66.92% Train). Generation: 16s, Training: 34s. Estimated remaining time: 42h 26m 7s. Estimated total time: 43h 28m 2s. Time estimates for 10 more iterations: 8m 41s, 100 more iterations: 1h 26m 56s, 500 more iterations: 7h 14m 40s. [2025-11-12 23:11:36,794][__main__][INFO] - Starting iteration 70. [2025-11-12 23:11:37,258][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 6 and human policies 1. [2025-11-12 23:11:37,259][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 23:11:50,338][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 23:11:52,901][__main__][INFO] - Number of regex retries in iteration 70: 1 [2025-11-12 23:11:52,902][__main__][INFO] - agents played in iteration 70 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 23:11:53,744][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:11:53,771][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:11:53,797][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:11:53,819][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:11:53,820][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 23:11:53,820][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 23:11:54,449][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 23:11:54,905][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 23:11:55,423][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 23:11:55,918][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 23:11:56,422][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 23:11:56,944][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 23:11:57,449][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 23:11:57,957][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 23:11:58,457][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 23:11:58,958][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 23:11:59,464][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-12 23:11:59,969][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-12 23:12:00,469][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-12 23:12:00,967][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-12 23:12:01,466][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-12 23:12:01,969][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-12 23:12:02,470][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-12 23:12:02,967][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-12 23:12:03,472][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-12 23:12:03,973][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-12 23:12:04,472][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-12 23:12:04,971][mllm.training.trainer_common][INFO] - 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Processing mini-batch 43 of 64 [2025-11-12 23:12:16,550][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 23:12:17,053][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 23:12:17,560][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 23:12:18,059][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 23:12:18,561][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 23:12:19,057][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 23:12:19,555][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 23:12:20,052][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 23:12:20,553][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 23:12:21,047][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 23:12:21,550][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 23:12:22,051][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 23:12:22,546][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 23:12:23,048][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 23:12:23,548][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 23:12:24,049][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 23:12:24,552][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 23:12:25,049][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 23:12:25,563][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 23:12:26,064][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 23:12:26,563][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10484 tokens. [2025-11-12 23:12:27,204][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.18%, Current % of VRAM taken: 59.63%, Block Peak % of device VRAM: 62.39%, ΔTime: 00:00:32 [2025-11-12 23:12:27,968][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 23:12:27,970][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 23:12:27,973][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 23:12:29,804][__main__][INFO] - Iteration 71 took 52s (29.77% Gen, 66.74% Train). Generation: 15s, Training: 35s. Estimated remaining time: 42h 44m 31s. Estimated total time: 43h 47m 19s. Time estimates for 10 more iterations: 8m 45s, 100 more iterations: 1h 27m 34s, 500 more iterations: 7h 17m 53s. [2025-11-12 23:12:29,807][__main__][INFO] - Starting iteration 71. [2025-11-12 23:12:30,296][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 7 and human policies 1. [2025-11-12 23:12:30,297][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 23:12:38,323][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 23:12:47,165][__main__][INFO] - Number of regex retries in iteration 71: 1 [2025-11-12 23:12:47,166][__main__][INFO] - agents played in iteration 71 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 23:12:48,040][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:12:48,066][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:12:48,092][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:12:48,115][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:12:48,115][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 23:12:48,116][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 23:12:48,758][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 23:12:49,230][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 23:12:49,735][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 23:12:50,239][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 23:12:50,743][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 23:12:51,243][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 23:12:51,751][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 23:12:52,250][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 23:12:52,749][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 23:12:53,271][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 23:12:53,771][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-12 23:13:05,374][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 23:13:05,873][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 23:13:06,378][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 23:13:06,885][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 23:13:07,391][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 23:13:07,895][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 23:13:08,396][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 23:13:08,897][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 23:13:09,398][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 23:13:09,902][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 23:13:10,410][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 23:13:10,912][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 23:13:11,412][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 23:13:11,913][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 23:13:12,412][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 23:13:12,910][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 23:13:13,417][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 23:13:13,916][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 23:13:14,417][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 23:13:14,916][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 23:13:15,416][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 23:13:15,922][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 23:13:16,423][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 23:13:16,925][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 23:13:17,425][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 23:13:17,924][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 23:13:18,430][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 23:13:18,928][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 23:13:19,423][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 23:13:19,918][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 23:13:20,413][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 23:13:20,910][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10464 tokens. [2025-11-12 23:13:21,580][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.11%, Current % of VRAM taken: 59.56%, Block Peak % of device VRAM: 62.25%, ΔTime: 00:00:32 [2025-11-12 23:13:22,338][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 23:13:22,340][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 23:13:22,341][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 23:13:23,375][__main__][INFO] - Iteration 72 took 53s (31.78% Gen, 66.27% Train). Generation: 16s, Training: 35s. Estimated remaining time: 43h 10m 17s. Estimated total time: 44h 13m 59s. Time estimates for 10 more iterations: 8m 50s, 100 more iterations: 1h 28m 27s, 500 more iterations: 7h 22m 19s. [2025-11-12 23:13:23,377][__main__][INFO] - Starting iteration 72. [2025-11-12 23:13:23,850][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 7 and human policies 1. [2025-11-12 23:13:23,850][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 23:13:39,486][__main__][INFO] - Number of regex retries in iteration 72: 0 [2025-11-12 23:13:39,487][__main__][INFO] - agents played in iteration 72 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 23:13:40,281][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:13:40,306][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:13:40,331][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:13:40,353][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:13:40,354][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 23:13:40,354][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 23:13:40,945][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 23:13:41,395][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 23:13:41,898][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 23:13:42,392][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 23:13:42,896][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 23:13:43,394][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 23:13:43,897][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 23:13:44,398][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 23:13:44,897][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 23:13:45,400][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 23:13:45,923][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-12 23:13:46,428][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-12 23:13:46,942][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-12 23:13:47,440][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-12 23:13:47,939][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-12 23:13:48,448][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-12 23:13:48,944][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-12 23:13:49,460][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-12 23:13:49,959][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-12 23:13:50,459][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-12 23:13:50,974][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-12 23:13:51,500][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-12 23:13:57,528][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 23:13:58,033][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 23:13:58,535][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 23:13:59,037][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 23:13:59,543][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 23:14:00,058][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 23:14:00,560][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 23:14:01,061][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 23:14:01,565][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 23:14:02,066][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 23:14:02,574][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 23:14:03,076][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 23:14:03,574][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 23:14:04,080][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 23:14:04,578][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 23:14:05,078][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 23:14:05,574][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 23:14:06,073][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 23:14:06,584][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 23:14:07,080][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 23:14:07,579][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 23:14:08,073][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 23:14:08,571][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 23:14:09,075][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 23:14:09,571][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 23:14:10,070][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 23:14:10,580][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 23:14:11,076][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 23:14:11,576][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 23:14:12,075][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 23:14:12,574][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 23:14:13,080][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10645 tokens. [2025-11-12 23:14:13,716][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.25%, Current % of VRAM taken: 59.70%, Block Peak % of device VRAM: 62.25%, ΔTime: 00:00:32 [2025-11-12 23:14:14,476][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 23:14:14,478][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 23:14:14,480][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 23:14:15,720][__main__][INFO] - Iteration 73 took 51s (30.15% Gen, 67.46% Train). Generation: 15s, Training: 34s. Estimated remaining time: 42h 8m 59s. Estimated total time: 43h 13m 33s. Time estimates for 10 more iterations: 8m 38s, 100 more iterations: 1h 26m 27s, 500 more iterations: 7h 12m 15s. [2025-11-12 23:14:15,722][__main__][INFO] - Starting iteration 73. [2025-11-12 23:14:16,212][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 7 and human policies 1. [2025-11-12 23:14:16,213][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 23:14:18,715][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 23:14:27,024][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 0 balls Given the per-item values, you have a value of 1 for hats, while Alice values them at 10. Conversely, both you and Alice value books and balls at 10. However, to fully exploit the significant value difference for hats, we should propose to take all the hats since you have a substantial advantage in valuing them. Offering 10 hats fills the entire allocation, so there's no risk of exceeding the item quantity, and you don't need to distribute any books or balls since Alice will value them higher and likely take them anyway. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 23:14:33,617][__main__][INFO] - Number of regex retries in iteration 73: 2 [2025-11-12 23:14:33,617][__main__][INFO] - agents played in iteration 73 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 23:14:34,495][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:14:34,527][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:14:34,556][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:14:34,580][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:14:34,581][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 23:14:34,582][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 23:14:35,290][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 23:14:35,760][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 23:14:36,266][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 23:14:36,779][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 23:14:37,281][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 23:14:37,782][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 23:14:38,285][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 23:14:38,786][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 23:14:39,292][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 23:14:39,796][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 23:14:40,295][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-12 23:14:40,799][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-12 23:14:41,296][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-12 23:14:41,797][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-12 23:14:42,293][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-12 23:14:42,791][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-12 23:14:43,295][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-12 23:14:43,823][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-12 23:14:44,325][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-12 23:14:44,826][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-12 23:14:45,327][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-12 23:14:45,827][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-12 23:14:46,328][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-12 23:14:46,829][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-12 23:14:47,327][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-12 23:14:47,827][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-12 23:14:48,329][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-12 23:14:48,831][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-12 23:14:49,331][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-12 23:14:49,833][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-12 23:14:50,334][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-12 23:14:50,832][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-12 23:14:51,337][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-12 23:14:51,841][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 23:14:52,342][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 23:14:52,852][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 23:14:53,355][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 23:14:53,860][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 23:14:54,365][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 23:14:54,867][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 23:14:55,371][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 23:14:55,871][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 23:14:56,370][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 23:14:56,865][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 23:14:57,361][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 23:14:57,857][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 23:14:58,353][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 23:14:58,852][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 23:14:59,349][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 23:14:59,849][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 23:15:00,348][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 23:15:00,847][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 23:15:01,347][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 23:15:01,850][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 23:15:02,348][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 23:15:02,847][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 23:15:03,354][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 23:15:03,853][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 23:15:04,353][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 23:15:04,854][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 23:15:05,356][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 23:15:05,868][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 23:15:06,368][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 23:15:06,870][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 23:15:07,371][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10650 tokens. [2025-11-12 23:15:07,996][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.24%, Current % of VRAM taken: 59.69%, Block Peak % of device VRAM: 62.29%, ΔTime: 00:00:32 [2025-11-12 23:15:08,775][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 23:15:08,777][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 23:15:08,778][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 23:15:09,701][__main__][INFO] - Iteration 74 took 53s (32.54% Gen, 65.73% Train). Generation: 17s, Training: 35s. Estimated remaining time: 43h 29m 0s. Estimated total time: 44h 34m 28s. Time estimates for 10 more iterations: 8m 54s, 100 more iterations: 1h 29m 8s, 500 more iterations: 7h 25m 44s. [2025-11-12 23:15:09,703][__main__][INFO] - Starting iteration 74. [2025-11-12 23:15:10,193][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 7 and human policies 1. [2025-11-12 23:15:10,193][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 23:15:26,091][__main__][INFO] - Number of regex retries in iteration 74: 0 [2025-11-12 23:15:26,091][__main__][INFO] - agents played in iteration 74 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 23:15:26,928][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:15:26,951][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:15:26,973][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:15:26,995][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:15:26,996][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 23:15:26,996][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 23:15:27,651][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 23:15:28,114][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 23:15:28,627][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 23:15:29,134][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 23:15:29,635][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 23:15:30,136][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 23:15:30,641][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 23:15:31,143][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 23:15:31,645][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 23:15:32,150][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 23:15:32,652][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-12 23:15:33,154][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-12 23:15:33,653][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-12 23:15:34,153][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-12 23:15:34,662][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-12 23:15:35,161][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-12 23:15:35,665][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-12 23:15:36,164][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-12 23:15:36,660][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-12 23:15:37,162][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-12 23:15:37,660][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-12 23:15:38,159][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-12 23:15:38,662][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-12 23:15:39,161][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-12 23:15:39,678][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-12 23:15:40,180][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-12 23:15:40,682][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-12 23:15:41,187][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-12 23:15:41,688][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-12 23:15:42,186][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-12 23:15:42,689][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-12 23:15:43,190][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-12 23:15:43,704][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-12 23:15:44,204][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 23:15:44,708][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 23:15:45,210][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 23:15:45,712][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 23:15:46,215][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 23:15:46,717][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 23:15:47,219][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 23:15:47,722][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 23:15:48,223][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 23:15:48,722][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 23:15:49,222][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 23:15:49,721][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 23:15:50,217][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 23:15:50,714][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 23:15:51,213][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 23:15:51,715][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 23:15:52,217][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 23:15:52,717][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 23:15:53,215][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 23:15:53,715][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 23:15:54,215][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 23:15:54,715][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 23:15:55,216][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 23:15:55,715][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 23:15:56,219][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 23:15:56,723][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 23:15:57,223][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 23:15:57,724][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 23:15:58,229][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 23:15:58,732][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 23:15:59,235][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 23:15:59,742][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10710 tokens. [2025-11-12 23:16:00,392][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.16%, Current % of VRAM taken: 59.62%, Block Peak % of device VRAM: 62.27%, ΔTime: 00:00:32 [2025-11-12 23:16:01,174][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 23:16:01,176][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 23:16:01,178][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 23:16:02,105][__main__][INFO] - Iteration 75 took 51s (30.62% Gen, 67.59% Train). Generation: 15s, Training: 35s. Estimated remaining time: 42h 9m 17s. Estimated total time: 43h 15m 38s. Time estimates for 10 more iterations: 8m 39s, 100 more iterations: 1h 26m 31s, 500 more iterations: 7h 12m 36s. [2025-11-12 23:16:02,107][__main__][INFO] - Starting iteration 75. [2025-11-12 23:16:02,577][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 7 and human policies 1. [2025-11-12 23:16:02,578][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 23:16:04,657][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 23:16:18,154][__main__][INFO] - Number of regex retries in iteration 75: 1 [2025-11-12 23:16:18,155][__main__][INFO] - agents played in iteration 75 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 23:16:18,957][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.54%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:16:18,982][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.54%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:16:19,007][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.54%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:16:19,029][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.54%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:16:19,029][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 23:16:19,030][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 23:16:19,621][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 23:16:20,076][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 23:16:20,589][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 23:16:21,096][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 23:16:21,607][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 23:16:22,115][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 23:16:22,623][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 23:16:23,128][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 23:16:23,631][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 23:16:24,132][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 23:16:24,634][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-12 23:16:25,134][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-12 23:16:25,633][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-12 23:16:26,134][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-12 23:16:26,633][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-12 23:16:27,131][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-12 23:16:27,630][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-12 23:16:28,132][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-12 23:16:28,632][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-12 23:16:29,133][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-12 23:16:29,638][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-12 23:16:30,140][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-12 23:16:30,641][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-12 23:16:31,141][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-12 23:16:31,639][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-12 23:16:32,138][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-12 23:16:32,640][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-12 23:16:33,142][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-12 23:16:33,642][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-12 23:16:34,144][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-12 23:16:34,647][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-12 23:16:35,149][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-12 23:16:35,651][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-12 23:16:36,154][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 23:16:36,655][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 23:16:37,157][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 23:16:37,659][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 23:16:38,161][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 23:16:38,660][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 23:16:39,161][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 23:16:39,659][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 23:16:40,173][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 23:16:40,676][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 23:16:41,197][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 23:16:41,697][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 23:16:42,198][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 23:16:42,716][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 23:16:43,215][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 23:16:43,719][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 23:16:44,217][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 23:16:44,717][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 23:16:45,217][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 23:16:45,714][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 23:16:46,212][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 23:16:46,713][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 23:16:47,210][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 23:16:47,706][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 23:16:48,210][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 23:16:48,713][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 23:16:49,219][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 23:16:49,723][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 23:16:50,223][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 23:16:50,721][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 23:16:51,224][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 23:16:51,731][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10619 tokens. [2025-11-12 23:16:52,399][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.29%, Current % of VRAM taken: 59.74%, Block Peak % of device VRAM: 62.38%, ΔTime: 00:00:32 [2025-11-12 23:16:53,168][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 23:16:53,170][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 23:16:53,172][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 23:16:54,087][__main__][INFO] - Iteration 76 took 51s (30.24% Gen, 67.98% Train). Generation: 15s, Training: 35s. Estimated remaining time: 41h 48m 19s. Estimated total time: 42h 55m 32s. Time estimates for 10 more iterations: 8m 35s, 100 more iterations: 1h 25m 51s, 500 more iterations: 7h 9m 15s. [2025-11-12 23:16:54,089][__main__][INFO] - Starting iteration 76. [2025-11-12 23:16:54,578][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 7 and human policies 1. [2025-11-12 23:16:54,579][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 23:17:00,676][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 23:17:10,498][__main__][INFO] - Number of regex retries in iteration 76: 1 [2025-11-12 23:17:10,499][__main__][INFO] - agents played in iteration 76 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 23:17:11,281][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:17:11,303][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:17:11,325][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:17:11,347][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:17:11,347][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 23:17:11,348][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 23:17:11,955][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 23:17:12,409][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 23:17:12,912][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 23:17:13,413][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 23:17:13,916][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 23:17:14,418][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 23:17:14,920][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 23:17:15,423][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 23:17:15,929][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 23:17:16,427][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 23:17:16,926][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-12 23:17:17,429][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-12 23:17:17,930][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-12 23:17:18,431][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-12 23:17:18,929][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-12 23:17:19,426][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-12 23:17:19,927][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-12 23:17:20,427][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-12 23:17:20,930][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-12 23:17:21,430][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-12 23:17:21,934][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-12 23:17:22,434][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-12 23:17:22,933][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-12 23:17:23,436][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-12 23:17:23,936][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-12 23:17:24,434][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-12 23:17:24,935][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-12 23:17:25,435][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-12 23:17:25,938][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-12 23:17:26,440][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-12 23:17:26,943][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-12 23:17:27,442][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-12 23:17:27,944][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-12 23:17:28,453][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 23:17:28,981][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 23:17:29,482][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 23:17:29,990][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 23:17:30,492][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 23:17:31,006][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 23:17:31,506][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 23:17:32,007][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 23:17:32,511][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 23:17:33,015][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 23:17:33,519][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 23:17:34,022][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 23:17:34,521][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 23:17:35,022][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 23:17:35,521][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 23:17:36,016][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 23:17:36,514][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 23:17:37,014][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 23:17:37,512][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 23:17:38,009][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 23:17:38,506][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 23:17:39,017][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 23:17:39,515][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 23:17:40,015][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 23:17:40,517][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 23:17:41,017][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 23:17:41,518][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 23:17:42,016][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 23:17:42,514][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 23:17:43,024][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 23:17:43,529][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 23:17:44,032][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10618 tokens. [2025-11-12 23:17:44,667][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.18%, Current % of VRAM taken: 59.63%, Block Peak % of device VRAM: 62.39%, ΔTime: 00:00:32 [2025-11-12 23:17:45,452][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 23:17:45,454][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 23:17:45,456][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 23:17:46,463][__main__][INFO] - Iteration 77 took 51s (30.68% Gen, 67.37% Train). Generation: 15s, Training: 34s. Estimated remaining time: 42h 6m 11s. Estimated total time: 43h 14m 16s. Time estimates for 10 more iterations: 8m 38s, 100 more iterations: 1h 26m 28s, 500 more iterations: 7h 12m 22s. [2025-11-12 23:17:46,465][__main__][INFO] - Starting iteration 77. [2025-11-12 23:17:46,969][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 7 and human policies 1. [2025-11-12 23:17:46,970][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 23:17:50,909][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 23:18:02,884][__main__][INFO] - Number of regex retries in iteration 77: 1 [2025-11-12 23:18:02,885][__main__][INFO] - agents played in iteration 77 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 23:18:03,723][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:18:03,745][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:18:03,767][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:18:03,789][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:18:03,790][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 23:18:03,791][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 23:18:04,385][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 23:18:04,840][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 23:18:05,345][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 23:18:05,850][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 23:18:06,355][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 23:18:06,863][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 23:18:07,370][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 23:18:07,883][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 23:18:08,386][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 23:18:08,891][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 23:18:09,392][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-12 23:18:09,891][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-12 23:18:10,397][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-12 23:18:10,898][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-12 23:18:11,395][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-12 23:18:11,897][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-12 23:18:12,395][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-12 23:18:12,910][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-12 23:18:13,411][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-12 23:18:13,934][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-12 23:18:14,441][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-12 23:18:14,945][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-12 23:18:15,447][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-12 23:18:15,947][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-12 23:18:16,450][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-12 23:18:16,954][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-12 23:18:17,455][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-12 23:18:17,955][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-12 23:18:18,459][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-12 23:18:18,959][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-12 23:18:19,461][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-12 23:18:19,963][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-12 23:18:20,464][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-12 23:18:20,967][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 23:18:21,469][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 23:18:21,970][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 23:18:22,474][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 23:18:22,976][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 23:18:23,478][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 23:18:23,981][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 23:18:24,483][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 23:18:24,984][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 23:18:25,485][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 23:18:25,985][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 23:18:26,498][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 23:18:27,001][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 23:18:27,516][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 23:18:28,015][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 23:18:28,515][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 23:18:29,031][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 23:18:29,531][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 23:18:30,039][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 23:18:30,540][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 23:18:31,040][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 23:18:31,541][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 23:18:32,041][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 23:18:32,541][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 23:18:33,040][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 23:18:33,538][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 23:18:34,039][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 23:18:34,536][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 23:18:35,036][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 23:18:35,540][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 23:18:36,041][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 23:18:36,541][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10720 tokens. [2025-11-12 23:18:37,168][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.18%, Current % of VRAM taken: 59.64%, Block Peak % of device VRAM: 62.31%, ΔTime: 00:00:32 [2025-11-12 23:18:37,930][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 23:18:37,932][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 23:18:37,933][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 23:18:39,100][__main__][INFO] - Iteration 78 took 52s (30.53% Gen, 67.23% Train). Generation: 15s, Training: 35s. Estimated remaining time: 42h 17m 37s. Estimated total time: 43h 26m 35s. Time estimates for 10 more iterations: 8m 41s, 100 more iterations: 1h 26m 53s, 500 more iterations: 7h 14m 25s. [2025-11-12 23:18:39,102][__main__][INFO] - Starting iteration 78. [2025-11-12 23:18:39,566][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 7 and human policies 1. [2025-11-12 23:18:39,567][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 23:18:41,652][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 23:18:54,071][__main__][INFO] - Number of regex retries in iteration 78: 1 [2025-11-12 23:18:54,071][__main__][INFO] - agents played in iteration 78 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 23:18:54,890][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:18:54,917][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:18:54,943][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:18:54,965][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:18:54,966][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 23:18:54,967][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 23:18:55,577][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 23:18:56,100][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 23:18:56,612][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 23:18:57,115][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 23:18:57,626][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 23:18:58,127][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 23:18:58,630][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 23:18:59,139][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 23:18:59,644][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 23:19:00,149][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 23:19:00,654][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-12 23:19:01,158][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-12 23:19:01,664][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-12 23:19:02,164][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-12 23:19:02,666][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-12 23:19:03,169][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-12 23:19:03,672][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-12 23:19:04,173][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-12 23:19:04,673][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-12 23:19:05,171][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-12 23:19:05,674][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-12 23:19:06,175][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-12 23:19:06,676][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-12 23:19:07,176][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-12 23:19:07,676][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-12 23:19:08,178][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-12 23:19:08,681][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-12 23:19:09,182][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-12 23:19:09,683][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-12 23:19:10,185][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-12 23:19:10,687][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-12 23:19:11,188][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-12 23:19:11,688][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-12 23:19:12,193][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 23:19:12,696][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 23:19:13,198][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 23:19:13,701][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 23:19:14,203][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 23:19:14,727][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 23:19:15,228][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 23:19:15,730][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 23:19:16,232][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 23:19:16,735][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 23:19:17,239][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 23:19:17,739][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 23:19:18,247][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 23:19:18,749][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 23:19:19,252][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 23:19:19,755][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 23:19:20,253][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 23:19:20,751][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 23:19:21,253][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 23:19:21,750][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 23:19:22,248][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 23:19:22,747][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 23:19:23,245][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 23:19:23,743][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 23:19:24,238][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 23:19:24,738][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 23:19:25,244][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 23:19:25,746][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 23:19:26,246][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 23:19:26,743][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 23:19:27,242][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 23:19:27,749][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10818 tokens. [2025-11-12 23:19:28,418][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.23%, Current % of VRAM taken: 59.68%, Block Peak % of device VRAM: 62.39%, ΔTime: 00:00:32 [2025-11-12 23:19:29,213][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 23:19:29,215][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 23:19:29,217][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 23:19:30,421][__main__][INFO] - Iteration 79 took 50s (28.52% Gen, 69.11% Train). Generation: 14s, Training: 35s. Estimated remaining time: 41h 12m 56s. Estimated total time: 42h 22m 45s. Time estimates for 10 more iterations: 8m 28s, 100 more iterations: 1h 24m 45s, 500 more iterations: 7h 3m 47s. [2025-11-12 23:19:30,423][__main__][INFO] - Starting iteration 79. [2025-11-12 23:19:30,909][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 7 and human policies 1. [2025-11-12 23:19:30,910][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 23:19:37,604][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 23:19:40,337][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 23:19:46,599][__main__][INFO] - Number of regex retries in iteration 79: 2 [2025-11-12 23:19:46,600][__main__][INFO] - agents played in iteration 79 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 23:19:47,385][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:19:47,415][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:19:47,443][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:19:47,466][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:19:47,467][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 23:19:47,467][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 23:19:48,059][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 23:19:48,522][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 23:19:49,025][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 23:19:49,526][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 23:19:50,024][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 23:19:50,522][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 23:19:51,022][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 23:19:51,520][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 23:19:52,021][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 23:19:52,537][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 23:19:53,039][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-12 23:19:53,542][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-12 23:19:54,044][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-12 23:19:54,544][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-12 23:19:55,049][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-12 23:19:55,551][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-12 23:19:56,051][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-12 23:19:56,558][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-12 23:19:57,065][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-12 23:19:57,565][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-12 23:19:58,065][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-12 23:19:58,565][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-12 23:19:59,066][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-12 23:19:59,565][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-12 23:20:00,064][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-12 23:20:00,568][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-12 23:20:01,071][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-12 23:20:01,570][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-12 23:20:02,071][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-12 23:20:02,572][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-12 23:20:03,077][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-12 23:20:03,580][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-12 23:20:04,082][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-12 23:20:04,587][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 23:20:05,089][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 23:20:05,588][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 23:20:06,093][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 23:20:06,595][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 23:20:07,094][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 23:20:07,594][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 23:20:08,095][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 23:20:08,596][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 23:20:09,100][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 23:20:09,602][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 23:20:10,112][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 23:20:10,616][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 23:20:11,135][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 23:20:11,639][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 23:20:12,141][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 23:20:12,658][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 23:20:13,159][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 23:20:13,658][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 23:20:14,157][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 23:20:14,659][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 23:20:15,161][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 23:20:15,661][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 23:20:16,161][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 23:20:16,664][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 23:20:17,166][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 23:20:17,663][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 23:20:18,162][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 23:20:18,659][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 23:20:19,163][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 23:20:19,663][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 23:20:20,164][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10769 tokens. [2025-11-12 23:20:20,809][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.17%, Current % of VRAM taken: 59.62%, Block Peak % of device VRAM: 62.28%, ΔTime: 00:00:32 [2025-11-12 23:20:21,577][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 23:20:21,579][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 23:20:21,581][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 23:20:22,493][__main__][INFO] - Iteration 80 took 51s (30.42% Gen, 67.81% Train). Generation: 15s, Training: 34s. Estimated remaining time: 41h 48m 32s. Estimated total time: 42h 59m 13s. Time estimates for 10 more iterations: 8m 35s, 100 more iterations: 1h 25m 58s, 500 more iterations: 7h 9m 52s. [2025-11-12 23:20:22,496][__main__][INFO] - Starting iteration 80. [2025-11-12 23:20:22,992][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 7 and human policies 1. [2025-11-12 23:20:22,992][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 23:20:38,708][__main__][INFO] - Number of regex retries in iteration 80: 0 [2025-11-12 23:20:38,708][__main__][INFO] - agents played in iteration 80 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 23:20:39,572][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:20:39,597][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:20:39,621][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:20:39,643][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:20:39,644][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 23:20:39,644][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 23:20:40,247][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 23:20:40,703][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 23:20:41,205][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 23:20:41,713][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 23:20:42,214][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 23:20:42,713][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 23:20:43,212][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 23:20:43,713][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 23:20:44,215][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 23:20:44,717][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 23:20:45,219][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-12 23:20:45,717][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-12 23:20:46,219][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-12 23:20:46,733][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-12 23:20:47,237][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-12 23:20:47,739][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-12 23:20:48,240][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-12 23:20:48,746][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-12 23:20:49,249][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-12 23:20:49,748][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-12 23:20:50,248][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-12 23:20:50,758][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-12 23:20:51,259][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-12 23:20:51,763][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-12 23:20:52,264][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-12 23:20:52,766][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-12 23:20:53,269][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-12 23:20:53,769][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-12 23:20:54,271][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-12 23:20:54,777][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-12 23:20:55,280][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-12 23:20:55,781][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-12 23:20:56,289][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-12 23:20:56,793][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 23:20:57,297][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 23:20:57,802][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 23:20:58,305][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 23:20:58,813][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 23:20:59,314][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 23:20:59,816][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 23:21:00,328][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 23:21:00,830][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 23:21:01,345][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 23:21:01,846][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 23:21:02,349][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 23:21:02,850][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 23:21:03,350][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 23:21:03,856][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 23:21:04,356][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 23:21:04,860][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 23:21:05,367][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 23:21:05,866][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 23:21:06,367][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 23:21:06,868][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 23:21:07,368][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 23:21:07,872][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 23:21:08,370][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 23:21:08,870][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 23:21:09,365][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 23:21:09,862][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 23:21:10,367][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 23:21:10,864][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 23:21:11,360][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 23:21:11,867][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 23:21:12,364][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10767 tokens. [2025-11-12 23:21:13,053][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.14%, Current % of VRAM taken: 59.59%, Block Peak % of device VRAM: 62.31%, ΔTime: 00:00:32 [2025-11-12 23:21:13,814][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 23:21:13,815][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 23:21:13,817][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 23:21:15,584][__main__][INFO] - Iteration 81 took 52s (29.88% Gen, 66.76% Train). Generation: 15s, Training: 35s. Estimated remaining time: 42h 38m 3s. Estimated total time: 43h 49m 37s. Time estimates for 10 more iterations: 8m 45s, 100 more iterations: 1h 27m 39s, 500 more iterations: 7h 18m 16s. [2025-11-12 23:21:15,586][__main__][INFO] - Starting iteration 81. [2025-11-12 23:21:16,058][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 8 and human policies 1. [2025-11-12 23:21:16,058][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 23:21:25,330][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 23:21:31,486][__main__][INFO] - Number of regex retries in iteration 81: 1 [2025-11-12 23:21:31,486][__main__][INFO] - agents played in iteration 81 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 23:21:32,330][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:21:32,354][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:21:32,379][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:21:32,401][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:21:32,401][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 23:21:32,402][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 23:21:33,005][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 23:21:33,458][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 23:21:33,965][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 23:21:34,462][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 23:21:34,958][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 23:21:35,469][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 23:21:36,050][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 23:21:36,547][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 23:21:37,045][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 23:21:37,547][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 23:21:38,075][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-12 23:21:38,577][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-12 23:21:39,083][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-12 23:21:39,583][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-12 23:21:40,087][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-12 23:21:40,588][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-12 23:21:41,093][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-12 23:21:41,595][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-12 23:21:42,100][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-12 23:21:42,604][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-12 23:21:43,107][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-12 23:21:43,611][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-12 23:21:44,119][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-12 23:21:44,621][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-12 23:21:45,121][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-12 23:21:45,642][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-12 23:21:46,147][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-12 23:21:46,650][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-12 23:21:47,166][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-12 23:21:47,667][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-12 23:21:48,172][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-12 23:21:48,676][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-12 23:21:49,179][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-12 23:21:49,682][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 23:21:50,182][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 23:21:50,683][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 23:21:51,189][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 23:21:51,687][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 23:21:52,186][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 23:21:52,688][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 23:21:53,186][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 23:21:53,687][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 23:21:54,188][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 23:21:54,689][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 23:21:55,192][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 23:21:55,689][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 23:21:56,194][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 23:21:56,696][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 23:21:57,199][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 23:21:57,701][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 23:21:58,200][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 23:21:58,701][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 23:21:59,209][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 23:21:59,712][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 23:22:00,216][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 23:22:00,717][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 23:22:01,218][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 23:22:01,716][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 23:22:02,219][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 23:22:02,719][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 23:22:03,219][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 23:22:03,721][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 23:22:04,226][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 23:22:04,726][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 23:22:05,230][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10706 tokens. [2025-11-12 23:22:05,914][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.26%, Current % of VRAM taken: 59.71%, Block Peak % of device VRAM: 62.37%, ΔTime: 00:00:32 [2025-11-12 23:22:06,718][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 23:22:06,720][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 23:22:06,721][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 23:22:07,625][__main__][INFO] - Iteration 82 took 51s (29.92% Gen, 68.33% Train). Generation: 15s, Training: 35s. Estimated remaining time: 41h 45m 58s. Estimated total time: 42h 58m 24s. Time estimates for 10 more iterations: 8m 35s, 100 more iterations: 1h 25m 56s, 500 more iterations: 7h 9m 44s. [2025-11-12 23:22:07,627][__main__][INFO] - Starting iteration 82. [2025-11-12 23:22:08,121][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 8 and human policies 1. [2025-11-12 23:22:08,121][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 23:22:23,936][__main__][INFO] - Number of regex retries in iteration 82: 0 [2025-11-12 23:22:23,937][__main__][INFO] - agents played in iteration 82 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 23:22:24,712][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:22:24,734][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:22:24,756][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:22:24,778][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:22:24,779][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 23:22:24,780][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 23:22:25,389][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 23:22:25,842][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 23:22:26,344][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 23:22:26,854][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 23:22:27,351][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 23:22:27,852][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 23:22:28,347][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 23:22:28,843][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 23:22:29,341][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 23:22:29,841][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 23:22:30,344][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-12 23:22:30,856][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-12 23:22:31,356][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-12 23:22:31,869][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-12 23:22:32,369][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-12 23:22:32,872][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-12 23:22:33,378][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-12 23:22:33,878][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-12 23:22:34,384][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-12 23:22:34,889][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-12 23:22:35,389][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-12 23:22:35,891][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-12 23:22:36,394][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-12 23:22:36,898][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-12 23:22:37,398][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-12 23:22:37,898][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-12 23:22:38,439][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-12 23:22:38,941][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-12 23:22:39,446][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-12 23:22:39,947][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-12 23:22:40,453][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-12 23:22:40,976][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-12 23:22:41,478][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-12 23:22:41,980][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 23:22:42,483][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 23:22:42,984][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 23:22:43,485][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 23:22:43,982][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 23:22:44,479][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 23:22:44,988][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 23:22:45,489][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 23:22:45,988][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 23:22:46,487][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 23:22:46,986][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 23:22:47,489][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 23:22:47,988][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 23:22:48,486][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 23:22:48,993][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 23:22:49,495][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 23:22:49,996][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 23:22:50,495][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 23:22:50,995][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 23:22:51,502][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 23:22:52,002][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 23:22:52,503][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 23:22:53,006][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 23:22:53,505][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 23:22:54,004][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 23:22:54,502][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 23:22:55,001][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 23:22:55,501][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 23:22:56,001][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 23:22:56,499][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 23:22:56,998][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 23:22:57,501][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10722 tokens. [2025-11-12 23:22:58,179][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.24%, Current % of VRAM taken: 59.69%, Block Peak % of device VRAM: 62.48%, ΔTime: 00:00:32 [2025-11-12 23:22:58,937][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 23:22:58,938][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 23:22:58,940][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 23:22:59,852][__main__][INFO] - Iteration 83 took 51s (30.57% Gen, 67.66% Train). Generation: 15s, Training: 35s. Estimated remaining time: 41h 53m 18s. Estimated total time: 43h 6m 36s. Time estimates for 10 more iterations: 8m 37s, 100 more iterations: 1h 26m 13s, 500 more iterations: 7h 11m 6s. [2025-11-12 23:22:59,854][__main__][INFO] - Starting iteration 83. [2025-11-12 23:23:00,349][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 8 and human policies 1. [2025-11-12 23:23:00,350][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 23:23:03,157][mllm.models.large_language_model_local][WARNING] - Response Proposal: 5 hats, 5 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 23:23:16,425][__main__][INFO] - Number of regex retries in iteration 83: 1 [2025-11-12 23:23:16,425][__main__][INFO] - agents played in iteration 83 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 23:23:17,250][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:23:17,277][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:23:17,303][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:23:17,325][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:23:17,326][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 23:23:17,327][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 23:23:17,936][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 23:23:18,390][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 23:23:18,895][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 23:23:19,392][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 23:23:19,891][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 23:23:20,394][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 23:23:20,894][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 23:23:21,391][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 23:23:21,888][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 23:23:22,392][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 23:23:22,894][mllm.training.trainer_common][INFO] - 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Processing mini-batch 43 of 64 [2025-11-12 23:23:40,014][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 23:23:40,514][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 23:23:41,015][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 23:23:41,518][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 23:23:42,019][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 23:23:42,521][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 23:23:43,023][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 23:23:43,522][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 23:23:44,023][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 23:23:44,528][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 23:23:45,034][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 23:23:45,540][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 23:23:46,046][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 23:23:46,555][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 23:23:47,064][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 23:23:47,571][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 23:23:48,080][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 23:23:48,595][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 23:23:49,095][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 23:23:49,609][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 23:23:50,109][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10846 tokens. [2025-11-12 23:23:50,798][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.26%, Current % of VRAM taken: 59.71%, Block Peak % of device VRAM: 62.39%, ΔTime: 00:00:32 [2025-11-12 23:23:51,555][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 23:23:51,556][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 23:23:51,558][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 23:23:52,483][__main__][INFO] - Iteration 84 took 52s (30.83% Gen, 67.39% Train). Generation: 16s, Training: 35s. Estimated remaining time: 42h 12m 31s. Estimated total time: 43h 26m 41s. Time estimates for 10 more iterations: 8m 41s, 100 more iterations: 1h 26m 53s, 500 more iterations: 7h 14m 26s. [2025-11-12 23:23:52,485][__main__][INFO] - Starting iteration 84. [2025-11-12 23:23:52,952][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 8 and human policies 1. [2025-11-12 23:23:52,953][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 23:23:55,267][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 23:23:57,985][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 23:24:08,073][__main__][INFO] - Number of regex retries in iteration 84: 2 [2025-11-12 23:24:08,074][__main__][INFO] - agents played in iteration 84 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 23:24:08,905][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:24:08,932][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:24:08,959][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:24:08,982][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:24:08,982][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 23:24:08,983][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 23:24:09,590][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 23:24:10,045][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 23:24:10,554][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 23:24:11,054][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 23:24:11,553][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 23:24:12,053][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 23:24:12,552][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 23:24:13,052][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 23:24:13,552][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 23:24:14,052][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 23:24:14,554][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-12 23:24:15,054][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-12 23:24:15,555][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-12 23:24:16,055][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-12 23:24:16,554][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-12 23:24:17,056][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-12 23:24:17,555][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-12 23:24:18,058][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-12 23:24:18,564][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-12 23:24:19,067][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-12 23:24:19,570][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-12 23:24:20,075][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-12 23:24:26,128][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 23:24:26,632][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 23:24:27,136][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 23:24:27,641][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 23:24:28,145][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 23:24:28,649][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 23:24:29,151][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 23:24:29,654][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 23:24:30,155][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 23:24:30,656][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 23:24:31,157][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 23:24:31,658][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 23:24:32,159][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 23:24:32,657][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 23:24:33,159][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 23:24:33,662][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 23:24:34,161][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 23:24:34,659][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 23:24:35,156][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 23:24:35,655][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 23:24:36,155][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 23:24:36,657][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 23:24:37,154][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 23:24:37,652][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 23:24:38,160][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 23:24:38,666][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 23:24:39,168][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 23:24:39,670][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 23:24:40,169][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 23:24:40,671][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 23:24:41,169][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 23:24:41,667][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10757 tokens. [2025-11-12 23:24:42,331][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.15%, Current % of VRAM taken: 59.61%, Block Peak % of device VRAM: 62.31%, ΔTime: 00:00:32 [2025-11-12 23:24:43,108][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 23:24:43,110][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 23:24:43,112][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 23:24:44,249][__main__][INFO] - Iteration 85 took 51s (29.48% Gen, 68.30% Train). Generation: 15s, Training: 35s. Estimated remaining time: 41h 29m 48s. Estimated total time: 42h 44m 50s. Time estimates for 10 more iterations: 8m 32s, 100 more iterations: 1h 25m 29s, 500 more iterations: 7h 7m 28s. [2025-11-12 23:24:44,251][__main__][INFO] - Starting iteration 85. [2025-11-12 23:24:44,729][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 8 and human policies 1. [2025-11-12 23:24:44,730][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 23:24:58,870][__main__][INFO] - Number of regex retries in iteration 85: 0 [2025-11-12 23:24:58,871][__main__][INFO] - agents played in iteration 85 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 23:24:59,642][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:24:59,664][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:24:59,686][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:24:59,707][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:24:59,708][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 23:24:59,708][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 23:25:00,314][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 23:25:00,768][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 23:25:01,269][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 23:25:01,767][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 23:25:02,264][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 23:25:02,759][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 23:25:03,256][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 23:25:03,753][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 23:25:04,256][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 23:25:04,754][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 23:25:05,251][mllm.training.trainer_common][INFO] - 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Processing mini-batch 43 of 64 [2025-11-12 23:25:22,362][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 23:25:22,862][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 23:25:23,361][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 23:25:23,866][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 23:25:24,367][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 23:25:24,866][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 23:25:25,368][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 23:25:25,869][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 23:25:26,372][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 23:25:26,875][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 23:25:27,376][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 23:25:27,878][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 23:25:28,381][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 23:25:28,882][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 23:25:29,392][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 23:25:29,896][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 23:25:30,399][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 23:25:30,900][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 23:25:31,398][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 23:25:31,901][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 23:25:32,401][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10785 tokens. [2025-11-12 23:25:33,111][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.22%, Current % of VRAM taken: 59.68%, Block Peak % of device VRAM: 62.41%, ΔTime: 00:00:32 [2025-11-12 23:25:33,873][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 23:25:33,874][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 23:25:33,876][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 23:25:34,832][__main__][INFO] - Iteration 86 took 50s (28.22% Gen, 69.87% Train). Generation: 14s, Training: 35s. Estimated remaining time: 40h 29m 17s. Estimated total time: 41h 45m 10s. Time estimates for 10 more iterations: 8m 21s, 100 more iterations: 1h 23m 30s, 500 more iterations: 6h 57m 31s. [2025-11-12 23:25:34,835][__main__][INFO] - Starting iteration 86. [2025-11-12 23:25:35,329][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 8 and human policies 1. [2025-11-12 23:25:35,329][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 23:25:40,566][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 23:25:50,515][__main__][INFO] - Number of regex retries in iteration 86: 1 [2025-11-12 23:25:50,516][__main__][INFO] - agents played in iteration 86 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 23:25:51,365][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:25:51,395][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:25:51,423][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:25:51,458][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:25:51,459][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 23:25:51,459][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 23:25:52,068][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 23:25:52,524][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 23:25:53,035][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 23:25:53,534][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 23:25:54,032][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 23:25:54,532][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 23:25:55,036][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 23:25:55,546][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 23:25:56,044][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 23:25:56,544][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 23:25:57,044][mllm.training.trainer_common][INFO] - 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Processing mini-batch 43 of 64 [2025-11-12 23:26:14,110][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 23:26:14,612][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 23:26:15,112][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 23:26:15,615][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 23:26:16,115][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 23:26:16,632][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 23:26:17,131][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 23:26:17,636][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 23:26:18,138][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 23:26:18,640][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 23:26:19,148][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 23:26:19,648][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 23:26:20,148][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 23:26:20,658][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 23:26:21,161][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 23:26:21,663][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 23:26:22,165][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 23:26:22,666][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 23:26:23,169][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 23:26:23,669][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 23:26:24,167][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10772 tokens. [2025-11-12 23:26:24,834][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.20%, Current % of VRAM taken: 59.65%, Block Peak % of device VRAM: 62.26%, ΔTime: 00:00:32 [2025-11-12 23:26:25,642][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 23:26:25,643][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 23:26:25,645][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 23:26:26,565][__main__][INFO] - Iteration 87 took 51s (29.64% Gen, 68.56% Train). Generation: 15s, Training: 35s. Estimated remaining time: 41h 25m 5s. Estimated total time: 42h 41m 50s. Time estimates for 10 more iterations: 8m 32s, 100 more iterations: 1h 25m 23s, 500 more iterations: 7h 6m 58s. [2025-11-12 23:26:26,567][__main__][INFO] - Starting iteration 87. [2025-11-12 23:26:27,062][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 8 and human policies 1. [2025-11-12 23:26:27,063][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 23:26:33,305][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 23:26:40,281][__main__][INFO] - Number of regex retries in iteration 87: 1 [2025-11-12 23:26:40,282][__main__][INFO] - agents played in iteration 87 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 23:26:41,209][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:26:41,236][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:26:41,262][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:26:41,286][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:26:41,287][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 23:26:41,288][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 23:26:41,884][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 23:26:42,342][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 23:26:42,853][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 23:26:43,354][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 23:26:43,854][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 23:26:44,364][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 23:26:44,862][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 23:26:45,368][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 23:26:45,871][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 23:26:46,376][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 23:26:46,878][mllm.training.trainer_common][INFO] - 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Processing mini-batch 43 of 64 [2025-11-12 23:27:03,948][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 23:27:04,449][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 23:27:04,951][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 23:27:05,452][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 23:27:05,953][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 23:27:06,462][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 23:27:06,962][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 23:27:07,463][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 23:27:07,967][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 23:27:08,469][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 23:27:08,979][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 23:27:09,479][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 23:27:09,980][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 23:27:10,484][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 23:27:10,984][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 23:27:11,487][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 23:27:11,989][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 23:27:12,492][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 23:27:12,993][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 23:27:13,495][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 23:27:13,996][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10874 tokens. [2025-11-12 23:27:14,713][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.24%, Current % of VRAM taken: 59.70%, Block Peak % of device VRAM: 62.35%, ΔTime: 00:00:32 [2025-11-12 23:27:15,497][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 23:27:15,498][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 23:27:15,500][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 23:27:16,399][__main__][INFO] - Iteration 88 took 49s (26.79% Gen, 71.38% Train). Generation: 13s, Training: 35s. Estimated remaining time: 39h 49m 17s. Estimated total time: 41h 6m 51s. Time estimates for 10 more iterations: 8m 13s, 100 more iterations: 1h 22m 13s, 500 more iterations: 6h 51m 8s. [2025-11-12 23:27:16,401][__main__][INFO] - Starting iteration 88. [2025-11-12 23:27:16,873][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 8 and human policies 1. [2025-11-12 23:27:16,874][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 23:27:25,774][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 23:27:26,486][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 23:27:29,635][__main__][INFO] - Number of regex retries in iteration 88: 2 [2025-11-12 23:27:29,636][__main__][INFO] - agents played in iteration 88 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 23:27:30,485][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:27:30,512][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:27:30,537][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:27:30,560][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:27:30,560][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 23:27:30,561][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 23:27:31,167][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 23:27:31,625][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 23:27:32,127][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 23:27:32,650][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 23:27:33,148][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 23:27:33,647][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 23:27:34,148][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 23:27:34,647][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 23:27:35,154][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 23:27:35,653][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 23:27:36,152][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-12 23:27:47,663][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 23:27:48,171][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 23:27:48,672][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 23:27:49,174][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 23:27:49,674][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 23:27:50,174][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 23:27:50,679][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 23:27:51,178][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 23:27:51,680][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 23:27:52,185][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 23:27:52,686][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 23:27:53,185][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 23:27:53,687][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 23:27:54,190][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 23:27:54,693][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 23:27:55,195][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 23:27:55,697][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 23:27:56,201][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 23:27:56,704][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 23:27:57,208][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 23:27:57,710][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 23:27:58,214][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 23:27:58,721][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 23:27:59,221][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 23:27:59,724][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 23:28:00,222][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 23:28:00,723][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 23:28:01,235][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 23:28:01,735][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 23:28:02,236][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 23:28:02,741][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 23:28:03,243][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10859 tokens. [2025-11-12 23:28:03,921][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.18%, Current % of VRAM taken: 59.64%, Block Peak % of device VRAM: 62.24%, ΔTime: 00:00:32 [2025-11-12 23:28:04,672][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 23:28:04,674][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 23:28:04,675][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 23:28:05,600][__main__][INFO] - Iteration 89 took 48s (26.19% Gen, 71.91% Train). Generation: 12s, Training: 35s. Estimated remaining time: 39h 17m 59s. Estimated total time: 40h 36m 23s. Time estimates for 10 more iterations: 8m 7s, 100 more iterations: 1h 21m 12s, 500 more iterations: 6h 46m 3s. [2025-11-12 23:28:05,602][__main__][INFO] - Starting iteration 89. [2025-11-12 23:28:06,090][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 8 and human policies 1. [2025-11-12 23:28:06,090][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 23:28:12,911][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 23:28:21,673][__main__][INFO] - Number of regex retries in iteration 89: 1 [2025-11-12 23:28:21,673][__main__][INFO] - agents played in iteration 89 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 23:28:22,515][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:28:22,542][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:28:22,568][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:28:22,590][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:28:22,591][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 23:28:22,592][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 23:28:23,192][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 23:28:23,646][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 23:28:24,147][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 23:28:24,648][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 23:28:25,144][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 23:28:25,645][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 23:28:26,156][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 23:28:26,656][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 23:28:27,155][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 23:28:27,652][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 23:28:28,149][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-12 23:28:28,660][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-12 23:28:29,160][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-12 23:28:29,660][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-12 23:28:30,158][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-12 23:28:30,657][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-12 23:28:31,171][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-12 23:28:31,672][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-12 23:28:32,169][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-12 23:28:32,682][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-12 23:28:33,179][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-12 23:28:33,679][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-12 23:28:39,670][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 23:28:40,167][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 23:28:40,663][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 23:28:41,164][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 23:28:41,681][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 23:28:42,179][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 23:28:42,679][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 23:28:43,179][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 23:28:43,677][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 23:28:44,182][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 23:28:44,684][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 23:28:45,186][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 23:28:45,690][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 23:28:46,193][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 23:28:46,696][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 23:28:47,198][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 23:28:47,700][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 23:28:48,202][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 23:28:48,704][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 23:28:49,204][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 23:28:49,705][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 23:28:50,206][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 23:28:50,708][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 23:28:51,209][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 23:28:51,713][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 23:28:52,216][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 23:28:52,716][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 23:28:53,240][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 23:28:53,744][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 23:28:54,248][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 23:28:54,750][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 23:28:55,252][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10832 tokens. [2025-11-12 23:28:56,032][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.27%, Current % of VRAM taken: 59.72%, Block Peak % of device VRAM: 62.34%, ΔTime: 00:00:32 [2025-11-12 23:28:56,784][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 23:28:56,785][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 23:28:56,787][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 23:28:57,823][__main__][INFO] - Iteration 90 took 51s (30.12% Gen, 67.87% Train). Generation: 15s, Training: 35s. Estimated remaining time: 41h 47m 24s. Estimated total time: 43h 6m 40s. Time estimates for 10 more iterations: 8m 37s, 100 more iterations: 1h 26m 13s, 500 more iterations: 7h 11m 6s. [2025-11-12 23:28:57,825][__main__][INFO] - Starting iteration 90. [2025-11-12 23:28:58,294][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 8 and human policies 1. [2025-11-12 23:28:58,294][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 23:29:12,496][__main__][INFO] - Number of regex retries in iteration 90: 0 [2025-11-12 23:29:12,497][__main__][INFO] - agents played in iteration 90 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 23:29:13,323][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:29:13,347][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:29:13,369][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:29:13,400][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:29:13,401][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 23:29:13,402][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 23:29:14,009][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 23:29:14,467][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 23:29:14,989][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 23:29:15,488][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 23:29:15,991][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 23:29:16,491][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 23:29:16,990][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 23:29:17,494][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 23:29:17,997][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 23:29:18,496][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 23:29:18,997][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-12 23:29:30,500][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 23:29:31,002][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 23:29:31,503][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 23:29:32,006][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 23:29:32,505][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 23:29:33,005][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 23:29:33,509][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 23:29:34,010][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 23:29:34,517][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 23:29:35,017][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 23:29:35,518][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 23:29:36,018][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 23:29:36,521][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 23:29:37,025][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 23:29:37,529][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 23:29:38,035][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 23:29:38,540][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 23:29:39,043][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 23:29:39,543][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 23:29:40,047][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 23:29:40,551][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 23:29:41,050][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 23:29:41,550][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 23:29:42,052][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 23:29:42,560][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 23:29:43,064][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 23:29:43,566][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 23:29:44,079][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 23:29:44,577][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 23:29:45,092][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 23:29:45,596][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 23:29:46,099][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10872 tokens. [2025-11-12 23:29:46,787][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.24%, Current % of VRAM taken: 59.70%, Block Peak % of device VRAM: 62.48%, ΔTime: 00:00:32 [2025-11-12 23:29:47,566][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 23:29:47,568][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 23:29:47,571][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 23:29:49,410][__main__][INFO] - Iteration 91 took 51s (27.78% Gen, 68.62% Train). Generation: 14s, Training: 35s. Estimated remaining time: 41h 15m 43s. Estimated total time: 42h 35m 51s. Time estimates for 10 more iterations: 8m 31s, 100 more iterations: 1h 25m 11s, 500 more iterations: 7h 5m 58s. [2025-11-12 23:29:49,412][__main__][INFO] - Starting iteration 91. [2025-11-12 23:29:49,877][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 9 and human policies 1. [2025-11-12 23:29:49,878][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 23:29:52,858][mllm.models.large_language_model_local][WARNING] - Response Propose: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 23:30:04,131][__main__][INFO] - Number of regex retries in iteration 91: 1 [2025-11-12 23:30:04,132][__main__][INFO] - agents played in iteration 91 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 23:30:04,916][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:30:04,939][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:30:04,963][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:30:04,984][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:30:04,985][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 23:30:04,985][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 23:30:05,584][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 23:30:06,040][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 23:30:06,546][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 23:30:07,046][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 23:30:07,546][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 23:30:08,046][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 23:30:08,546][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 23:30:09,051][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 23:30:09,553][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 23:30:10,053][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 23:30:10,556][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-12 23:30:11,060][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-12 23:30:11,565][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-12 23:30:12,067][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-12 23:30:12,570][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-12 23:30:13,071][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-12 23:30:13,570][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-12 23:30:14,085][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-12 23:30:14,588][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-12 23:30:15,087][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-12 23:30:15,593][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-12 23:30:16,094][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-12 23:30:22,112][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 23:30:22,615][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 23:30:23,115][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 23:30:23,613][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 23:30:24,113][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 23:30:24,617][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 23:30:25,112][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 23:30:25,613][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 23:30:26,109][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 23:30:26,607][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 23:30:27,112][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 23:30:27,616][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 23:30:28,123][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 23:30:28,628][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 23:30:29,135][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 23:30:29,638][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 23:30:30,144][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 23:30:30,650][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 23:30:31,155][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 23:30:31,657][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 23:30:32,159][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 23:30:32,672][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 23:30:33,175][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 23:30:33,690][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 23:30:34,194][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 23:30:34,700][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 23:30:35,205][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 23:30:35,708][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 23:30:36,209][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 23:30:36,712][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 23:30:37,214][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 23:30:37,722][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10814 tokens. [2025-11-12 23:30:38,407][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.30%, Current % of VRAM taken: 59.75%, Block Peak % of device VRAM: 62.41%, ΔTime: 00:00:32 [2025-11-12 23:30:39,201][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 23:30:39,203][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 23:30:39,206][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 23:30:40,306][__main__][INFO] - Iteration 92 took 50s (28.26% Gen, 69.55% Train). Generation: 14s, Training: 35s. Estimated remaining time: 40h 40m 29s. Estimated total time: 42h 1m 27s. Time estimates for 10 more iterations: 8m 24s, 100 more iterations: 1h 24m 2s, 500 more iterations: 7h 0m 14s. [2025-11-12 23:30:40,308][__main__][INFO] - Starting iteration 92. [2025-11-12 23:30:40,786][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 9 and human policies 1. [2025-11-12 23:30:40,787][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 23:30:55,898][__main__][INFO] - Number of regex retries in iteration 92: 0 [2025-11-12 23:30:55,899][__main__][INFO] - agents played in iteration 92 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 23:30:56,676][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:30:56,704][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:30:56,730][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:30:56,754][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:30:56,754][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 23:30:56,755][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 23:30:57,357][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 23:30:57,809][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 23:30:58,317][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 23:30:58,817][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 23:30:59,316][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 23:30:59,813][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 23:31:00,316][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 23:31:00,814][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 23:31:01,310][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 23:31:01,812][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 23:31:02,317][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-12 23:31:13,809][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 23:31:14,313][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 23:31:14,810][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 23:31:15,306][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 23:31:15,805][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 23:31:16,302][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 23:31:16,802][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 23:31:17,296][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 23:31:17,795][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 23:31:18,307][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 23:31:18,800][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 23:31:19,299][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 23:31:19,797][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 23:31:20,295][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 23:31:20,803][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 23:31:21,301][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 23:31:21,803][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 23:31:22,307][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 23:31:22,811][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 23:31:23,314][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 23:31:23,816][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 23:31:24,319][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 23:31:24,824][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 23:31:25,327][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 23:31:25,830][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 23:31:26,331][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 23:31:26,833][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 23:31:27,338][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 23:31:27,840][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 23:31:28,342][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 23:31:28,846][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 23:31:29,348][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10847 tokens. [2025-11-12 23:31:30,031][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.21%, Current % of VRAM taken: 59.66%, Block Peak % of device VRAM: 62.26%, ΔTime: 00:00:32 [2025-11-12 23:31:30,785][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 23:31:30,789][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 23:31:30,792][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 23:31:31,739][__main__][INFO] - Iteration 93 took 50s (29.66% Gen, 68.48% Train). Generation: 15s, Training: 34s. Estimated remaining time: 41h 5m 49s. Estimated total time: 42h 27m 39s. Time estimates for 10 more iterations: 8m 29s, 100 more iterations: 1h 24m 55s, 500 more iterations: 7h 4m 36s. [2025-11-12 23:31:31,741][__main__][INFO] - Starting iteration 93. [2025-11-12 23:31:32,215][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 9 and human policies 1. [2025-11-12 23:31:32,215][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 23:31:38,627][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 23:31:46,973][__main__][INFO] - Number of regex retries in iteration 93: 1 [2025-11-12 23:31:46,973][__main__][INFO] - agents played in iteration 93 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 23:31:47,811][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:31:47,839][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:31:47,863][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:31:47,886][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:31:47,886][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 23:31:47,887][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 23:31:48,490][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 23:31:48,950][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 23:31:49,452][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 23:31:49,956][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 23:31:50,455][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 23:31:50,954][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 23:31:51,457][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 23:31:51,959][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 23:31:52,459][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 23:31:52,957][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 23:31:53,457][mllm.training.trainer_common][INFO] - 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Processing mini-batch 43 of 64 [2025-11-12 23:32:10,418][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 23:32:10,917][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 23:32:11,416][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 23:32:11,914][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 23:32:12,428][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 23:32:12,926][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 23:32:13,423][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 23:32:13,922][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 23:32:14,423][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 23:32:14,933][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 23:32:15,436][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 23:32:15,940][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 23:32:16,451][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 23:32:16,953][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 23:32:17,455][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 23:32:17,957][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 23:32:18,458][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 23:32:18,961][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 23:32:19,462][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 23:32:19,963][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 23:32:20,463][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10877 tokens. [2025-11-12 23:32:21,129][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.17%, Current % of VRAM taken: 59.63%, Block Peak % of device VRAM: 62.32%, ΔTime: 00:00:32 [2025-11-12 23:32:21,905][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 23:32:21,907][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 23:32:21,909][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 23:32:22,875][__main__][INFO] - Iteration 94 took 50s (29.13% Gen, 68.96% Train). Generation: 14s, Training: 34s. Estimated remaining time: 40h 50m 19s. Estimated total time: 42h 13m 1s. Time estimates for 10 more iterations: 8m 26s, 100 more iterations: 1h 24m 26s, 500 more iterations: 7h 2m 10s. [2025-11-12 23:32:22,877][__main__][INFO] - Starting iteration 94. [2025-11-12 23:32:23,344][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 9 and human policies 1. [2025-11-12 23:32:23,344][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 23:32:32,723][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 23:32:37,204][__main__][INFO] - Number of regex retries in iteration 94: 1 [2025-11-12 23:32:37,204][__main__][INFO] - agents played in iteration 94 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 23:32:38,012][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:32:38,039][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:32:38,066][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:32:38,089][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:32:38,089][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 23:32:38,090][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 23:32:38,761][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 23:32:39,217][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 23:32:39,720][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 23:32:40,223][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 23:32:40,721][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 23:32:41,233][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 23:32:41,735][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 23:32:42,235][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 23:32:42,739][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 23:32:43,237][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 23:32:43,736][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-12 23:32:55,234][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 23:32:55,731][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 23:32:56,229][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 23:32:56,727][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 23:32:57,240][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 23:32:57,738][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 23:32:58,234][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 23:32:58,744][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 23:32:59,245][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 23:32:59,747][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 23:33:00,246][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 23:33:00,745][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 23:33:01,249][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 23:33:01,748][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 23:33:02,249][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 23:33:02,747][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 23:33:03,246][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 23:33:03,749][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 23:33:04,248][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 23:33:04,746][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 23:33:05,250][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 23:33:05,752][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 23:33:06,256][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 23:33:06,759][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 23:33:07,263][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 23:33:07,770][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 23:33:08,269][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 23:33:08,772][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 23:33:09,276][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 23:33:09,777][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 23:33:10,281][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 23:33:10,783][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10869 tokens. [2025-11-12 23:33:11,496][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.26%, Current % of VRAM taken: 59.71%, Block Peak % of device VRAM: 62.35%, ΔTime: 00:00:32 [2025-11-12 23:33:12,294][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 23:33:12,297][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 23:33:12,299][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 23:33:13,337][__main__][INFO] - Iteration 95 took 49s (27.72% Gen, 70.20% Train). Generation: 13s, Training: 35s. Estimated remaining time: 40h 16m 10s. Estimated total time: 41h 39m 42s. Time estimates for 10 more iterations: 8m 19s, 100 more iterations: 1h 23m 19s, 500 more iterations: 6h 56m 37s. [2025-11-12 23:33:13,339][__main__][INFO] - Starting iteration 95. [2025-11-12 23:33:13,854][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 9 and human policies 1. [2025-11-12 23:33:13,854][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 23:33:17,672][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 0 books, 30 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 23:33:28,397][__main__][INFO] - Number of regex retries in iteration 95: 1 [2025-11-12 23:33:28,397][__main__][INFO] - agents played in iteration 95 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 23:33:29,201][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:33:29,229][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:33:29,256][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:33:29,279][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:33:29,280][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 23:33:29,280][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 23:33:29,920][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 23:33:30,378][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 23:33:30,884][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 23:33:31,383][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 23:33:31,883][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 23:33:32,381][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 23:33:32,880][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 23:33:33,379][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 23:33:33,876][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 23:33:34,373][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 23:33:34,871][mllm.training.trainer_common][INFO] - 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Processing mini-batch 54 of 64 [2025-11-12 23:33:57,371][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 23:33:57,873][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 23:33:58,376][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 23:33:58,882][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 23:33:59,385][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 23:33:59,891][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 23:34:00,393][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 23:34:00,894][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 23:34:01,394][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 23:34:01,895][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10867 tokens. [2025-11-12 23:34:02,583][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.21%, Current % of VRAM taken: 59.66%, Block Peak % of device VRAM: 62.43%, ΔTime: 00:00:32 [2025-11-12 23:34:03,362][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 23:34:03,366][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 23:34:03,367][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 23:34:04,324][__main__][INFO] - Iteration 96 took 50s (28.81% Gen, 69.29% Train). Generation: 14s, Training: 34s. Estimated remaining time: 40h 39m 8s. Estimated total time: 42h 3m 31s. Time estimates for 10 more iterations: 8m 24s, 100 more iterations: 1h 24m 7s, 500 more iterations: 7h 0m 35s. [2025-11-12 23:34:04,326][__main__][INFO] - Starting iteration 96. [2025-11-12 23:34:04,825][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 9 and human policies 1. [2025-11-12 23:34:04,826][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 23:34:18,066][__main__][INFO] - Number of regex retries in iteration 96: 0 [2025-11-12 23:34:18,067][__main__][INFO] - agents played in iteration 96 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 23:34:18,973][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:34:18,998][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:34:19,023][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:34:19,046][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:34:19,046][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 23:34:19,048][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 23:34:19,674][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 23:34:20,134][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 23:34:20,640][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 23:34:21,149][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 23:34:21,650][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 23:34:22,150][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 23:34:22,658][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 23:34:23,161][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 23:34:23,662][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 23:34:24,159][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 23:34:24,660][mllm.training.trainer_common][INFO] - 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Processing mini-batch 43 of 64 [2025-11-12 23:34:41,673][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 23:34:42,173][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 23:34:42,670][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 23:34:43,168][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 23:34:43,672][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 23:34:44,173][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 23:34:44,675][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 23:34:45,177][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 23:34:45,676][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 23:34:46,176][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 23:34:46,677][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 23:34:47,176][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 23:34:47,680][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 23:34:48,177][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 23:34:48,675][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 23:34:49,176][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 23:34:49,673][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 23:34:50,174][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 23:34:50,674][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 23:34:51,174][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 23:34:51,679][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10873 tokens. [2025-11-12 23:34:52,384][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.17%, Current % of VRAM taken: 59.63%, Block Peak % of device VRAM: 62.34%, ΔTime: 00:00:32 [2025-11-12 23:34:53,177][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 23:34:53,179][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 23:34:53,180][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 23:34:54,062][__main__][INFO] - Iteration 97 took 49s (26.89% Gen, 71.32% Train). Generation: 13s, Training: 35s. Estimated remaining time: 39h 36m 40s. Estimated total time: 41h 1m 53s. Time estimates for 10 more iterations: 8m 12s, 100 more iterations: 1h 22m 3s, 500 more iterations: 6h 50m 18s. [2025-11-12 23:34:54,064][__main__][INFO] - Starting iteration 97. [2025-11-12 23:34:54,544][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 9 and human policies 1. [2025-11-12 23:34:54,545][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 23:35:08,991][__main__][INFO] - Number of regex retries in iteration 97: 0 [2025-11-12 23:35:08,991][__main__][INFO] - agents played in iteration 97 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 23:35:09,855][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:35:09,891][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:35:09,922][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:35:09,949][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:35:09,950][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 23:35:09,951][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 23:35:10,578][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 23:35:11,034][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 23:35:11,550][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 23:35:12,055][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 23:35:12,557][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 23:35:13,059][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 23:35:13,559][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 23:35:14,071][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 23:35:14,574][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 23:35:15,077][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 23:35:15,579][mllm.training.trainer_common][INFO] - 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Processing mini-batch 54 of 64 [2025-11-12 23:35:38,071][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 23:35:38,569][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 23:35:39,072][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 23:35:39,589][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 23:35:40,091][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 23:35:40,588][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 23:35:41,086][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 23:35:41,585][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 23:35:42,089][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 23:35:42,592][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10785 tokens. [2025-11-12 23:35:43,294][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.26%, Current % of VRAM taken: 59.71%, Block Peak % of device VRAM: 62.31%, ΔTime: 00:00:32 [2025-11-12 23:35:44,063][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 23:35:44,064][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 23:35:44,066][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 23:35:44,957][__main__][INFO] - Iteration 98 took 50s (28.66% Gen, 69.58% Train). Generation: 14s, Training: 35s. Estimated remaining time: 40h 34m 36s. Estimated total time: 42h 0m 39s. Time estimates for 10 more iterations: 8m 24s, 100 more iterations: 1h 24m 1s, 500 more iterations: 7h 0m 6s. [2025-11-12 23:35:44,959][__main__][INFO] - Starting iteration 98. [2025-11-12 23:35:45,432][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 9 and human policies 1. [2025-11-12 23:35:45,432][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 23:35:48,182][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 23:35:59,716][__main__][INFO] - Number of regex retries in iteration 98: 1 [2025-11-12 23:35:59,717][__main__][INFO] - agents played in iteration 98 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 23:36:00,603][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:36:00,626][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:36:00,648][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:36:00,671][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:36:00,672][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 23:36:00,672][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 23:36:01,388][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 23:36:01,852][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 23:36:02,354][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 23:36:02,862][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 23:36:03,361][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 23:36:03,864][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 23:36:04,367][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 23:36:04,864][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 23:36:05,362][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 23:36:05,868][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 23:36:06,368][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-12 23:36:17,881][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 23:36:18,379][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 23:36:18,886][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 23:36:19,384][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 23:36:19,886][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 23:36:20,385][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 23:36:20,885][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 23:36:21,392][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 23:36:21,891][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 23:36:22,392][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 23:36:22,890][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 23:36:23,389][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 23:36:23,893][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 23:36:24,390][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 23:36:24,888][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 23:36:25,391][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 23:36:25,890][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 23:36:26,390][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 23:36:26,887][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 23:36:27,386][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 23:36:27,888][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 23:36:28,387][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 23:36:28,887][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 23:36:29,387][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 23:36:29,885][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 23:36:30,383][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 23:36:30,879][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 23:36:31,378][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 23:36:31,884][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 23:36:32,383][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 23:36:32,880][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 23:36:33,379][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10840 tokens. [2025-11-12 23:36:34,098][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.21%, Current % of VRAM taken: 59.66%, Block Peak % of device VRAM: 62.26%, ΔTime: 00:00:32 [2025-11-12 23:36:34,901][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 23:36:34,902][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 23:36:34,904][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 23:36:36,178][__main__][INFO] - Iteration 99 took 50s (28.15% Gen, 69.34% Train). Generation: 14s, Training: 35s. Estimated remaining time: 40h 50m 26s. Estimated total time: 42h 17m 20s. Time estimates for 10 more iterations: 8m 27s, 100 more iterations: 1h 24m 34s, 500 more iterations: 7h 2m 53s. [2025-11-12 23:36:36,180][__main__][INFO] - Starting iteration 99. [2025-11-12 23:36:36,692][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 9 and human policies 1. [2025-11-12 23:36:36,692][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 23:36:39,820][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 23:36:51,211][__main__][INFO] - Number of regex retries in iteration 99: 1 [2025-11-12 23:36:51,212][__main__][INFO] - agents played in iteration 99 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 23:36:52,053][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:36:52,081][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:36:52,107][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:36:52,130][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:36:52,130][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 23:36:52,131][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 23:36:52,761][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 23:36:53,222][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 23:36:53,728][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 23:36:54,235][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 23:36:54,743][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 23:36:55,242][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 23:36:55,742][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 23:36:56,250][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 23:36:56,751][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 23:36:57,252][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 23:36:57,752][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-12 23:37:09,300][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 23:37:09,795][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 23:37:10,293][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 23:37:10,791][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 23:37:11,288][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 23:37:11,791][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 23:37:12,287][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 23:37:12,784][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 23:37:13,287][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 23:37:13,786][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 23:37:14,286][mllm.training.trainer_common][INFO] - 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Processing mini-batch 54 of 64 [2025-11-12 23:37:20,282][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 23:37:20,781][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 23:37:21,281][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 23:37:21,780][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 23:37:22,278][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 23:37:22,777][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 23:37:23,276][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 23:37:23,792][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 23:37:24,291][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 23:37:24,790][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10864 tokens. [2025-11-12 23:37:25,426][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.25%, Current % of VRAM taken: 59.70%, Block Peak % of device VRAM: 62.36%, ΔTime: 00:00:32 [2025-11-12 23:37:26,191][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 23:37:26,193][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 23:37:26,194][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 23:37:27,092][__main__][INFO] - Iteration 100 took 50s (28.81% Gen, 69.41% Train). Generation: 14s, Training: 34s. Estimated remaining time: 40h 32m 15s. Estimated total time: 42h 0m 0s. Time estimates for 10 more iterations: 8m 24s, 100 more iterations: 1h 24m 0s, 500 more iterations: 7h 0m 0s. [2025-11-12 23:37:27,094][__main__][INFO] - Starting iteration 100. [2025-11-12 23:37:27,590][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 9 and human policies 1. [2025-11-12 23:37:27,591][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 23:37:42,096][__main__][INFO] - Number of regex retries in iteration 100: 0 [2025-11-12 23:37:42,097][__main__][INFO] - agents played in iteration 100 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 23:37:42,926][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:37:42,948][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:37:42,971][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:37:42,994][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:37:42,994][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 23:37:42,995][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 23:37:43,664][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 23:37:44,121][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 23:37:44,626][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 23:37:45,130][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 23:37:45,638][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 23:37:46,144][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 23:37:46,647][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 23:37:47,153][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 23:37:47,654][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 23:37:48,158][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 23:37:48,681][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-12 23:37:49,179][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-12 23:37:49,682][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-12 23:37:50,184][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-12 23:37:50,684][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-12 23:37:51,184][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-12 23:37:51,683][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-12 23:37:52,181][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-12 23:37:52,680][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-12 23:37:53,182][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-12 23:37:53,679][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-12 23:37:54,180][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-12 23:37:54,675][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-12 23:37:55,173][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-12 23:37:55,672][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-12 23:37:56,173][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-12 23:37:56,686][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-12 23:37:57,188][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-12 23:37:57,685][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-12 23:37:58,183][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-12 23:37:58,681][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-12 23:37:59,190][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-12 23:37:59,688][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-12 23:38:00,185][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 23:38:00,682][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 23:38:01,180][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 23:38:01,678][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 23:38:02,175][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 23:38:02,673][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 23:38:03,188][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 23:38:03,685][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 23:38:04,182][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 23:38:04,679][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 23:38:05,177][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 23:38:05,685][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 23:38:06,180][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 23:38:06,675][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 23:38:07,186][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 23:38:07,684][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 23:38:08,182][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 23:38:08,680][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 23:38:09,177][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 23:38:09,684][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 23:38:10,181][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 23:38:10,678][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 23:38:11,179][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 23:38:11,676][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 23:38:12,174][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 23:38:12,672][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 23:38:13,169][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 23:38:13,680][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 23:38:14,178][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 23:38:14,677][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 23:38:15,186][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 23:38:15,684][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10831 tokens. [2025-11-12 23:38:16,318][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.23%, Current % of VRAM taken: 59.68%, Block Peak % of device VRAM: 62.25%, ΔTime: 00:00:32 [2025-11-12 23:38:17,080][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 23:38:17,082][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 23:38:17,084][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 23:38:18,861][__main__][INFO] - Iteration 101 took 51s (28.29% Gen, 68.24% Train). Generation: 14s, Training: 34s. Estimated remaining time: 41h 14m 56s. Estimated total time: 42h 43m 33s. Time estimates for 10 more iterations: 8m 32s, 100 more iterations: 1h 25m 27s, 500 more iterations: 7h 7m 15s. [2025-11-12 23:38:18,863][__main__][INFO] - Starting iteration 101. [2025-11-12 23:38:19,335][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 10 and human policies 1. [2025-11-12 23:38:19,336][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 23:38:22,523][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 23:38:28,057][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 23:38:33,367][__main__][INFO] - Number of regex retries in iteration 101: 2 [2025-11-12 23:38:33,368][__main__][INFO] - agents played in iteration 101 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 23:38:34,167][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:38:34,195][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:38:34,223][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:38:34,246][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:38:34,247][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 23:38:34,248][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 23:38:34,894][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 23:38:35,359][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 23:38:35,867][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 23:38:36,377][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 23:38:36,883][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 23:38:37,391][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 23:38:37,902][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 23:38:38,407][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 23:38:38,915][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 23:38:39,416][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 23:38:39,915][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-12 23:38:51,423][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 23:38:51,923][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 23:38:52,422][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 23:38:52,921][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 23:38:53,422][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 23:38:53,920][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 23:38:54,421][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 23:38:54,920][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 23:38:55,419][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 23:38:55,924][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 23:38:56,422][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 23:38:56,922][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 23:38:57,422][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 23:38:57,920][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 23:38:58,421][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 23:38:58,919][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 23:38:59,415][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 23:38:59,921][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 23:39:00,419][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 23:39:00,917][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 23:39:01,417][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 23:39:01,915][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 23:39:02,420][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 23:39:02,918][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 23:39:03,417][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 23:39:03,917][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 23:39:04,417][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 23:39:04,917][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 23:39:05,416][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 23:39:05,914][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 23:39:06,423][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 23:39:06,923][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10854 tokens. [2025-11-12 23:39:07,554][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.29%, Current % of VRAM taken: 59.74%, Block Peak % of device VRAM: 62.52%, ΔTime: 00:00:32 [2025-11-12 23:39:08,316][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 23:39:08,318][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 23:39:08,320][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 23:39:09,283][__main__][INFO] - Iteration 102 took 49s (28.09% Gen, 69.98% Train). Generation: 14s, Training: 34s. Estimated remaining time: 40h 7m 59s. Estimated total time: 41h 37m 27s. Time estimates for 10 more iterations: 8m 19s, 100 more iterations: 1h 23m 14s, 500 more iterations: 6h 56m 14s. [2025-11-12 23:39:09,285][__main__][INFO] - Starting iteration 102. [2025-11-12 23:39:09,764][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 10 and human policies 1. [2025-11-12 23:39:09,764][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 23:39:24,091][__main__][INFO] - Number of regex retries in iteration 102: 0 [2025-11-12 23:39:24,091][__main__][INFO] - agents played in iteration 102 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 23:39:24,879][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.63%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:39:24,906][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.63%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:39:24,932][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.63%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:39:24,954][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.63%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:39:24,955][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 23:39:24,955][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 23:39:25,592][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 23:39:26,059][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 23:39:26,564][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 23:39:27,066][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 23:39:27,570][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 23:39:28,073][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 23:39:28,579][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 23:39:29,084][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 23:39:29,591][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 23:39:30,117][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 23:39:30,621][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-12 23:39:42,120][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 23:39:42,617][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 23:39:43,121][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 23:39:43,620][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 23:39:44,118][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 23:39:44,628][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 23:39:45,124][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 23:39:45,626][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 23:39:46,122][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 23:39:46,620][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 23:39:47,130][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 23:39:47,628][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 23:39:48,129][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 23:39:48,625][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 23:39:49,122][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 23:39:49,624][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 23:39:50,123][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 23:39:50,618][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 23:39:51,123][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 23:39:51,622][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 23:39:52,119][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 23:39:52,629][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 23:39:53,125][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 23:39:53,624][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 23:39:54,123][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 23:39:54,618][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 23:39:55,128][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 23:39:55,626][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 23:39:56,124][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 23:39:56,623][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 23:39:57,122][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 23:39:57,621][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10864 tokens. [2025-11-12 23:39:58,244][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.21%, Current % of VRAM taken: 59.66%, Block Peak % of device VRAM: 62.24%, ΔTime: 00:00:32 [2025-11-12 23:39:59,042][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 23:39:59,043][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 23:39:59,045][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 23:40:00,065][__main__][INFO] - Iteration 103 took 50s (28.48% Gen, 69.49% Train). Generation: 14s, Training: 34s. Estimated remaining time: 40h 24m 46s. Estimated total time: 41h 55m 4s. Time estimates for 10 more iterations: 8m 23s, 100 more iterations: 1h 23m 50s, 500 more iterations: 6h 59m 10s. [2025-11-12 23:40:00,067][__main__][INFO] - Starting iteration 103. [2025-11-12 23:40:00,567][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 10 and human policies 1. [2025-11-12 23:40:00,568][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 23:40:09,861][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 23:40:15,934][__main__][INFO] - Number of regex retries in iteration 103: 1 [2025-11-12 23:40:15,934][__main__][INFO] - agents played in iteration 103 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 23:40:16,754][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:40:16,782][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:40:16,809][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:40:16,832][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:40:16,833][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 23:40:16,834][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 23:40:17,468][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 23:40:17,924][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 23:40:18,428][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 23:40:18,937][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 23:40:19,438][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 23:40:19,939][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 23:40:20,447][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 23:40:20,949][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 23:40:21,452][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 23:40:21,957][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 23:40:22,460][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-12 23:40:22,966][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-12 23:40:23,468][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-12 23:40:23,969][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-12 23:40:24,474][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-12 23:40:24,974][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-12 23:40:25,475][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-12 23:40:25,974][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-12 23:40:26,473][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-12 23:40:26,975][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-12 23:40:27,477][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-12 23:40:27,976][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-12 23:40:28,478][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-12 23:40:28,977][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-12 23:40:29,480][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-12 23:40:29,981][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-12 23:40:30,482][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-12 23:40:30,981][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-12 23:40:31,479][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-12 23:40:31,977][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-12 23:40:32,475][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-12 23:40:32,973][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-12 23:40:33,474][mllm.training.trainer_common][INFO] - 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Processing mini-batch 43 of 64 [2025-11-12 23:40:39,457][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 23:40:39,952][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 23:40:40,449][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 23:40:40,947][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 23:40:41,453][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 23:40:41,952][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 23:40:42,457][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 23:40:42,957][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 23:40:43,455][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 23:40:43,956][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 23:40:44,454][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 23:40:44,974][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 23:40:45,475][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 23:40:45,971][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 23:40:46,468][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 23:40:46,964][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 23:40:47,461][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 23:40:47,961][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 23:40:48,458][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 23:40:48,954][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 23:40:49,451][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10829 tokens. [2025-11-12 23:40:50,075][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.20%, Current % of VRAM taken: 59.65%, Block Peak % of device VRAM: 62.48%, ΔTime: 00:00:32 [2025-11-12 23:40:50,850][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 23:40:50,852][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 23:40:50,853][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 23:40:51,804][__main__][INFO] - Iteration 104 took 51s (29.99% Gen, 68.15% Train). Generation: 15s, Training: 34s. Estimated remaining time: 41h 10m 42s. Estimated total time: 42h 41m 52s. Time estimates for 10 more iterations: 8m 32s, 100 more iterations: 1h 25m 23s, 500 more iterations: 7h 6m 58s. [2025-11-12 23:40:51,806][__main__][INFO] - Starting iteration 104. [2025-11-12 23:40:52,270][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 10 and human policies 1. [2025-11-12 23:40:52,271][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 23:40:55,123][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 23:41:05,993][__main__][INFO] - Number of regex retries in iteration 104: 1 [2025-11-12 23:41:05,994][__main__][INFO] - agents played in iteration 104 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 23:41:06,824][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:41:06,846][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:41:06,869][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:41:06,891][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:41:06,891][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 23:41:06,893][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 23:41:07,571][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 23:41:08,029][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 23:41:08,535][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 23:41:09,049][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 23:41:09,552][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 23:41:10,057][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 23:41:10,556][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 23:41:11,057][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 23:41:11,561][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 23:41:12,061][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 23:41:12,562][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-12 23:41:24,058][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 23:41:24,552][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 23:41:25,049][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 23:41:25,546][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 23:41:26,052][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 23:41:26,551][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 23:41:27,049][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 23:41:27,545][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 23:41:28,045][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 23:41:28,549][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 23:41:29,046][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 23:41:29,543][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 23:41:30,040][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 23:41:30,537][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 23:41:31,037][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 23:41:31,536][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 23:41:32,035][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 23:41:32,552][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 23:41:33,050][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 23:41:33,552][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 23:41:34,058][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 23:41:34,558][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 23:41:35,056][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 23:41:35,554][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 23:41:36,053][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 23:41:36,559][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 23:41:37,060][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 23:41:37,561][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 23:41:38,060][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 23:41:38,561][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 23:41:39,063][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 23:41:39,566][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10853 tokens. [2025-11-12 23:41:40,202][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.26%, Current % of VRAM taken: 59.71%, Block Peak % of device VRAM: 62.42%, ΔTime: 00:00:32 [2025-11-12 23:41:40,963][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 23:41:40,965][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 23:41:40,966][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 23:41:41,907][__main__][INFO] - Iteration 105 took 49s (27.65% Gen, 70.46% Train). Generation: 13s, Training: 34s. Estimated remaining time: 39h 49m 52s. Estimated total time: 41h 21m 52s. Time estimates for 10 more iterations: 8m 16s, 100 more iterations: 1h 22m 43s, 500 more iterations: 6h 53m 38s. [2025-11-12 23:41:41,909][__main__][INFO] - Starting iteration 105. [2025-11-12 23:41:42,392][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 10 and human policies 1. [2025-11-12 23:41:42,393][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 23:41:55,478][__main__][INFO] - Number of regex retries in iteration 105: 0 [2025-11-12 23:41:55,479][__main__][INFO] - agents played in iteration 105 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 23:41:56,395][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:41:56,417][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:41:56,440][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:41:56,463][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:41:56,463][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 23:41:56,464][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 23:41:57,100][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 23:41:57,565][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 23:41:58,073][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 23:41:58,577][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 23:41:59,085][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 23:41:59,588][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 23:42:00,091][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 23:42:00,600][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 23:42:01,103][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 23:42:01,604][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 23:42:02,107][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-12 23:42:02,610][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-12 23:42:03,115][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-12 23:42:03,618][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-12 23:42:04,119][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-12 23:42:04,620][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-12 23:42:05,122][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-12 23:42:05,624][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-12 23:42:06,122][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-12 23:42:06,622][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-12 23:42:07,127][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-12 23:42:07,628][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-12 23:42:13,668][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 23:42:14,170][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 23:42:14,677][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 23:42:15,175][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 23:42:15,674][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 23:42:16,178][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 23:42:16,678][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 23:42:17,178][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 23:42:17,677][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 23:42:18,175][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 23:42:18,672][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 23:42:19,171][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 23:42:19,668][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 23:42:20,176][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 23:42:20,674][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 23:42:21,172][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 23:42:21,670][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 23:42:22,169][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 23:42:22,664][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 23:42:23,162][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 23:42:23,661][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 23:42:24,166][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 23:42:24,664][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 23:42:25,163][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 23:42:25,663][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 23:42:26,160][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 23:42:26,659][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 23:42:27,159][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 23:42:27,658][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 23:42:28,164][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 23:42:28,661][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 23:42:29,159][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10876 tokens. [2025-11-12 23:42:29,794][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.23%, Current % of VRAM taken: 59.68%, Block Peak % of device VRAM: 62.50%, ΔTime: 00:00:32 [2025-11-12 23:42:30,560][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 23:42:30,561][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 23:42:30,563][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 23:42:31,463][__main__][INFO] - Iteration 106 took 49s (26.67% Gen, 71.50% Train). Generation: 13s, Training: 35s. Estimated remaining time: 39h 20m 43s. Estimated total time: 40h 53m 32s. Time estimates for 10 more iterations: 8m 10s, 100 more iterations: 1h 21m 47s, 500 more iterations: 6h 48m 55s. [2025-11-12 23:42:31,465][__main__][INFO] - Starting iteration 106. [2025-11-12 23:42:31,956][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 10 and human policies 1. [2025-11-12 23:42:31,957][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 23:42:43,023][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 23:42:43,719][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 23:42:47,029][__main__][INFO] - Number of regex retries in iteration 106: 2 [2025-11-12 23:42:47,030][__main__][INFO] - agents played in iteration 106 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 23:42:47,879][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:42:47,906][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:42:47,932][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:42:47,955][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:42:47,956][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 23:42:47,957][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 23:42:48,602][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 23:42:49,061][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 23:42:49,569][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 23:42:50,075][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 23:42:50,584][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 23:42:51,086][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 23:42:51,590][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 23:42:52,095][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 23:42:52,597][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 23:42:53,104][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 23:42:53,631][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-12 23:42:54,136][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-12 23:42:54,651][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-12 23:42:55,153][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-12 23:42:55,656][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-12 23:42:56,158][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-12 23:42:56,660][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-12 23:42:57,169][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-12 23:42:57,672][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-12 23:42:58,171][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-12 23:42:58,681][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-12 23:42:59,181][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-12 23:42:59,683][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-12 23:43:00,187][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-12 23:43:00,688][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-12 23:43:01,190][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-12 23:43:01,691][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-12 23:43:02,189][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-12 23:43:02,690][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-12 23:43:03,194][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-12 23:43:03,695][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-12 23:43:04,194][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-12 23:43:04,696][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-12 23:43:05,198][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 23:43:05,701][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 23:43:06,205][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 23:43:06,703][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 23:43:07,202][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 23:43:07,704][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 23:43:08,201][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 23:43:08,700][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 23:43:09,203][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 23:43:09,702][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 23:43:10,202][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 23:43:10,702][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 23:43:11,200][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 23:43:11,699][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 23:43:12,198][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 23:43:12,697][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 23:43:13,194][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 23:43:13,693][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 23:43:14,193][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 23:43:14,693][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 23:43:15,196][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 23:43:15,698][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 23:43:16,197][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 23:43:16,697][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 23:43:17,195][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 23:43:17,694][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 23:43:18,197][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 23:43:18,696][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 23:43:19,196][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 23:43:19,694][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 23:43:20,194][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 23:43:20,695][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10862 tokens. [2025-11-12 23:43:21,319][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.26%, Current % of VRAM taken: 59.72%, Block Peak % of device VRAM: 62.49%, ΔTime: 00:00:32 [2025-11-12 23:43:22,121][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 23:43:22,123][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 23:43:22,125][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 23:43:23,154][__main__][INFO] - Iteration 107 took 51s (29.44% Gen, 68.55% Train). Generation: 15s, Training: 35s. Estimated remaining time: 41h 6m 13s. Estimated total time: 42h 39m 54s. Time estimates for 10 more iterations: 8m 31s, 100 more iterations: 1h 25m 19s, 500 more iterations: 7h 6m 39s. [2025-11-12 23:43:23,156][__main__][INFO] - Starting iteration 107. [2025-11-12 23:43:23,637][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 10 and human policies 1. [2025-11-12 23:43:23,638][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 23:43:27,961][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 23:43:38,202][__main__][INFO] - Number of regex retries in iteration 107: 1 [2025-11-12 23:43:38,203][__main__][INFO] - agents played in iteration 107 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 23:43:39,059][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:43:39,083][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:43:39,107][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:43:39,129][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:43:39,129][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 23:43:39,130][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 23:43:39,798][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 23:43:40,254][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 23:43:40,767][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 23:43:41,271][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 23:43:41,788][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 23:43:42,288][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 23:43:42,789][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 23:43:43,290][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 23:43:43,791][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 23:43:44,295][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 23:43:44,794][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-12 23:43:45,294][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-12 23:43:45,800][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-12 23:43:46,306][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-12 23:43:46,806][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-12 23:43:47,305][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-12 23:43:47,804][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-12 23:43:48,304][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-12 23:43:48,808][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-12 23:43:49,308][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-12 23:43:49,809][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-12 23:43:50,312][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-12 23:43:50,815][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-12 23:43:51,314][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-12 23:43:51,812][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-12 23:43:52,314][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-12 23:43:52,814][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-12 23:43:53,314][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-12 23:43:53,817][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-12 23:43:54,318][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-12 23:43:54,816][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-12 23:43:55,314][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-12 23:43:55,813][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-12 23:43:56,313][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 23:43:56,813][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 23:43:57,311][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 23:43:57,812][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 23:43:58,313][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 23:43:58,811][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 23:43:59,308][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 23:43:59,807][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 23:44:00,304][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 23:44:00,805][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 23:44:01,306][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 23:44:01,805][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 23:44:02,303][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 23:44:02,808][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 23:44:03,307][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 23:44:03,807][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 23:44:04,303][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 23:44:04,802][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 23:44:05,306][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 23:44:05,802][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 23:44:06,300][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 23:44:06,800][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 23:44:07,296][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 23:44:07,800][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 23:44:08,298][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 23:44:08,796][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 23:44:09,294][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 23:44:09,792][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 23:44:10,289][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 23:44:10,786][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 23:44:11,283][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 23:44:11,795][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10866 tokens. [2025-11-12 23:44:12,431][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.13%, Current % of VRAM taken: 59.58%, Block Peak % of device VRAM: 62.30%, ΔTime: 00:00:32 [2025-11-12 23:44:13,206][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 23:44:13,207][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 23:44:13,209][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 23:44:14,152][__main__][INFO] - Iteration 108 took 50s (28.83% Gen, 69.30% Train). Generation: 14s, Training: 35s. Estimated remaining time: 40h 31m 14s. Estimated total time: 42h 5m 47s. Time estimates for 10 more iterations: 8m 25s, 100 more iterations: 1h 24m 11s, 500 more iterations: 7h 0m 57s. [2025-11-12 23:44:14,155][__main__][INFO] - Starting iteration 108. [2025-11-12 23:44:14,648][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 10 and human policies 1. [2025-11-12 23:44:14,649][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 23:44:19,730][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 23:44:25,372][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 23:44:27,252][__main__][INFO] - Number of regex retries in iteration 108: 2 [2025-11-12 23:44:27,253][__main__][INFO] - agents played in iteration 108 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 23:44:28,042][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.52%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:44:28,073][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.52%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:44:28,100][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.52%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:44:28,124][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.52%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:44:28,125][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 23:44:28,126][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 23:44:28,791][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 23:44:29,254][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 23:44:29,760][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 23:44:30,276][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 23:44:30,778][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 23:44:31,292][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 23:44:31,795][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 23:44:32,293][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 23:44:32,797][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 23:44:33,299][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 23:44:33,804][mllm.training.trainer_common][INFO] - 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Processing mini-batch 21 of 64 [2025-11-12 23:44:39,828][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-12 23:44:40,330][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-12 23:44:40,830][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-12 23:44:41,330][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-12 23:44:41,834][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-12 23:44:42,336][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-12 23:44:42,840][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-12 23:44:43,344][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-12 23:44:43,846][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-12 23:44:44,349][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-12 23:44:44,848][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-12 23:44:45,350][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 23:44:45,852][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 23:44:46,353][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 23:44:46,855][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 23:44:47,367][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 23:44:47,868][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 23:44:48,375][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 23:44:48,879][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 23:44:49,383][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 23:44:49,887][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 23:44:50,386][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 23:44:50,888][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 23:44:51,388][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 23:44:51,888][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 23:44:52,401][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 23:44:52,900][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 23:44:53,399][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 23:44:53,898][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 23:44:54,397][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 23:44:54,902][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 23:44:55,400][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 23:44:55,900][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 23:44:56,408][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 23:44:56,906][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 23:44:57,410][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 23:44:57,909][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 23:44:58,409][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 23:44:58,911][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 23:44:59,410][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 23:44:59,907][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 23:45:00,410][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 23:45:00,909][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10877 tokens. [2025-11-12 23:45:01,546][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.23%, Current % of VRAM taken: 59.68%, Block Peak % of device VRAM: 62.20%, ΔTime: 00:00:32 [2025-11-12 23:45:02,309][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 23:45:02,311][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 23:45:02,312][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 23:45:03,356][__main__][INFO] - Iteration 109 took 48s (25.88% Gen, 71.98% Train). Generation: 12s, Training: 35s. Estimated remaining time: 39h 0m 2s. Estimated total time: 40h 35m 24s. Time estimates for 10 more iterations: 8m 7s, 100 more iterations: 1h 21m 10s, 500 more iterations: 6h 45m 54s. [2025-11-12 23:45:03,358][__main__][INFO] - Starting iteration 109. [2025-11-12 23:45:03,857][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 10 and human policies 1. [2025-11-12 23:45:03,857][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 23:45:17,299][__main__][INFO] - Number of regex retries in iteration 109: 0 [2025-11-12 23:45:17,300][__main__][INFO] - agents played in iteration 109 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 23:45:18,209][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:45:18,237][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:45:18,263][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:45:18,286][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:45:18,287][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 23:45:18,288][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 23:45:18,928][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 23:45:19,387][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 23:45:19,911][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 23:45:20,414][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 23:45:20,915][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 23:45:21,423][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 23:45:21,926][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 23:45:22,431][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 23:45:22,935][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 23:45:23,438][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 23:45:23,942][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-12 23:45:24,443][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-12 23:45:24,948][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-12 23:45:25,450][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-12 23:45:25,953][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-12 23:45:26,458][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-12 23:45:26,964][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-12 23:45:27,469][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-12 23:45:27,982][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-12 23:45:28,484][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-12 23:45:28,996][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-12 23:45:29,502][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-12 23:45:35,534][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 23:45:36,034][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 23:45:36,534][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 23:45:37,038][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 23:45:37,538][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 23:45:38,040][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 23:45:38,540][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 23:45:39,042][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 23:45:39,546][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 23:45:40,043][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 23:45:40,539][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 23:45:41,038][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 23:45:41,532][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 23:45:42,027][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 23:45:42,523][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 23:45:43,021][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 23:45:43,525][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 23:45:44,028][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 23:45:44,527][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 23:45:45,024][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 23:45:45,524][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 23:45:46,027][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 23:45:46,524][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 23:45:47,024][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 23:45:47,528][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 23:45:48,026][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 23:45:48,525][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 23:45:49,024][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 23:45:49,522][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 23:45:50,024][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 23:45:50,524][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 23:45:51,023][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10870 tokens. [2025-11-12 23:45:51,661][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.25%, Current % of VRAM taken: 59.71%, Block Peak % of device VRAM: 62.31%, ΔTime: 00:00:32 [2025-11-12 23:45:52,444][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 23:45:52,446][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 23:45:52,448][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 23:45:53,355][__main__][INFO] - Iteration 110 took 49s (27.16% Gen, 71.01% Train). Generation: 13s, Training: 35s. Estimated remaining time: 39h 38m 44s. Estimated total time: 41h 14m 56s. Time estimates for 10 more iterations: 8m 14s, 100 more iterations: 1h 22m 29s, 500 more iterations: 6h 52m 29s. [2025-11-12 23:45:53,357][__main__][INFO] - Starting iteration 110. [2025-11-12 23:45:53,847][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 10 and human policies 1. [2025-11-12 23:45:53,847][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 23:46:08,468][__main__][INFO] - Number of regex retries in iteration 110: 0 [2025-11-12 23:46:08,469][__main__][INFO] - agents played in iteration 110 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 23:46:09,317][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:46:09,345][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:46:09,372][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:46:09,396][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:46:09,396][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 23:46:09,398][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 23:46:10,056][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 23:46:10,516][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 23:46:11,033][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 23:46:11,533][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 23:46:12,034][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 23:46:12,539][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 23:46:13,043][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 23:46:13,546][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 23:46:14,051][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 23:46:14,556][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 23:46:15,058][mllm.training.trainer_common][INFO] - 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Processing mini-batch 21 of 64 [2025-11-12 23:46:21,089][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-12 23:46:21,602][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-12 23:46:22,103][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-12 23:46:22,606][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-12 23:46:23,113][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-12 23:46:23,617][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-12 23:46:24,128][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-12 23:46:24,632][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-12 23:46:25,135][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-12 23:46:25,637][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-12 23:46:26,140][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-12 23:46:26,643][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 23:46:27,148][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 23:46:27,650][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 23:46:28,156][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 23:46:28,657][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 23:46:29,161][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 23:46:29,660][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 23:46:30,160][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 23:46:30,664][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 23:46:31,166][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 23:46:31,668][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 23:46:32,171][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 23:46:32,670][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 23:46:33,170][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 23:46:33,669][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 23:46:34,167][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 23:46:34,667][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 23:46:35,166][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 23:46:35,664][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 23:46:36,164][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 23:46:36,661][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 23:46:37,159][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 23:46:37,657][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 23:46:38,152][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 23:46:38,651][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 23:46:39,149][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 23:46:39,647][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 23:46:40,145][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 23:46:40,647][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 23:46:41,148][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 23:46:41,645][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 23:46:42,144][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10870 tokens. [2025-11-12 23:46:42,783][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.20%, Current % of VRAM taken: 59.65%, Block Peak % of device VRAM: 62.41%, ΔTime: 00:00:32 [2025-11-12 23:46:43,574][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 23:46:43,576][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 23:46:43,578][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 23:46:45,328][__main__][INFO] - Iteration 111 took 51s (28.40% Gen, 68.20% Train). Generation: 14s, Training: 35s. Estimated remaining time: 41h 17m 2s. Estimated total time: 42h 54m 5s. Time estimates for 10 more iterations: 8m 34s, 100 more iterations: 1h 25m 48s, 500 more iterations: 7h 9m 0s. [2025-11-12 23:46:45,330][__main__][INFO] - Starting iteration 111. [2025-11-12 23:46:45,796][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 11 and human policies 1. [2025-11-12 23:46:45,797][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 23:46:59,721][__main__][INFO] - Number of regex retries in iteration 111: 0 [2025-11-12 23:46:59,722][__main__][INFO] - agents played in iteration 111 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 23:47:00,524][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:47:00,548][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:47:00,570][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:47:00,592][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:47:00,594][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 23:47:00,594][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 23:47:01,276][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 23:47:01,739][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 23:47:02,246][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 23:47:02,750][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 23:47:03,255][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 23:47:03,754][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 23:47:04,256][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 23:47:04,758][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 23:47:05,266][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 23:47:05,768][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 23:47:06,271][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-12 23:47:06,773][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-12 23:47:07,275][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-12 23:47:07,775][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-12 23:47:08,276][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-12 23:47:08,780][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-12 23:47:09,285][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-12 23:47:09,786][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-12 23:47:10,292][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-12 23:47:10,792][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-12 23:47:11,289][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-12 23:47:11,789][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-12 23:47:17,861][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 23:47:18,360][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 23:47:18,861][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 23:47:19,365][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 23:47:19,866][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 23:47:20,368][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 23:47:20,871][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 23:47:21,370][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 23:47:21,879][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 23:47:22,380][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 23:47:22,880][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 23:47:23,380][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 23:47:23,880][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 23:47:24,377][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 23:47:24,879][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 23:47:25,380][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 23:47:25,882][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 23:47:26,383][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 23:47:26,882][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 23:47:27,380][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 23:47:27,878][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 23:47:28,380][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 23:47:28,879][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 23:47:29,378][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 23:47:29,877][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 23:47:30,374][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 23:47:30,873][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 23:47:31,369][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 23:47:31,866][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 23:47:32,364][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 23:47:32,860][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 23:47:33,358][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10854 tokens. [2025-11-12 23:47:33,994][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.23%, Current % of VRAM taken: 59.68%, Block Peak % of device VRAM: 62.48%, ΔTime: 00:00:32 [2025-11-12 23:47:34,777][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 23:47:34,778][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 23:47:34,780][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 23:47:35,715][__main__][INFO] - Iteration 112 took 49s (27.89% Gen, 70.23% Train). Generation: 13s, Training: 35s. Estimated remaining time: 39h 58m 3s. Estimated total time: 41h 35m 57s. Time estimates for 10 more iterations: 8m 19s, 100 more iterations: 1h 23m 11s, 500 more iterations: 6h 55m 59s. [2025-11-12 23:47:35,717][__main__][INFO] - Starting iteration 112. [2025-11-12 23:47:36,216][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 11 and human policies 1. [2025-11-12 23:47:36,217][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 23:47:39,859][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 23:47:52,537][__main__][INFO] - Number of regex retries in iteration 112: 1 [2025-11-12 23:47:52,538][__main__][INFO] - agents played in iteration 112 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 23:47:53,362][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:47:53,391][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:47:53,414][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:47:53,436][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:47:53,437][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 23:47:53,437][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 23:47:54,137][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 23:47:54,601][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 23:47:55,108][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 23:47:55,609][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 23:47:56,117][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 23:47:56,619][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 23:47:57,145][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 23:47:57,650][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 23:47:58,156][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 23:47:58,660][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 23:47:59,162][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-12 23:48:10,746][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 23:48:11,247][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 23:48:11,750][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 23:48:12,250][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 23:48:12,753][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 23:48:13,261][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 23:48:13,761][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 23:48:14,267][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 23:48:14,766][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 23:48:15,264][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 23:48:15,762][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 23:48:16,261][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 23:48:16,764][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 23:48:17,264][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 23:48:17,762][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 23:48:18,263][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 23:48:18,763][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 23:48:19,264][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 23:48:19,762][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 23:48:20,261][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 23:48:20,762][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 23:48:21,261][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 23:48:21,760][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 23:48:22,258][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 23:48:22,756][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 23:48:23,254][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 23:48:23,753][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 23:48:24,249][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 23:48:24,750][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 23:48:25,248][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 23:48:25,746][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 23:48:26,245][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10869 tokens. [2025-11-12 23:48:26,888][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.17%, Current % of VRAM taken: 59.63%, Block Peak % of device VRAM: 62.24%, ΔTime: 00:00:32 [2025-11-12 23:48:27,670][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 23:48:27,672][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 23:48:27,673][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 23:48:28,600][__main__][INFO] - Iteration 113 took 52s (31.16% Gen, 67.07% Train). Generation: 16s, Training: 35s. Estimated remaining time: 42h 0m 24s. Estimated total time: 43h 39m 11s. Time estimates for 10 more iterations: 8m 43s, 100 more iterations: 1h 27m 18s, 500 more iterations: 7h 16m 31s. [2025-11-12 23:48:28,602][__main__][INFO] - Starting iteration 113. [2025-11-12 23:48:29,088][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 11 and human policies 1. [2025-11-12 23:48:29,088][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 23:48:44,983][__main__][INFO] - Number of regex retries in iteration 113: 0 [2025-11-12 23:48:44,983][__main__][INFO] - agents played in iteration 113 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 23:48:45,831][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:48:45,853][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:48:45,876][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:48:45,898][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:48:45,899][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 23:48:45,900][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 23:48:46,566][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 23:48:47,036][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 23:48:47,545][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 23:48:48,066][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 23:48:48,567][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 23:48:49,070][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 23:48:49,573][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 23:48:50,077][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 23:48:50,585][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 23:48:51,085][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 23:48:51,587][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-12 23:49:03,199][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 23:49:03,699][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 23:49:04,198][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 23:49:04,700][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 23:49:05,203][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 23:49:05,705][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 23:49:06,211][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 23:49:06,715][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 23:49:07,217][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 23:49:07,721][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 23:49:08,218][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 23:49:08,714][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 23:49:09,212][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 23:49:09,710][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 23:49:10,209][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 23:49:10,706][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 23:49:11,204][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 23:49:11,706][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 23:49:12,204][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 23:49:12,704][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 23:49:13,202][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 23:49:13,701][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 23:49:14,202][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 23:49:14,701][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 23:49:15,200][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 23:49:15,701][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 23:49:16,221][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 23:49:16,724][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 23:49:17,225][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 23:49:17,727][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 23:49:18,228][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 23:49:18,726][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10873 tokens. [2025-11-12 23:49:19,359][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.23%, Current % of VRAM taken: 59.68%, Block Peak % of device VRAM: 62.39%, ΔTime: 00:00:32 [2025-11-12 23:49:20,152][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 23:49:20,154][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 23:49:20,156][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 23:49:21,073][__main__][INFO] - Iteration 114 took 51s (30.58% Gen, 67.66% Train). Generation: 15s, Training: 35s. Estimated remaining time: 41h 39m 36s. Estimated total time: 43h 19m 16s. Time estimates for 10 more iterations: 8m 39s, 100 more iterations: 1h 26m 38s, 500 more iterations: 7h 13m 12s. [2025-11-12 23:49:21,075][__main__][INFO] - Starting iteration 114. [2025-11-12 23:49:21,541][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 11 and human policies 1. [2025-11-12 23:49:21,541][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 23:49:35,830][__main__][INFO] - Number of regex retries in iteration 114: 0 [2025-11-12 23:49:35,831][__main__][INFO] - agents played in iteration 114 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 23:49:36,692][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:49:36,714][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:49:36,736][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:49:36,759][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:49:36,759][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 23:49:36,761][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 23:49:37,393][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 23:49:37,863][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 23:49:38,368][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 23:49:38,870][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 23:49:39,375][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 23:49:39,877][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 23:49:40,387][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 23:49:40,889][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 23:49:41,394][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 23:49:41,900][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 23:49:42,403][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-12 23:49:42,906][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-12 23:49:43,407][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-12 23:49:43,908][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-12 23:49:44,408][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-12 23:49:44,909][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-12 23:49:45,410][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-12 23:49:45,908][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-12 23:49:46,405][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-12 23:49:46,907][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-12 23:49:47,409][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-12 23:49:47,907][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-12 23:49:53,930][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 23:49:54,433][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 23:49:54,934][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 23:49:55,434][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 23:49:55,936][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 23:49:56,439][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 23:49:56,939][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 23:49:57,440][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 23:49:57,946][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 23:49:58,448][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 23:49:58,965][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 23:49:59,468][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 23:49:59,970][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 23:50:00,473][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 23:50:00,972][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 23:50:01,484][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 23:50:01,984][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 23:50:02,485][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 23:50:02,986][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 23:50:03,487][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 23:50:03,987][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 23:50:04,490][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 23:50:04,989][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 23:50:05,506][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 23:50:06,005][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 23:50:06,501][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 23:50:07,002][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 23:50:07,501][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 23:50:08,000][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 23:50:08,499][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 23:50:08,997][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 23:50:09,512][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10854 tokens. [2025-11-12 23:50:10,152][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.20%, Current % of VRAM taken: 59.65%, Block Peak % of device VRAM: 62.29%, ΔTime: 00:00:32 [2025-11-12 23:50:10,953][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 23:50:10,954][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 23:50:10,956][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 23:50:11,905][__main__][INFO] - Iteration 115 took 50s (28.37% Gen, 69.74% Train). Generation: 14s, Training: 35s. Estimated remaining time: 40h 17m 45s. Estimated total time: 41h 58m 16s. Time estimates for 10 more iterations: 8m 23s, 100 more iterations: 1h 23m 56s, 500 more iterations: 6h 59m 42s. [2025-11-12 23:50:11,908][__main__][INFO] - Starting iteration 115. [2025-11-12 23:50:12,388][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 11 and human policies 1. [2025-11-12 23:50:12,388][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 23:50:25,209][__main__][INFO] - Number of regex retries in iteration 115: 0 [2025-11-12 23:50:25,210][__main__][INFO] - agents played in iteration 115 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 23:50:26,104][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:50:26,131][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:50:26,158][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:50:26,182][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:50:26,182][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 23:50:26,183][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 23:50:26,840][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 23:50:27,299][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 23:50:27,813][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 23:50:28,314][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 23:50:28,814][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 23:50:29,323][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 23:50:29,825][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 23:50:30,330][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 23:50:30,846][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 23:50:31,347][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 23:50:31,852][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-12 23:50:43,453][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 23:50:43,956][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 23:50:44,457][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 23:50:44,962][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 23:50:45,464][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 23:50:45,965][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 23:50:46,467][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 23:50:46,968][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 23:50:47,470][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 23:50:47,970][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 23:50:48,475][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 23:50:48,987][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 23:50:49,489][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 23:50:49,994][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 23:50:50,504][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 23:50:51,006][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 23:50:51,517][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 23:50:52,014][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 23:50:52,510][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 23:50:53,011][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 23:50:53,510][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 23:50:54,008][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 23:50:54,507][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 23:50:55,004][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 23:50:55,516][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 23:50:56,018][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 23:50:56,514][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 23:50:57,014][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 23:50:57,512][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 23:50:58,022][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 23:50:58,519][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 23:50:59,020][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10877 tokens. [2025-11-12 23:50:59,661][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.20%, Current % of VRAM taken: 59.66%, Block Peak % of device VRAM: 62.26%, ΔTime: 00:00:32 [2025-11-12 23:51:00,418][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 23:51:00,420][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 23:51:00,421][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 23:51:01,346][__main__][INFO] - Iteration 116 took 48s (26.19% Gen, 71.92% Train). Generation: 12s, Training: 35s. Estimated remaining time: 39h 6m 35s. Estimated total time: 40h 47m 54s. Time estimates for 10 more iterations: 8m 9s, 100 more iterations: 1h 21m 35s, 500 more iterations: 6h 47m 59s. [2025-11-12 23:51:01,348][__main__][INFO] - Starting iteration 116. [2025-11-12 23:51:01,835][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 11 and human policies 1. [2025-11-12 23:51:01,836][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 23:51:07,914][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 23:51:17,767][__main__][INFO] - Number of regex retries in iteration 116: 1 [2025-11-12 23:51:17,768][__main__][INFO] - agents played in iteration 116 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 23:51:18,609][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:51:18,636][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:51:18,674][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:51:18,698][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:51:18,698][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 23:51:18,699][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 23:51:19,354][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 23:51:19,815][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 23:51:20,324][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 23:51:20,828][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 23:51:21,334][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 23:51:21,835][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 23:51:22,338][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 23:51:22,847][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 23:51:23,353][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 23:51:23,856][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 23:51:24,358][mllm.training.trainer_common][INFO] - 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Processing mini-batch 43 of 64 [2025-11-12 23:51:41,467][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 23:51:41,964][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 23:51:42,469][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 23:51:42,970][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 23:51:43,472][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 23:51:43,973][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 23:51:44,470][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 23:51:44,967][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 23:51:45,463][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 23:51:45,958][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 23:51:46,458][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 23:51:46,953][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 23:51:47,450][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 23:51:47,953][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 23:51:48,451][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 23:51:48,951][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 23:51:49,445][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 23:51:49,943][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 23:51:50,445][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 23:51:50,944][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 23:51:51,442][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10864 tokens. [2025-11-12 23:51:52,102][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.22%, Current % of VRAM taken: 59.67%, Block Peak % of device VRAM: 62.46%, ΔTime: 00:00:32 [2025-11-12 23:51:52,891][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 23:51:52,893][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 23:51:52,895][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 23:51:53,953][__main__][INFO] - Iteration 117 took 52s (30.57% Gen, 67.40% Train). Generation: 15s, Training: 35s. Estimated remaining time: 41h 43m 43s. Estimated total time: 43h 25m 55s. Time estimates for 10 more iterations: 8m 41s, 100 more iterations: 1h 26m 51s, 500 more iterations: 7h 14m 19s. [2025-11-12 23:51:53,955][__main__][INFO] - Starting iteration 117. [2025-11-12 23:51:54,440][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 11 and human policies 1. [2025-11-12 23:51:54,440][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 23:52:06,046][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 0 balls Given the values, I see an opportunity to maximize my points by focusing on the items where I have the highest value and where Bob has the lowest value. In this case, both hats and either books or balls have values of 1 or 10 respectively, but since the value of hats is the same for both of us, it would be better to take the entire allocation of hats to ensure I receive the full value of 1 point per hat. Since both Bob and I value hats at 1, and there's no way to force him to give up any value here, I propose keeping all 10 hats to secure my points from this round. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 23:52:11,708][__main__][INFO] - Number of regex retries in iteration 117: 1 [2025-11-12 23:52:11,709][__main__][INFO] - agents played in iteration 117 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 23:52:12,555][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:52:12,578][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:52:12,599][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:52:12,621][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:52:12,623][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 23:52:12,624][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 23:52:13,291][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 23:52:13,751][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 23:52:14,263][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 23:52:14,764][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 23:52:15,267][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 23:52:15,768][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 23:52:16,271][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 23:52:16,776][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 23:52:17,280][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 23:52:17,783][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 23:52:18,294][mllm.training.trainer_common][INFO] - 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Processing mini-batch 54 of 64 [2025-11-12 23:52:40,891][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 23:52:41,393][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 23:52:41,892][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 23:52:42,390][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 23:52:42,888][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 23:52:43,386][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 23:52:43,884][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 23:52:44,383][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 23:52:44,882][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 23:52:45,380][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10873 tokens. [2025-11-12 23:52:46,018][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.19%, Current % of VRAM taken: 59.64%, Block Peak % of device VRAM: 62.37%, ΔTime: 00:00:32 [2025-11-12 23:52:46,808][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 23:52:46,810][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 23:52:46,812][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 23:52:47,893][__main__][INFO] - Iteration 118 took 53s (32.30% Gen, 65.67% Train). Generation: 17s, Training: 35s. Estimated remaining time: 42h 49m 34s. Estimated total time: 44h 32m 40s. Time estimates for 10 more iterations: 8m 54s, 100 more iterations: 1h 29m 5s, 500 more iterations: 7h 25m 26s. [2025-11-12 23:52:47,895][__main__][INFO] - Starting iteration 118. [2025-11-12 23:52:48,388][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 11 and human policies 1. [2025-11-12 23:52:48,389][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 23:53:00,752][__main__][INFO] - Number of regex retries in iteration 118: 0 [2025-11-12 23:53:00,752][__main__][INFO] - agents played in iteration 118 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 23:53:01,648][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:53:01,670][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:53:01,693][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:53:01,715][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:53:01,716][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 23:53:01,717][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 23:53:02,362][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 23:53:02,824][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 23:53:03,333][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 23:53:03,833][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 23:53:04,333][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 23:53:04,835][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 23:53:05,352][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 23:53:05,855][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 23:53:06,357][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 23:53:06,862][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 23:53:07,366][mllm.training.trainer_common][INFO] - 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Processing mini-batch 43 of 64 [2025-11-12 23:53:24,449][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 23:53:24,959][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 23:53:25,464][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 23:53:25,967][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 23:53:26,472][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 23:53:26,973][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 23:53:27,474][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 23:53:27,969][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 23:53:28,469][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 23:53:28,969][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 23:53:29,470][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 23:53:29,972][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 23:53:30,466][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 23:53:30,966][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 23:53:31,468][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 23:53:31,965][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 23:53:32,464][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 23:53:32,964][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 23:53:33,463][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 23:53:33,960][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 23:53:34,457][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10872 tokens. [2025-11-12 23:53:35,088][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.17%, Current % of VRAM taken: 59.63%, Block Peak % of device VRAM: 62.24%, ΔTime: 00:00:32 [2025-11-12 23:53:35,866][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 23:53:35,867][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 23:53:35,869][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 23:53:36,788][__main__][INFO] - Iteration 119 took 48s (25.54% Gen, 72.55% Train). Generation: 12s, Training: 35s. Estimated remaining time: 38h 36m 6s. Estimated total time: 40h 20m 2s. Time estimates for 10 more iterations: 8m 4s, 100 more iterations: 1h 20m 40s, 500 more iterations: 6h 43m 20s. [2025-11-12 23:53:36,790][__main__][INFO] - Starting iteration 119. [2025-11-12 23:53:37,275][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 11 and human policies 1. [2025-11-12 23:53:37,276][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 23:53:40,336][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 23:53:51,133][__main__][INFO] - Number of regex retries in iteration 119: 1 [2025-11-12 23:53:51,134][__main__][INFO] - agents played in iteration 119 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 23:53:51,905][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:53:51,931][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:53:51,957][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:53:51,980][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:53:51,980][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 23:53:51,981][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 23:53:52,587][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 23:53:53,043][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 23:53:53,557][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 23:53:54,057][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 23:53:54,562][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 23:53:55,075][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 23:53:55,577][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 23:53:56,081][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 23:53:56,581][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 23:53:57,080][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 23:53:57,586][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-12 23:53:58,088][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-12 23:53:58,588][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-12 23:53:59,092][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-12 23:53:59,592][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-12 23:54:00,091][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-12 23:54:00,593][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-12 23:54:01,094][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-12 23:54:01,597][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-12 23:54:02,101][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-12 23:54:02,604][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-12 23:54:03,110][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-12 23:54:03,612][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-12 23:54:04,118][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-12 23:54:04,619][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-12 23:54:05,125][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-12 23:54:05,639][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-12 23:54:06,144][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-12 23:54:06,659][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-12 23:54:07,161][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-12 23:54:07,663][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-12 23:54:08,166][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-12 23:54:08,668][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-12 23:54:09,169][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 23:54:09,668][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 23:54:10,169][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 23:54:10,677][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 23:54:11,179][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 23:54:11,682][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 23:54:12,186][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 23:54:12,688][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 23:54:13,189][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 23:54:13,691][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 23:54:14,192][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 23:54:14,693][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 23:54:15,190][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 23:54:15,689][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 23:54:16,192][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 23:54:16,693][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 23:54:17,195][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 23:54:17,695][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 23:54:18,194][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 23:54:18,697][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 23:54:19,198][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 23:54:19,697][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 23:54:20,197][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 23:54:20,697][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 23:54:21,198][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 23:54:21,700][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 23:54:22,204][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 23:54:22,705][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 23:54:23,206][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 23:54:23,706][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 23:54:24,206][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 23:54:24,705][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10875 tokens. [2025-11-12 23:54:25,331][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.25%, Current % of VRAM taken: 59.70%, Block Peak % of device VRAM: 62.28%, ΔTime: 00:00:32 [2025-11-12 23:54:26,117][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 23:54:26,118][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 23:54:26,120][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 23:54:27,024][__main__][INFO] - Iteration 120 took 49s (27.86% Gen, 70.32% Train). Generation: 13s, Training: 34s. Estimated remaining time: 39h 42m 42s. Estimated total time: 41h 27m 27s. Time estimates for 10 more iterations: 8m 17s, 100 more iterations: 1h 22m 54s, 500 more iterations: 6h 54m 34s. [2025-11-12 23:54:27,026][__main__][INFO] - Starting iteration 120. [2025-11-12 23:54:27,517][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 11 and human policies 1. [2025-11-12 23:54:27,517][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 23:54:30,746][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 23:54:37,652][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 23:54:42,210][__main__][INFO] - Number of regex retries in iteration 120: 2 [2025-11-12 23:54:42,211][__main__][INFO] - agents played in iteration 120 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 23:54:43,142][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:54:43,165][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:54:43,187][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:54:43,208][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:54:43,209][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 23:54:43,210][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 23:54:43,816][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 23:54:44,268][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 23:54:44,772][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 23:54:45,270][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 23:54:45,772][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 23:54:46,274][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 23:54:46,773][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 23:54:47,284][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 23:54:47,785][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 23:54:48,291][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 23:54:48,797][mllm.training.trainer_common][INFO] - 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Processing mini-batch 21 of 64 [2025-11-12 23:54:54,835][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-12 23:54:55,350][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-12 23:54:55,852][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-12 23:54:56,361][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-12 23:54:56,863][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-12 23:54:57,366][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-12 23:54:57,872][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-12 23:54:58,373][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-12 23:54:58,877][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-12 23:54:59,378][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-12 23:54:59,875][mllm.training.trainer_common][INFO] - 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Processing mini-batch 43 of 64 [2025-11-12 23:55:05,908][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 23:55:06,411][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 23:55:06,915][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 23:55:07,418][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 23:55:07,918][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 23:55:08,420][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 23:55:08,922][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 23:55:09,421][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 23:55:09,922][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 23:55:10,421][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 23:55:10,923][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 23:55:11,425][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 23:55:11,925][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 23:55:12,426][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 23:55:12,924][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 23:55:13,424][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 23:55:13,924][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 23:55:14,427][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 23:55:14,929][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 23:55:15,428][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 23:55:15,927][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10878 tokens. [2025-11-12 23:55:16,566][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.13%, Current % of VRAM taken: 59.58%, Block Peak % of device VRAM: 62.20%, ΔTime: 00:00:32 [2025-11-12 23:55:17,356][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 23:55:17,357][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 23:55:17,359][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 23:55:19,096][__main__][INFO] - Iteration 121 took 51s (28.49% Gen, 68.14% Train). Generation: 14s, Training: 35s. Estimated remaining time: 41h 13m 21s. Estimated total time: 42h 58m 59s. Time estimates for 10 more iterations: 8m 35s, 100 more iterations: 1h 25m 57s, 500 more iterations: 7h 9m 49s. [2025-11-12 23:55:19,098][__main__][INFO] - Starting iteration 121. [2025-11-12 23:55:19,567][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 12 and human policies 1. [2025-11-12 23:55:19,567][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 23:55:24,173][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 23:55:33,858][__main__][INFO] - Number of regex retries in iteration 121: 1 [2025-11-12 23:55:33,858][__main__][INFO] - agents played in iteration 121 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 23:55:34,703][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.52%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:55:34,727][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.52%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:55:34,753][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.52%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:55:34,776][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.52%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:55:34,776][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 23:55:34,777][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 23:55:35,379][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 23:55:36,117][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 23:55:36,623][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 23:55:37,125][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 23:55:37,633][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 23:55:38,135][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 23:55:38,637][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 23:55:39,138][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 23:55:39,643][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 23:55:40,146][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 23:55:40,650][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-12 23:55:52,238][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 23:55:52,736][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 23:55:53,239][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 23:55:53,737][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 23:55:54,239][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 23:55:54,737][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 23:55:55,237][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 23:55:55,745][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 23:55:56,247][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 23:55:56,748][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 23:55:57,259][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 23:55:57,760][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 23:55:58,262][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 23:55:58,764][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 23:55:59,269][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 23:55:59,787][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 23:56:00,288][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 23:56:00,788][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 23:56:01,288][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 23:56:01,789][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 23:56:02,295][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 23:56:02,795][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 23:56:03,294][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 23:56:03,807][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 23:56:04,308][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 23:56:04,814][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 23:56:05,329][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 23:56:05,830][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 23:56:06,337][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 23:56:06,841][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 23:56:07,342][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 23:56:07,846][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10870 tokens. [2025-11-12 23:56:08,478][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.25%, Current % of VRAM taken: 59.71%, Block Peak % of device VRAM: 62.26%, ΔTime: 00:00:33 [2025-11-12 23:56:09,277][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 23:56:09,279][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 23:56:09,281][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 23:56:10,199][__main__][INFO] - Iteration 122 took 50s (28.22% Gen, 69.96% Train). Generation: 14s, Training: 35s. Estimated remaining time: 40h 25m 9s. Estimated total time: 42h 11m 37s. Time estimates for 10 more iterations: 8m 26s, 100 more iterations: 1h 24m 23s, 500 more iterations: 7h 1m 56s. [2025-11-12 23:56:10,201][__main__][INFO] - Starting iteration 122. [2025-11-12 23:56:10,680][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 12 and human policies 1. [2025-11-12 23:56:10,681][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 23:56:13,938][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 23:56:25,455][__main__][INFO] - Number of regex retries in iteration 122: 1 [2025-11-12 23:56:25,455][__main__][INFO] - agents played in iteration 122 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 23:56:26,233][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:56:26,258][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:56:26,284][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:56:26,306][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:56:26,306][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 23:56:26,307][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 23:56:26,914][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 23:56:27,375][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 23:56:27,878][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 23:56:28,378][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 23:56:28,879][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 23:56:29,379][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 23:56:29,897][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 23:56:30,400][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 23:56:30,904][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 23:56:31,411][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 23:56:31,912][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-12 23:56:32,417][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-12 23:56:32,920][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-12 23:56:33,422][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-12 23:56:33,929][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-12 23:56:34,433][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-12 23:56:34,933][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-12 23:56:35,433][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-12 23:56:35,931][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-12 23:56:36,436][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-12 23:56:36,936][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-12 23:56:37,436][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-12 23:56:37,938][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-12 23:56:38,438][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-12 23:56:38,935][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-12 23:56:39,435][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-12 23:56:39,934][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-12 23:56:40,437][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-12 23:56:40,936][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-12 23:56:41,433][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-12 23:56:41,937][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-12 23:56:42,438][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-12 23:56:42,937][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-12 23:56:43,444][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 23:56:43,946][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 23:56:44,450][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 23:56:44,953][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 23:56:45,454][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 23:56:45,956][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 23:56:46,455][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 23:56:46,971][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 23:56:47,474][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 23:56:47,977][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 23:56:48,482][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 23:56:48,986][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 23:56:49,495][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 23:56:49,996][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 23:56:50,496][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 23:56:51,005][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 23:56:51,505][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 23:56:52,007][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 23:56:52,505][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 23:56:53,005][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 23:56:53,508][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 23:56:54,005][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 23:56:54,505][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 23:56:55,012][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 23:56:55,515][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 23:56:56,015][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 23:56:56,515][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 23:56:57,016][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 23:56:57,524][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 23:56:58,025][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 23:56:58,527][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 23:56:59,029][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10878 tokens. [2025-11-12 23:56:59,716][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.25%, Current % of VRAM taken: 59.70%, Block Peak % of device VRAM: 62.28%, ΔTime: 00:00:32 [2025-11-12 23:57:00,505][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 23:57:00,507][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 23:57:00,508][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 23:57:01,710][__main__][INFO] - Iteration 123 took 51s (28.95% Gen, 68.69% Train). Generation: 14s, Training: 35s. Estimated remaining time: 40h 44m 13s. Estimated total time: 42h 31m 33s. Time estimates for 10 more iterations: 8m 30s, 100 more iterations: 1h 25m 3s, 500 more iterations: 7h 5m 15s. [2025-11-12 23:57:01,713][__main__][INFO] - Starting iteration 123. [2025-11-12 23:57:02,199][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 12 and human policies 1. [2025-11-12 23:57:02,199][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 23:57:05,832][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 23:57:15,790][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 23:57:16,598][__main__][INFO] - Number of regex retries in iteration 123: 2 [2025-11-12 23:57:16,598][__main__][INFO] - agents played in iteration 123 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 23:57:17,439][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:57:17,465][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:57:17,491][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:57:17,513][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:57:17,514][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 23:57:17,514][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 23:57:18,124][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 23:57:18,583][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 23:57:19,089][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 23:57:19,588][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 23:57:20,094][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 23:57:20,591][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 23:57:21,103][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 23:57:21,606][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 23:57:22,108][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 23:57:22,610][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 23:57:23,114][mllm.training.trainer_common][INFO] - 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Processing mini-batch 21 of 64 [2025-11-12 23:57:29,136][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-12 23:57:29,638][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-12 23:57:30,139][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-12 23:57:30,644][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-12 23:57:31,147][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-12 23:57:31,651][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-12 23:57:32,155][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-12 23:57:32,656][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-12 23:57:33,163][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-12 23:57:33,667][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-12 23:57:34,171][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-12 23:57:34,685][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-12 23:57:35,189][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-12 23:57:35,712][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-12 23:57:36,215][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-12 23:57:36,719][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-12 23:57:37,220][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-12 23:57:37,724][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-12 23:57:38,226][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-12 23:57:38,730][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-12 23:57:39,232][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-12 23:57:39,737][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-12 23:57:40,239][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 23:57:40,739][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 23:57:41,240][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 23:57:41,740][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 23:57:42,239][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 23:57:42,739][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 23:57:43,239][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 23:57:43,739][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 23:57:44,239][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 23:57:44,741][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 23:57:45,244][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 23:57:45,745][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 23:57:46,247][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 23:57:46,750][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 23:57:47,248][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 23:57:47,749][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 23:57:48,252][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 23:57:48,751][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 23:57:49,252][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 23:57:49,752][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 23:57:50,252][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10877 tokens. [2025-11-12 23:57:50,901][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.20%, Current % of VRAM taken: 59.66%, Block Peak % of device VRAM: 62.33%, ΔTime: 00:00:32 [2025-11-12 23:57:51,713][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 23:57:51,714][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 23:57:51,716][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 23:57:52,627][__main__][INFO] - Iteration 124 took 50s (28.55% Gen, 69.64% Train). Generation: 14s, Training: 35s. Estimated remaining time: 40h 13m 15s. Estimated total time: 42h 1m 26s. Time estimates for 10 more iterations: 8m 24s, 100 more iterations: 1h 24m 2s, 500 more iterations: 7h 0m 14s. [2025-11-12 23:57:52,630][__main__][INFO] - Starting iteration 124. [2025-11-12 23:57:53,130][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 12 and human policies 1. [2025-11-12 23:57:53,130][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 23:57:59,796][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-12 23:58:07,820][__main__][INFO] - Number of regex retries in iteration 124: 1 [2025-11-12 23:58:07,820][__main__][INFO] - agents played in iteration 124 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 23:58:08,649][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:58:08,672][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:58:08,694][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:58:08,717][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:58:08,719][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 23:58:08,721][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 23:58:09,327][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 23:58:09,784][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 23:58:10,305][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 23:58:10,803][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 23:58:11,304][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 23:58:11,808][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 23:58:12,307][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 23:58:12,817][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 23:58:13,325][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 23:58:13,830][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 23:58:14,338][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-12 23:58:14,841][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-12 23:58:15,340][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-12 23:58:15,845][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-12 23:58:16,349][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-12 23:58:16,853][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-12 23:58:17,355][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-12 23:58:17,856][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-12 23:58:18,359][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-12 23:58:18,859][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-12 23:58:19,362][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-12 23:58:19,862][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-12 23:58:20,360][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-12 23:58:20,862][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-12 23:58:21,364][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-12 23:58:21,867][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-12 23:58:22,378][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-12 23:58:22,884][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-12 23:58:23,401][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-12 23:58:23,906][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-12 23:58:24,408][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-12 23:58:24,911][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-12 23:58:25,419][mllm.training.trainer_common][INFO] - 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Processing mini-batch 54 of 64 [2025-11-12 23:58:37,004][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 23:58:37,499][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 23:58:38,018][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 23:58:38,518][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 23:58:39,017][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 23:58:39,517][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 23:58:40,018][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 23:58:40,523][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 23:58:41,024][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 23:58:41,525][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10876 tokens. [2025-11-12 23:58:42,181][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.25%, Current % of VRAM taken: 59.70%, Block Peak % of device VRAM: 62.29%, ΔTime: 00:00:32 [2025-11-12 23:58:42,973][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 23:58:42,975][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 23:58:42,976][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 23:58:44,092][__main__][INFO] - Iteration 125 took 50s (28.83% Gen, 68.98% Train). Generation: 14s, Training: 35s. Estimated remaining time: 40h 39m 6s. Estimated total time: 42h 28m 8s. Time estimates for 10 more iterations: 8m 29s, 100 more iterations: 1h 24m 56s, 500 more iterations: 7h 4m 41s. [2025-11-12 23:58:44,095][__main__][INFO] - Starting iteration 125. [2025-11-12 23:58:44,563][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 12 and human policies 1. [2025-11-12 23:58:44,564][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 23:58:57,595][__main__][INFO] - Number of regex retries in iteration 125: 0 [2025-11-12 23:58:57,596][__main__][INFO] - agents played in iteration 125 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 23:58:58,369][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:58:58,397][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:58:58,422][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:58:58,445][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:58:58,445][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 23:58:58,446][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 23:58:59,056][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 23:58:59,507][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 23:59:00,020][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 23:59:00,516][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 23:59:01,013][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 23:59:01,512][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 23:59:02,011][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 23:59:02,514][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 23:59:03,012][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 23:59:03,512][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 23:59:04,027][mllm.training.trainer_common][INFO] - 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Processing mini-batch 43 of 64 [2025-11-12 23:59:21,144][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-12 23:59:21,647][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-12 23:59:22,153][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-12 23:59:22,658][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-12 23:59:23,162][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-12 23:59:23,665][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-12 23:59:24,170][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-12 23:59:24,674][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-12 23:59:25,173][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-12 23:59:25,673][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-12 23:59:26,174][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-12 23:59:26,676][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-12 23:59:27,179][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-12 23:59:27,681][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-12 23:59:28,178][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-12 23:59:28,678][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-12 23:59:29,175][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-12 23:59:29,673][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-12 23:59:30,173][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-12 23:59:30,671][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-12 23:59:31,170][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10870 tokens. [2025-11-12 23:59:31,817][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.19%, Current % of VRAM taken: 59.65%, Block Peak % of device VRAM: 62.38%, ΔTime: 00:00:32 [2025-11-12 23:59:32,609][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-12 23:59:32,611][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-12 23:59:32,612][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-12 23:59:33,522][__main__][INFO] - Iteration 126 took 48s (26.62% Gen, 71.52% Train). Generation: 13s, Training: 35s. Estimated remaining time: 38h 58m 8s. Estimated total time: 40h 47m 59s. Time estimates for 10 more iterations: 8m 9s, 100 more iterations: 1h 21m 35s, 500 more iterations: 6h 47m 59s. [2025-11-12 23:59:33,525][__main__][INFO] - Starting iteration 126. [2025-11-12 23:59:34,011][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 12 and human policies 1. [2025-11-12 23:59:34,011][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-12 23:59:47,660][__main__][INFO] - Number of regex retries in iteration 126: 0 [2025-11-12 23:59:47,660][__main__][INFO] - agents played in iteration 126 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-12 23:59:48,445][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:59:48,472][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:59:48,498][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:59:48,520][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-12 23:59:48,521][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-12 23:59:48,522][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-12 23:59:49,116][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-12 23:59:49,572][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-12 23:59:50,076][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-12 23:59:50,574][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-12 23:59:51,075][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-12 23:59:51,573][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-12 23:59:52,073][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-12 23:59:52,575][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-12 23:59:53,073][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-12 23:59:53,574][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-12 23:59:54,075][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 00:00:05,674][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 00:00:06,173][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 00:00:06,673][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 00:00:07,175][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 00:00:07,674][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 00:00:08,175][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 00:00:08,677][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 00:00:09,180][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 00:00:09,685][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 00:00:10,192][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 00:00:10,698][mllm.training.trainer_common][INFO] - 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Processing mini-batch 54 of 64 [2025-11-13 00:00:16,755][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 00:00:17,255][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 00:00:17,754][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 00:00:18,253][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 00:00:18,752][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 00:00:19,253][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 00:00:19,753][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 00:00:20,253][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 00:00:20,751][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 00:00:21,251][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10874 tokens. [2025-11-13 00:00:21,907][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.19%, Current % of VRAM taken: 59.64%, Block Peak % of device VRAM: 62.41%, ΔTime: 00:00:32 [2025-11-13 00:00:22,680][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 00:00:22,682][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 00:00:22,683][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 00:00:23,659][__main__][INFO] - Iteration 127 took 49s (27.49% Gen, 70.54% Train). Generation: 13s, Training: 35s. Estimated remaining time: 39h 31m 44s. Estimated total time: 41h 22m 26s. Time estimates for 10 more iterations: 8m 16s, 100 more iterations: 1h 22m 44s, 500 more iterations: 6h 53m 44s. [2025-11-13 00:00:23,661][__main__][INFO] - Starting iteration 127. [2025-11-13 00:00:24,151][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 12 and human policies 1. [2025-11-13 00:00:24,153][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 00:00:39,028][__main__][INFO] - Number of regex retries in iteration 127: 0 [2025-11-13 00:00:39,029][__main__][INFO] - agents played in iteration 127 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 00:00:39,847][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:00:39,871][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:00:39,893][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:00:39,915][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:00:39,916][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 00:00:39,917][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 00:00:40,535][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 00:00:40,988][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 00:00:41,493][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 00:00:41,991][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 00:00:42,493][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 00:00:42,995][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 00:00:43,494][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 00:00:43,993][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 00:00:44,496][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 00:00:44,995][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 00:00:45,493][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 00:00:45,998][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 00:00:46,503][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 00:00:47,015][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 00:00:47,513][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 00:00:48,017][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 00:00:48,523][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 00:00:49,025][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 00:00:49,537][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 00:00:50,037][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 00:00:50,540][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 00:00:51,044][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 00:00:57,070][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 00:00:57,572][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 00:00:58,100][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 00:00:58,602][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 00:00:59,115][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 00:00:59,619][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 00:01:00,134][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 00:01:00,639][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 00:01:01,141][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 00:01:01,648][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 00:01:02,151][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 00:01:02,656][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 00:01:03,160][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 00:01:03,662][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 00:01:04,169][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 00:01:04,673][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 00:01:05,176][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 00:01:05,680][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 00:01:06,181][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 00:01:06,684][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 00:01:07,186][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 00:01:07,689][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 00:01:08,189][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 00:01:08,687][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 00:01:09,187][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 00:01:09,687][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 00:01:10,187][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 00:01:10,689][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 00:01:11,190][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 00:01:11,690][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 00:01:12,195][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 00:01:12,694][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10875 tokens. [2025-11-13 00:01:13,360][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.23%, Current % of VRAM taken: 59.68%, Block Peak % of device VRAM: 62.28%, ΔTime: 00:00:32 [2025-11-13 00:01:14,134][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 00:01:14,135][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 00:01:14,137][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 00:01:15,057][__main__][INFO] - Iteration 128 took 50s (29.22% Gen, 68.97% Train). Generation: 14s, Training: 35s. Estimated remaining time: 40h 33m 45s. Estimated total time: 42h 25m 19s. Time estimates for 10 more iterations: 8m 29s, 100 more iterations: 1h 24m 50s, 500 more iterations: 7h 4m 13s. [2025-11-13 00:01:15,059][__main__][INFO] - Starting iteration 128. [2025-11-13 00:01:15,528][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 12 and human policies 1. [2025-11-13 00:01:15,529][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 00:01:18,796][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 00:01:30,756][__main__][INFO] - Number of regex retries in iteration 128: 1 [2025-11-13 00:01:30,757][__main__][INFO] - agents played in iteration 128 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 00:01:31,604][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:01:31,631][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:01:31,658][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:01:31,681][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:01:31,681][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 00:01:31,682][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 00:01:32,289][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 00:01:32,746][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 00:01:33,250][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 00:01:33,749][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 00:01:34,259][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 00:01:34,767][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 00:01:35,270][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 00:01:35,773][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 00:01:36,274][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 00:01:36,776][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 00:01:37,281][mllm.training.trainer_common][INFO] - 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Processing mini-batch 43 of 64 [2025-11-13 00:01:54,437][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 00:01:54,940][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 00:01:55,450][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 00:01:55,954][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 00:01:56,466][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 00:01:56,971][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 00:01:57,471][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 00:01:57,975][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 00:01:58,475][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 00:01:58,976][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 00:01:59,473][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 00:01:59,976][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 00:02:00,474][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 00:02:00,970][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 00:02:01,473][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 00:02:01,975][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 00:02:02,471][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 00:02:02,971][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 00:02:03,469][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 00:02:03,965][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 00:02:04,466][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10868 tokens. [2025-11-13 00:02:05,107][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.14%, Current % of VRAM taken: 59.59%, Block Peak % of device VRAM: 62.34%, ΔTime: 00:00:32 [2025-11-13 00:02:05,889][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 00:02:05,891][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 00:02:05,893][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 00:02:06,858][__main__][INFO] - Iteration 129 took 51s (29.67% Gen, 68.45% Train). Generation: 15s, Training: 35s. Estimated remaining time: 40h 54m 6s. Estimated total time: 42h 46m 31s. Time estimates for 10 more iterations: 8m 33s, 100 more iterations: 1h 25m 33s, 500 more iterations: 7h 7m 45s. [2025-11-13 00:02:06,860][__main__][INFO] - Starting iteration 129. [2025-11-13 00:02:07,342][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 12 and human policies 1. [2025-11-13 00:02:07,343][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 00:02:11,062][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 00:02:15,378][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 00:02:20,252][__main__][INFO] - Number of regex retries in iteration 129: 2 [2025-11-13 00:02:20,253][__main__][INFO] - agents played in iteration 129 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 00:02:21,091][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:02:21,117][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:02:21,142][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:02:21,165][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:02:21,166][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 00:02:21,167][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 00:02:21,780][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 00:02:22,237][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 00:02:22,754][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 00:02:23,258][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 00:02:23,763][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 00:02:24,270][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 00:02:24,770][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 00:02:25,274][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 00:02:25,774][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 00:02:26,277][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 00:02:26,785][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 00:02:27,287][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 00:02:27,787][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 00:02:28,292][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 00:02:28,796][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 00:02:29,299][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 00:02:29,801][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 00:02:30,301][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 00:02:30,805][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 00:02:31,307][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 00:02:31,808][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 00:02:32,310][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 00:02:32,811][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 00:02:33,313][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 00:02:33,813][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 00:02:34,315][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 00:02:34,815][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 00:02:35,316][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 00:02:35,820][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 00:02:36,320][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 00:02:36,820][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 00:02:37,328][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 00:02:37,830][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 00:02:38,329][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 00:02:38,836][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 00:02:39,337][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 00:02:39,847][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 00:02:40,345][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 00:02:40,844][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 00:02:41,362][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 00:02:41,862][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 00:02:42,365][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 00:02:42,865][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 00:02:43,366][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 00:02:43,869][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 00:02:44,369][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 00:02:44,870][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 00:02:45,376][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 00:02:45,879][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 00:02:46,384][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 00:02:46,888][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 00:02:47,391][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 00:02:47,894][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 00:02:48,398][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 00:02:48,901][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 00:02:49,404][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 00:02:49,906][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 00:02:50,405][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 00:02:50,907][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 00:02:51,408][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 00:02:51,913][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 00:02:52,414][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 00:02:52,914][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 00:02:53,423][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 00:02:53,927][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10867 tokens. [2025-11-13 00:02:54,583][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.26%, Current % of VRAM taken: 59.71%, Block Peak % of device VRAM: 62.35%, ΔTime: 00:00:32 [2025-11-13 00:02:55,354][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 00:02:55,356][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 00:02:55,357][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 00:02:56,571][__main__][INFO] - Iteration 130 took 49s (26.22% Gen, 71.31% Train). Generation: 12s, Training: 35s. Estimated remaining time: 39h 8m 15s. Estimated total time: 41h 1m 30s. Time estimates for 10 more iterations: 8m 12s, 100 more iterations: 1h 22m 3s, 500 more iterations: 6h 50m 15s. [2025-11-13 00:02:56,574][__main__][INFO] - Starting iteration 130. [2025-11-13 00:02:57,064][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 12 and human policies 1. [2025-11-13 00:02:57,064][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 00:03:00,088][mllm.models.large_language_model_local][WARNING] - Response Proposal: 5 hats, 5 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 00:03:00,171][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 00:03:11,499][__main__][INFO] - Number of regex retries in iteration 130: 2 [2025-11-13 00:03:11,499][__main__][INFO] - agents played in iteration 130 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 00:03:12,268][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:03:12,293][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:03:12,317][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:03:12,339][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:03:12,340][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 00:03:12,341][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 00:03:12,953][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 00:03:13,410][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 00:03:13,914][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 00:03:14,414][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 00:03:14,913][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 00:03:15,414][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 00:03:15,913][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 00:03:16,414][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 00:03:16,915][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 00:03:17,414][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 00:03:17,913][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 00:03:18,416][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 00:03:18,917][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 00:03:19,419][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 00:03:19,920][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 00:03:20,423][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 00:03:20,936][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 00:03:21,437][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 00:03:21,939][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 00:03:22,443][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 00:03:22,944][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 00:03:23,451][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 00:03:23,953][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 00:03:24,454][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 00:03:24,957][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 00:03:25,457][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 00:03:25,958][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 00:03:26,458][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 00:03:26,955][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 00:03:27,455][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 00:03:27,953][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 00:03:28,461][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 00:03:28,960][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 00:03:29,457][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 00:03:29,957][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 00:03:30,455][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 00:03:30,955][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 00:03:31,453][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 00:03:31,953][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 00:03:32,466][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 00:03:32,964][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 00:03:33,467][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 00:03:33,967][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 00:03:34,467][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 00:03:34,976][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 00:03:35,477][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 00:03:35,979][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 00:03:36,481][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 00:03:36,982][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 00:03:37,482][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 00:03:37,980][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 00:03:38,478][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 00:03:38,983][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 00:03:39,483][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 00:03:39,988][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 00:03:40,490][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 00:03:40,990][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 00:03:41,498][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 00:03:41,997][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 00:03:42,499][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 00:03:43,003][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 00:03:43,505][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 00:03:44,007][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 00:03:44,510][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 00:03:45,011][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10869 tokens. [2025-11-13 00:03:45,670][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.28%, Current % of VRAM taken: 59.74%, Block Peak % of device VRAM: 62.35%, ΔTime: 00:00:32 [2025-11-13 00:03:46,525][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 00:03:46,527][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 00:03:46,529][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 00:03:48,425][__main__][INFO] - Iteration 131 took 51s (28.10% Gen, 68.20% Train). Generation: 14s, Training: 35s. Estimated remaining time: 40h 53m 57s. Estimated total time: 42h 48m 4s. Time estimates for 10 more iterations: 8m 33s, 100 more iterations: 1h 25m 36s, 500 more iterations: 7h 8m 0s. [2025-11-13 00:03:48,427][__main__][INFO] - Starting iteration 131. [2025-11-13 00:03:48,906][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 13 and human policies 1. [2025-11-13 00:03:48,906][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 00:04:04,010][__main__][INFO] - Number of regex retries in iteration 131: 0 [2025-11-13 00:04:04,011][__main__][INFO] - agents played in iteration 131 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 00:04:04,963][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:04:04,991][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:04:05,017][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:04:05,041][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:04:05,041][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 00:04:05,042][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 00:04:05,663][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 00:04:06,119][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 00:04:06,621][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 00:04:07,120][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 00:04:07,618][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 00:04:08,117][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 00:04:08,620][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 00:04:09,123][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 00:04:09,626][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 00:04:10,129][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 00:04:10,629][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 00:04:22,215][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 00:04:22,717][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 00:04:23,218][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 00:04:23,721][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 00:04:24,224][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 00:04:24,727][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 00:04:25,230][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 00:04:25,733][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 00:04:26,234][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 00:04:26,735][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 00:04:27,237][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 00:04:27,740][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 00:04:28,243][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 00:04:28,759][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 00:04:29,260][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 00:04:29,761][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 00:04:30,280][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 00:04:30,779][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 00:04:31,280][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 00:04:31,783][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 00:04:32,285][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 00:04:32,791][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 00:04:33,294][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 00:04:33,797][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 00:04:34,303][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 00:04:34,804][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 00:04:35,306][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 00:04:35,808][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 00:04:36,310][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 00:04:36,812][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 00:04:37,313][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 00:04:37,813][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10877 tokens. [2025-11-13 00:04:38,454][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.25%, Current % of VRAM taken: 59.70%, Block Peak % of device VRAM: 62.34%, ΔTime: 00:00:32 [2025-11-13 00:04:39,243][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 00:04:39,245][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 00:04:39,247][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 00:04:40,219][__main__][INFO] - Iteration 132 took 51s (29.44% Gen, 68.67% Train). Generation: 15s, Training: 35s. Estimated remaining time: 40h 50m 44s. Estimated total time: 42h 45m 42s. Time estimates for 10 more iterations: 8m 33s, 100 more iterations: 1h 25m 31s, 500 more iterations: 7h 7m 37s. [2025-11-13 00:04:40,221][__main__][INFO] - Starting iteration 132. [2025-11-13 00:04:40,698][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 13 and human policies 1. [2025-11-13 00:04:40,699][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 00:04:46,791][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 00:04:55,110][__main__][INFO] - Number of regex retries in iteration 132: 1 [2025-11-13 00:04:55,110][__main__][INFO] - agents played in iteration 132 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 00:04:56,093][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:04:56,121][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:04:56,148][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:04:56,171][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:04:56,171][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 00:04:56,172][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 00:04:56,787][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 00:04:57,240][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 00:04:57,743][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 00:04:58,238][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 00:04:58,734][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 00:04:59,230][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 00:04:59,726][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 00:05:00,221][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 00:05:00,721][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 00:05:01,224][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 00:05:01,725][mllm.training.trainer_common][INFO] - 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Processing mini-batch 54 of 64 [2025-11-13 00:05:24,400][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 00:05:24,902][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 00:05:25,411][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 00:05:25,915][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 00:05:26,419][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 00:05:26,920][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 00:05:27,420][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 00:05:27,922][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 00:05:28,423][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 00:05:28,930][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10879 tokens. [2025-11-13 00:05:29,602][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.19%, Current % of VRAM taken: 59.64%, Block Peak % of device VRAM: 62.30%, ΔTime: 00:00:32 [2025-11-13 00:05:30,378][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 00:05:30,380][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 00:05:30,381][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 00:05:31,364][__main__][INFO] - Iteration 133 took 50s (28.44% Gen, 69.61% Train). Generation: 14s, Training: 35s. Estimated remaining time: 40h 17m 30s. Estimated total time: 42h 13m 19s. Time estimates for 10 more iterations: 8m 26s, 100 more iterations: 1h 24m 26s, 500 more iterations: 7h 2m 13s. [2025-11-13 00:05:31,366][__main__][INFO] - Starting iteration 133. [2025-11-13 00:05:31,846][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 13 and human policies 1. [2025-11-13 00:05:31,847][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 00:05:45,353][__main__][INFO] - Number of regex retries in iteration 133: 0 [2025-11-13 00:05:45,354][__main__][INFO] - agents played in iteration 133 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 00:05:46,126][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:05:46,148][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:05:46,172][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:05:46,204][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:05:46,205][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 00:05:46,206][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 00:05:46,802][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 00:05:47,258][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 00:05:47,777][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 00:05:48,275][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 00:05:48,774][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 00:05:49,270][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 00:05:49,769][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 00:05:50,268][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 00:05:50,768][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 00:05:51,267][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 00:05:51,777][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 00:06:03,332][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 00:06:03,834][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 00:06:04,337][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 00:06:04,841][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 00:06:05,342][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 00:06:05,841][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 00:06:06,343][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 00:06:06,846][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 00:06:07,354][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 00:06:07,852][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 00:06:08,350][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 00:06:08,863][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 00:06:09,361][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 00:06:09,881][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 00:06:10,377][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 00:06:10,873][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 00:06:11,376][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 00:06:11,877][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 00:06:12,381][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 00:06:12,882][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 00:06:13,383][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 00:06:13,885][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 00:06:14,386][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 00:06:14,887][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 00:06:15,389][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 00:06:15,891][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 00:06:16,403][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 00:06:16,906][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 00:06:17,410][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 00:06:17,917][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 00:06:18,419][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 00:06:18,926][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10878 tokens. [2025-11-13 00:06:19,609][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.27%, Current % of VRAM taken: 59.72%, Block Peak % of device VRAM: 62.42%, ΔTime: 00:00:32 [2025-11-13 00:06:20,381][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 00:06:20,384][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 00:06:20,386][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 00:06:21,238][__main__][INFO] - Iteration 134 took 49s (27.35% Gen, 70.93% Train). Generation: 13s, Training: 35s. Estimated remaining time: 39h 12m 57s. Estimated total time: 41h 9m 36s. Time estimates for 10 more iterations: 8m 13s, 100 more iterations: 1h 22m 19s, 500 more iterations: 6h 51m 36s. [2025-11-13 00:06:21,240][__main__][INFO] - Starting iteration 134. [2025-11-13 00:06:21,713][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 13 and human policies 1. [2025-11-13 00:06:21,714][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 00:06:35,356][__main__][INFO] - Number of regex retries in iteration 134: 0 [2025-11-13 00:06:35,357][__main__][INFO] - agents played in iteration 134 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 00:06:36,151][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:06:36,174][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:06:36,196][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:06:36,218][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:06:36,219][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 00:06:36,220][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 00:06:36,867][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 00:06:37,319][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 00:06:37,824][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 00:06:38,323][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 00:06:38,823][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 00:06:39,322][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 00:06:39,821][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 00:06:40,318][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 00:06:40,816][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 00:06:41,318][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 00:06:41,817][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 00:06:42,319][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 00:06:42,824][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 00:06:43,326][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 00:06:43,828][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 00:06:44,330][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 00:06:44,831][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 00:06:45,335][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 00:06:45,836][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 00:06:46,345][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 00:06:46,848][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 00:06:47,351][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 00:06:53,368][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 00:06:53,887][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 00:06:54,392][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 00:06:54,895][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 00:06:55,398][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 00:06:55,910][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 00:06:56,414][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 00:06:56,918][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 00:06:57,418][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 00:06:57,919][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 00:06:58,419][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 00:06:58,918][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 00:06:59,419][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 00:06:59,924][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 00:07:00,428][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 00:07:00,931][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 00:07:01,432][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 00:07:01,933][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 00:07:02,435][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 00:07:02,936][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 00:07:03,433][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 00:07:03,936][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 00:07:04,435][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 00:07:04,935][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 00:07:05,435][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 00:07:05,934][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 00:07:06,433][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 00:07:06,933][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 00:07:07,432][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 00:07:07,932][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 00:07:08,432][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 00:07:08,933][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10874 tokens. [2025-11-13 00:07:09,625][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.18%, Current % of VRAM taken: 59.64%, Block Peak % of device VRAM: 62.41%, ΔTime: 00:00:32 [2025-11-13 00:07:10,415][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 00:07:10,417][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 00:07:10,419][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 00:07:11,403][__main__][INFO] - Iteration 135 took 49s (27.46% Gen, 70.56% Train). Generation: 13s, Training: 35s. Estimated remaining time: 39h 27m 0s. Estimated total time: 41h 24m 30s. Time estimates for 10 more iterations: 8m 16s, 100 more iterations: 1h 22m 49s, 500 more iterations: 6h 54m 5s. [2025-11-13 00:07:11,405][__main__][INFO] - Starting iteration 135. [2025-11-13 00:07:11,908][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 13 and human policies 1. [2025-11-13 00:07:11,909][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 00:07:15,376][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 00:07:21,538][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 00:07:25,675][__main__][INFO] - Number of regex retries in iteration 135: 2 [2025-11-13 00:07:25,675][__main__][INFO] - agents played in iteration 135 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 00:07:26,585][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:07:26,608][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:07:26,632][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:07:26,654][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:07:26,654][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 00:07:26,655][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 00:07:27,302][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 00:07:27,768][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 00:07:28,274][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 00:07:28,778][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 00:07:29,282][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 00:07:29,782][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 00:07:30,290][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 00:07:30,793][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 00:07:31,294][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 00:07:31,792][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 00:07:32,290][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 00:07:32,800][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 00:07:33,303][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 00:07:33,807][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 00:07:34,309][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 00:07:34,812][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 00:07:35,314][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 00:07:35,808][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 00:07:36,313][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 00:07:36,818][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 00:07:37,317][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 00:07:37,819][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 00:07:38,317][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 00:07:38,814][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 00:07:39,316][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 00:07:39,818][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 00:07:40,320][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 00:07:40,820][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 00:07:41,322][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 00:07:41,824][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 00:07:42,326][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 00:07:42,826][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 00:07:43,331][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 00:07:43,831][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 00:07:44,331][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 00:07:44,833][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 00:07:45,334][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 00:07:45,836][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 00:07:46,340][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 00:07:46,843][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 00:07:47,345][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 00:07:47,849][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 00:07:48,353][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 00:07:48,858][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 00:07:49,363][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 00:07:49,873][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 00:07:50,374][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 00:07:50,874][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 00:07:51,384][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 00:07:51,882][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 00:07:52,397][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 00:07:52,895][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 00:07:53,395][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 00:07:53,893][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 00:07:54,392][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 00:07:54,902][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 00:07:55,402][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 00:07:55,902][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 00:07:56,403][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 00:07:56,902][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 00:07:57,405][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 00:07:57,906][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 00:07:58,406][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 00:07:58,916][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 00:07:59,418][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10877 tokens. [2025-11-13 00:08:00,129][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.22%, Current % of VRAM taken: 59.67%, Block Peak % of device VRAM: 62.50%, ΔTime: 00:00:32 [2025-11-13 00:08:00,896][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 00:08:00,897][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 00:08:00,899][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 00:08:01,870][__main__][INFO] - Iteration 136 took 49s (27.55% Gen, 70.50% Train). Generation: 13s, Training: 35s. Estimated remaining time: 39h 39m 47s. Estimated total time: 41h 38m 7s. Time estimates for 10 more iterations: 8m 19s, 100 more iterations: 1h 23m 16s, 500 more iterations: 6h 56m 21s. [2025-11-13 00:08:01,872][__main__][INFO] - Starting iteration 136. [2025-11-13 00:08:02,357][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 13 and human policies 1. [2025-11-13 00:08:02,358][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 00:08:17,276][__main__][INFO] - Number of regex retries in iteration 136: 0 [2025-11-13 00:08:17,277][__main__][INFO] - agents played in iteration 136 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 00:08:18,111][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:08:18,139][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:08:18,164][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:08:18,188][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:08:18,188][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 00:08:18,189][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 00:08:18,820][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 00:08:19,270][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 00:08:19,780][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 00:08:20,291][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 00:08:20,791][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 00:08:21,301][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 00:08:21,800][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 00:08:22,301][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 00:08:22,802][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 00:08:23,308][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 00:08:23,832][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 00:08:24,337][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 00:08:24,838][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 00:08:25,349][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 00:08:25,851][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 00:08:26,353][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 00:08:26,857][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 00:08:27,360][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 00:08:27,859][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 00:08:28,364][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 00:08:28,867][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 00:08:29,375][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 00:08:29,874][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 00:08:30,371][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 00:08:30,870][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 00:08:31,370][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 00:08:31,883][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 00:08:32,382][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 00:08:32,880][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 00:08:33,381][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 00:08:33,880][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 00:08:34,380][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 00:08:34,877][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 00:08:35,377][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 00:08:35,894][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 00:08:36,398][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 00:08:36,900][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 00:08:37,404][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 00:08:37,902][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 00:08:38,410][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 00:08:38,912][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 00:08:39,416][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 00:08:39,920][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 00:08:40,419][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 00:08:40,921][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 00:08:41,421][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 00:08:41,920][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 00:08:42,421][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 00:08:42,921][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 00:08:43,421][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 00:08:43,925][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 00:08:44,424][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 00:08:44,927][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 00:08:45,426][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 00:08:45,926][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 00:08:46,425][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 00:08:46,926][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 00:08:47,427][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 00:08:47,929][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 00:08:48,429][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 00:08:48,929][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 00:08:49,430][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 00:08:49,930][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 00:08:50,431][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 00:08:50,932][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10878 tokens. [2025-11-13 00:08:51,587][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.20%, Current % of VRAM taken: 59.66%, Block Peak % of device VRAM: 62.20%, ΔTime: 00:00:32 [2025-11-13 00:08:52,360][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 00:08:52,362][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 00:08:52,363][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 00:08:53,280][__main__][INFO] - Iteration 137 took 50s (29.30% Gen, 68.90% Train). Generation: 14s, Training: 35s. Estimated remaining time: 40h 26m 59s. Estimated total time: 42h 26m 11s. Time estimates for 10 more iterations: 8m 29s, 100 more iterations: 1h 24m 52s, 500 more iterations: 7h 4m 21s. [2025-11-13 00:08:53,282][__main__][INFO] - Starting iteration 137. [2025-11-13 00:08:53,769][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 13 and human policies 1. [2025-11-13 00:08:53,770][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 00:08:56,787][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 00:09:07,956][__main__][INFO] - Number of regex retries in iteration 137: 1 [2025-11-13 00:09:07,956][__main__][INFO] - agents played in iteration 137 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 00:09:08,772][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:09:08,797][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:09:08,823][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:09:08,845][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:09:08,846][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 00:09:08,847][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 00:09:09,474][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 00:09:09,932][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 00:09:10,437][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 00:09:10,941][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 00:09:11,442][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 00:09:11,946][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 00:09:12,457][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 00:09:12,959][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 00:09:13,469][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 00:09:13,977][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 00:09:14,483][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 00:09:26,052][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 00:09:26,555][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 00:09:27,055][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 00:09:27,558][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 00:09:28,062][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 00:09:28,565][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 00:09:29,071][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 00:09:29,574][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 00:09:30,077][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 00:09:30,577][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 00:09:31,080][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 00:09:31,600][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 00:09:32,101][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 00:09:32,602][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 00:09:33,105][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 00:09:33,605][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 00:09:34,115][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 00:09:34,614][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 00:09:35,113][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 00:09:35,614][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 00:09:36,113][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 00:09:36,614][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 00:09:37,116][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 00:09:37,617][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 00:09:38,123][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 00:09:38,623][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 00:09:39,122][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 00:09:39,623][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 00:09:40,122][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 00:09:40,625][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 00:09:41,126][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 00:09:41,628][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10878 tokens. [2025-11-13 00:09:42,292][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.21%, Current % of VRAM taken: 59.67%, Block Peak % of device VRAM: 62.35%, ΔTime: 00:00:32 [2025-11-13 00:09:43,050][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 00:09:43,052][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 00:09:43,054][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 00:09:44,140][__main__][INFO] - Iteration 138 took 50s (28.16% Gen, 69.68% Train). Generation: 14s, Training: 35s. Estimated remaining time: 39h 58m 32s. Estimated total time: 41h 58m 34s. Time estimates for 10 more iterations: 8m 23s, 100 more iterations: 1h 23m 57s, 500 more iterations: 6h 59m 45s. [2025-11-13 00:09:44,142][__main__][INFO] - Starting iteration 138. [2025-11-13 00:09:44,641][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 13 and human policies 1. [2025-11-13 00:09:44,642][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 00:09:58,017][__main__][INFO] - Number of regex retries in iteration 138: 0 [2025-11-13 00:09:58,018][__main__][INFO] - agents played in iteration 138 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 00:09:58,797][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:09:58,824][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:09:58,861][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:09:58,888][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:09:58,889][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 00:09:58,890][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 00:09:59,522][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 00:09:59,978][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 00:10:00,487][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 00:10:00,988][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 00:10:01,489][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 00:10:01,996][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 00:10:02,499][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 00:10:03,002][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 00:10:03,507][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 00:10:04,010][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 00:10:04,515][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 00:10:16,073][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 00:10:16,575][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 00:10:17,077][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 00:10:17,576][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 00:10:18,080][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 00:10:18,583][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 00:10:19,083][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 00:10:19,585][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 00:10:20,083][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 00:10:20,589][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 00:10:21,095][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 00:10:21,599][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 00:10:22,097][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 00:10:22,599][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 00:10:23,107][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 00:10:23,608][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 00:10:24,109][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 00:10:24,610][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 00:10:25,111][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 00:10:25,631][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 00:10:26,132][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 00:10:26,631][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 00:10:27,132][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 00:10:27,631][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 00:10:28,142][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 00:10:28,641][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 00:10:29,140][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 00:10:29,652][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 00:10:30,152][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 00:10:30,655][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 00:10:31,156][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 00:10:31,655][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10875 tokens. [2025-11-13 00:10:32,332][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.16%, Current % of VRAM taken: 59.61%, Block Peak % of device VRAM: 62.24%, ΔTime: 00:00:32 [2025-11-13 00:10:33,087][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 00:10:33,089][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 00:10:33,091][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 00:10:34,185][__main__][INFO] - Iteration 139 took 49s (27.00% Gen, 70.79% Train). Generation: 13s, Training: 35s. Estimated remaining time: 39h 16m 22s. Estimated total time: 41h 17m 15s. Time estimates for 10 more iterations: 8m 15s, 100 more iterations: 1h 22m 34s, 500 more iterations: 6h 52m 52s. [2025-11-13 00:10:34,187][__main__][INFO] - Starting iteration 139. [2025-11-13 00:10:34,691][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 13 and human policies 1. [2025-11-13 00:10:34,692][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 00:10:49,212][__main__][INFO] - Number of regex retries in iteration 139: 0 [2025-11-13 00:10:49,213][__main__][INFO] - agents played in iteration 139 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 00:10:49,993][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.53%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:10:50,021][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.53%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:10:50,048][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.53%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:10:50,070][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.53%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:10:50,071][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 00:10:50,072][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 00:10:50,695][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 00:10:51,152][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 00:10:51,660][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 00:10:52,162][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 00:10:52,663][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 00:10:53,170][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 00:10:53,676][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 00:10:54,179][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 00:10:54,682][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 00:10:55,189][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 00:10:55,696][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 00:10:56,199][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 00:10:56,704][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 00:10:57,211][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 00:10:57,716][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 00:10:58,221][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 00:10:58,736][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 00:10:59,238][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 00:10:59,750][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 00:11:00,253][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 00:11:00,754][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 00:11:01,254][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 00:11:01,753][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 00:11:02,256][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 00:11:02,756][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 00:11:03,256][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 00:11:03,760][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 00:11:04,266][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 00:11:04,766][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 00:11:05,268][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 00:11:05,767][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 00:11:06,269][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 00:11:06,768][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 00:11:07,268][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 00:11:07,771][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 00:11:08,272][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 00:11:08,775][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 00:11:09,276][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 00:11:09,776][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 00:11:10,275][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 00:11:10,776][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 00:11:11,278][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 00:11:11,782][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 00:11:12,283][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 00:11:12,784][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 00:11:13,285][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 00:11:13,785][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 00:11:14,291][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 00:11:14,791][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 00:11:15,291][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 00:11:15,795][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 00:11:16,297][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 00:11:16,796][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 00:11:17,298][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 00:11:17,799][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 00:11:18,302][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 00:11:18,799][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 00:11:19,302][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 00:11:19,799][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 00:11:20,296][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 00:11:20,798][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 00:11:21,297][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 00:11:21,797][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 00:11:22,298][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 00:11:22,798][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10871 tokens. [2025-11-13 00:11:23,444][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.23%, Current % of VRAM taken: 59.68%, Block Peak % of device VRAM: 62.41%, ΔTime: 00:00:32 [2025-11-13 00:11:24,207][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 00:11:24,209][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 00:11:24,211][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 00:11:25,200][__main__][INFO] - Iteration 140 took 50s (28.75% Gen, 69.29% Train). Generation: 14s, Training: 34s. Estimated remaining time: 40h 3m 45s. Estimated total time: 42h 5m 29s. Time estimates for 10 more iterations: 8m 25s, 100 more iterations: 1h 24m 10s, 500 more iterations: 7h 0m 54s. [2025-11-13 00:11:25,202][__main__][INFO] - Starting iteration 140. [2025-11-13 00:11:25,685][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 13 and human policies 1. [2025-11-13 00:11:25,685][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 00:11:40,403][__main__][INFO] - Number of regex retries in iteration 140: 0 [2025-11-13 00:11:40,404][__main__][INFO] - agents played in iteration 140 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 00:11:41,262][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:11:41,290][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:11:41,317][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:11:41,339][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:11:41,340][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 00:11:41,341][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 00:11:41,968][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 00:11:42,424][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 00:11:42,929][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 00:11:43,431][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 00:11:43,931][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 00:11:44,450][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 00:11:44,953][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 00:11:45,456][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 00:11:45,961][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 00:11:46,462][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 00:11:46,969][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 00:11:47,472][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 00:11:47,973][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 00:11:48,480][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 00:11:48,980][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 00:11:49,480][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 00:11:49,979][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 00:11:50,480][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 00:11:50,987][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 00:11:51,485][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 00:11:51,986][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 00:11:52,483][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 00:11:52,987][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 00:11:53,491][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 00:11:53,991][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 00:11:54,491][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 00:11:54,993][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 00:11:55,493][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 00:11:55,994][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 00:11:56,494][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 00:11:56,993][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 00:11:57,495][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 00:11:57,996][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 00:11:58,499][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 00:11:59,001][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 00:11:59,501][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 00:12:00,006][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 00:12:00,511][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 00:12:01,013][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 00:12:01,513][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 00:12:02,012][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 00:12:02,511][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 00:12:03,006][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 00:12:03,502][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 00:12:04,002][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 00:12:04,499][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 00:12:04,996][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 00:12:05,495][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 00:12:05,996][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 00:12:06,498][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 00:12:06,999][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 00:12:07,500][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 00:12:08,005][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 00:12:08,506][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 00:12:09,007][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 00:12:09,508][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 00:12:10,010][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 00:12:10,512][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 00:12:11,011][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 00:12:11,511][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 00:12:12,011][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 00:12:12,510][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 00:12:13,009][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 00:12:13,507][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 00:12:14,010][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10877 tokens. [2025-11-13 00:12:14,684][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.20%, Current % of VRAM taken: 59.66%, Block Peak % of device VRAM: 62.41%, ΔTime: 00:00:32 [2025-11-13 00:12:15,463][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 00:12:15,465][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 00:12:15,467][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 00:12:17,233][__main__][INFO] - Iteration 141 took 51s (28.55% Gen, 68.02% Train). Generation: 14s, Training: 35s. Estimated remaining time: 40h 54m 52s. Estimated total time: 42h 57m 28s. Time estimates for 10 more iterations: 8m 35s, 100 more iterations: 1h 25m 54s, 500 more iterations: 7h 9m 34s. [2025-11-13 00:12:17,235][__main__][INFO] - Starting iteration 141. [2025-11-13 00:12:17,717][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 14 and human policies 1. [2025-11-13 00:12:17,718][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 00:12:26,805][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 00:12:32,808][__main__][INFO] - Number of regex retries in iteration 141: 1 [2025-11-13 00:12:32,808][__main__][INFO] - agents played in iteration 141 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 00:12:33,592][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:12:33,619][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:12:33,643][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:12:33,665][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:12:33,666][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 00:12:33,667][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 00:12:34,336][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 00:12:34,794][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 00:12:35,301][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 00:12:35,800][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 00:12:36,303][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 00:12:36,805][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 00:12:37,308][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 00:12:37,812][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 00:12:38,316][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 00:12:38,824][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 00:12:39,331][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 00:12:39,835][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 00:12:40,336][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 00:12:40,847][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 00:12:41,348][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 00:12:41,856][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 00:12:42,358][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 00:12:42,860][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 00:12:43,366][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 00:12:43,867][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 00:12:44,372][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 00:12:44,876][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 00:12:50,910][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 00:12:51,414][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 00:12:51,915][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 00:12:52,415][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 00:12:52,911][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 00:12:53,410][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 00:12:53,909][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 00:12:54,407][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 00:12:54,910][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 00:12:55,410][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 00:12:55,908][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 00:12:56,410][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 00:12:56,911][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 00:12:57,413][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 00:12:57,916][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 00:12:58,422][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 00:12:58,921][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 00:12:59,420][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 00:12:59,926][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 00:13:00,427][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 00:13:00,928][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 00:13:01,428][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 00:13:01,928][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 00:13:02,426][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 00:13:02,928][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 00:13:03,429][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 00:13:03,931][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 00:13:04,432][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 00:13:04,935][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 00:13:05,435][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 00:13:05,935][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 00:13:06,436][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10877 tokens. [2025-11-13 00:13:07,107][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.23%, Current % of VRAM taken: 59.68%, Block Peak % of device VRAM: 62.28%, ΔTime: 00:00:32 [2025-11-13 00:13:07,869][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 00:13:07,871][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 00:13:07,872][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 00:13:08,821][__main__][INFO] - Iteration 142 took 51s (29.53% Gen, 68.61% Train). Generation: 15s, Training: 35s. Estimated remaining time: 40h 31m 44s. Estimated total time: 42h 35m 11s. Time estimates for 10 more iterations: 8m 31s, 100 more iterations: 1h 25m 10s, 500 more iterations: 7h 5m 51s. [2025-11-13 00:13:08,823][__main__][INFO] - Starting iteration 142. [2025-11-13 00:13:09,294][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 14 and human policies 1. [2025-11-13 00:13:09,295][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 00:13:12,619][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 00:13:12,697][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 00:13:22,987][__main__][INFO] - Number of regex retries in iteration 142: 2 [2025-11-13 00:13:22,988][__main__][INFO] - agents played in iteration 142 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 00:13:23,778][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:13:23,815][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:13:23,843][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:13:23,867][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:13:23,868][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 00:13:23,869][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 00:13:24,519][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 00:13:24,976][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 00:13:25,485][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 00:13:25,988][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 00:13:26,497][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 00:13:26,993][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 00:13:27,493][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 00:13:28,000][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 00:13:28,502][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 00:13:29,008][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 00:13:29,511][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 00:13:30,021][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 00:13:30,523][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 00:13:31,024][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 00:13:31,523][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 00:13:32,027][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 00:13:32,526][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 00:13:33,027][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 00:13:33,530][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 00:13:34,030][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 00:13:34,532][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 00:13:35,034][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 00:13:41,048][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 00:13:41,550][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 00:13:42,051][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 00:13:42,568][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 00:13:43,072][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 00:13:43,585][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 00:13:44,084][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 00:13:44,582][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 00:13:45,079][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 00:13:45,573][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 00:13:46,071][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 00:13:46,569][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 00:13:47,068][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 00:13:47,583][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 00:13:48,083][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 00:13:48,583][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 00:13:49,086][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 00:13:49,587][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 00:13:50,095][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 00:13:50,596][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 00:13:51,099][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 00:13:51,604][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 00:13:52,111][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 00:13:52,613][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 00:13:53,115][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 00:13:53,615][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 00:13:54,115][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 00:13:54,615][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 00:13:55,114][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 00:13:55,614][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 00:13:56,114][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 00:13:56,615][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10878 tokens. [2025-11-13 00:13:57,273][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.17%, Current % of VRAM taken: 59.63%, Block Peak % of device VRAM: 62.41%, ΔTime: 00:00:32 [2025-11-13 00:13:58,055][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 00:13:58,057][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 00:13:58,059][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 00:13:58,963][__main__][INFO] - Iteration 143 took 49s (27.57% Gen, 70.61% Train). Generation: 13s, Training: 35s. Estimated remaining time: 39h 19m 10s. Estimated total time: 41h 23m 28s. Time estimates for 10 more iterations: 8m 16s, 100 more iterations: 1h 22m 46s, 500 more iterations: 6h 53m 54s. [2025-11-13 00:13:58,965][__main__][INFO] - Starting iteration 143. [2025-11-13 00:13:59,438][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 14 and human policies 1. [2025-11-13 00:13:59,439][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 00:14:13,521][__main__][INFO] - Number of regex retries in iteration 143: 0 [2025-11-13 00:14:13,521][__main__][INFO] - agents played in iteration 143 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 00:14:14,329][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:14:14,352][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:14:14,374][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:14:14,396][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:14:14,397][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 00:14:14,399][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 00:14:15,082][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 00:14:15,544][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 00:14:16,054][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 00:14:16,561][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 00:14:17,064][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 00:14:17,565][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 00:14:18,069][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 00:14:18,573][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 00:14:19,087][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 00:14:19,588][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 00:14:20,090][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 00:14:20,606][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 00:14:21,109][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 00:14:21,613][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 00:14:22,113][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 00:14:22,616][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 00:14:23,119][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 00:14:23,616][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 00:14:24,117][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 00:14:24,616][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 00:14:25,116][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 00:14:25,618][mllm.training.trainer_common][INFO] - 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Processing mini-batch 43 of 64 [2025-11-13 00:14:37,217][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 00:14:37,716][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 00:14:38,215][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 00:14:38,716][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 00:14:39,218][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 00:14:39,720][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 00:14:40,223][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 00:14:40,724][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 00:14:41,227][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 00:14:41,728][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 00:14:42,229][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 00:14:42,737][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 00:14:43,237][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 00:14:43,740][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 00:14:44,240][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 00:14:44,739][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 00:14:45,240][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 00:14:45,741][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 00:14:46,241][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 00:14:46,744][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 00:14:47,244][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10879 tokens. [2025-11-13 00:14:47,910][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.22%, Current % of VRAM taken: 59.67%, Block Peak % of device VRAM: 62.29%, ΔTime: 00:00:32 [2025-11-13 00:14:48,665][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 00:14:48,666][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 00:14:48,669][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 00:14:49,647][__main__][INFO] - Iteration 144 took 50s (28.05% Gen, 70.00% Train). Generation: 14s, Training: 35s. Estimated remaining time: 39h 45m 19s. Estimated total time: 41h 50m 27s. Time estimates for 10 more iterations: 8m 22s, 100 more iterations: 1h 23m 40s, 500 more iterations: 6h 58m 24s. [2025-11-13 00:14:49,649][__main__][INFO] - Starting iteration 144. [2025-11-13 00:14:50,129][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 14 and human policies 1. [2025-11-13 00:14:50,129][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 00:15:03,343][__main__][INFO] - Number of regex retries in iteration 144: 0 [2025-11-13 00:15:03,344][__main__][INFO] - agents played in iteration 144 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 00:15:04,214][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:15:04,240][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:15:04,266][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:15:04,288][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:15:04,289][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 00:15:04,290][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 00:15:04,988][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 00:15:05,443][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 00:15:05,953][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 00:15:06,456][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 00:15:06,963][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 00:15:07,471][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 00:15:07,976][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 00:15:08,478][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 00:15:08,991][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 00:15:09,493][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 00:15:09,997][mllm.training.trainer_common][INFO] - 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Processing mini-batch 43 of 64 [2025-11-13 00:15:27,036][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 00:15:27,534][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 00:15:28,032][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 00:15:28,535][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 00:15:29,034][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 00:15:29,534][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 00:15:30,037][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 00:15:30,538][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 00:15:31,039][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 00:15:31,540][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 00:15:32,039][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 00:15:32,543][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 00:15:33,044][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 00:15:33,544][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 00:15:34,045][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 00:15:34,550][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 00:15:35,051][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 00:15:35,551][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 00:15:36,052][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 00:15:36,552][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 00:15:37,050][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10877 tokens. [2025-11-13 00:15:37,712][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.18%, Current % of VRAM taken: 59.63%, Block Peak % of device VRAM: 62.29%, ΔTime: 00:00:32 [2025-11-13 00:15:38,471][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 00:15:38,473][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 00:15:38,475][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 00:15:39,503][__main__][INFO] - Iteration 145 took 49s (26.76% Gen, 71.15% Train). Generation: 13s, Training: 35s. Estimated remaining time: 39h 2m 46s. Estimated total time: 41h 8m 44s. Time estimates for 10 more iterations: 8m 13s, 100 more iterations: 1h 22m 17s, 500 more iterations: 6h 51m 27s. [2025-11-13 00:15:39,505][__main__][INFO] - Starting iteration 145. [2025-11-13 00:15:39,987][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 14 and human policies 1. [2025-11-13 00:15:39,988][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 00:15:52,920][__main__][INFO] - Number of regex retries in iteration 145: 0 [2025-11-13 00:15:52,921][__main__][INFO] - agents played in iteration 145 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 00:15:53,766][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:15:53,792][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:15:53,818][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:15:53,840][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:15:53,841][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 00:15:53,842][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 00:15:54,465][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 00:15:54,922][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 00:15:55,426][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 00:15:55,930][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 00:15:56,435][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 00:15:56,938][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 00:15:57,447][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 00:15:57,951][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 00:15:58,457][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 00:15:58,959][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 00:15:59,464][mllm.training.trainer_common][INFO] - 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Processing mini-batch 43 of 64 [2025-11-13 00:16:16,500][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 00:16:17,001][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 00:16:17,503][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 00:16:18,003][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 00:16:18,512][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 00:16:19,010][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 00:16:19,517][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 00:16:20,016][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 00:16:20,514][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 00:16:21,014][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 00:16:21,515][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 00:16:22,016][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 00:16:22,518][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 00:16:23,019][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 00:16:23,538][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 00:16:24,036][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 00:16:24,539][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 00:16:25,036][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 00:16:25,536][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 00:16:26,038][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 00:16:26,536][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10880 tokens. [2025-11-13 00:16:27,198][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.16%, Current % of VRAM taken: 59.61%, Block Peak % of device VRAM: 62.24%, ΔTime: 00:00:32 [2025-11-13 00:16:27,985][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 00:16:27,986][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 00:16:27,988][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 00:16:28,915][__main__][INFO] - Iteration 146 took 48s (26.43% Gen, 71.67% Train). Generation: 12s, Training: 35s. Estimated remaining time: 38h 39m 38s. Estimated total time: 40h 46m 25s. Time estimates for 10 more iterations: 8m 9s, 100 more iterations: 1h 21m 32s, 500 more iterations: 6h 47m 44s. [2025-11-13 00:16:28,917][__main__][INFO] - Starting iteration 146. [2025-11-13 00:16:29,395][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 14 and human policies 1. [2025-11-13 00:16:29,396][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 00:16:32,911][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 00:16:43,245][__main__][INFO] - Number of regex retries in iteration 146: 1 [2025-11-13 00:16:43,246][__main__][INFO] - agents played in iteration 146 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 00:16:44,111][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:16:44,138][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:16:44,165][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:16:44,189][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:16:44,189][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 00:16:44,190][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 00:16:44,798][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 00:16:45,252][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 00:16:45,756][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 00:16:46,255][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 00:16:46,754][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 00:16:47,258][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 00:16:47,761][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 00:16:48,263][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 00:16:48,765][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 00:16:49,270][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 00:16:49,774][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 00:17:01,376][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 00:17:01,879][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 00:17:02,383][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 00:17:02,888][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 00:17:03,393][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 00:17:03,894][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 00:17:04,405][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 00:17:04,908][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 00:17:05,420][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 00:17:05,924][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 00:17:06,427][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 00:17:06,928][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 00:17:07,428][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 00:17:07,931][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 00:17:08,433][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 00:17:08,936][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 00:17:09,452][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 00:17:09,952][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 00:17:10,451][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 00:17:10,955][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 00:17:11,454][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 00:17:11,954][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 00:17:12,449][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 00:17:12,948][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 00:17:13,452][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 00:17:13,955][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 00:17:14,459][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 00:17:14,962][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 00:17:15,465][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 00:17:15,968][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 00:17:16,468][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 00:17:16,971][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10878 tokens. [2025-11-13 00:17:17,654][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.27%, Current % of VRAM taken: 59.73%, Block Peak % of device VRAM: 62.41%, ΔTime: 00:00:32 [2025-11-13 00:17:18,389][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 00:17:18,391][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 00:17:18,393][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 00:17:19,304][__main__][INFO] - Iteration 147 took 49s (27.75% Gen, 70.42% Train). Generation: 13s, Training: 35s. Estimated remaining time: 39h 27m 50s. Estimated total time: 41h 35m 28s. Time estimates for 10 more iterations: 8m 19s, 100 more iterations: 1h 23m 10s, 500 more iterations: 6h 55m 54s. [2025-11-13 00:17:19,306][__main__][INFO] - Starting iteration 147. [2025-11-13 00:17:19,796][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 14 and human policies 1. [2025-11-13 00:17:19,797][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 00:17:23,301][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 00:17:24,867][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 00:17:31,391][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 00:17:33,009][__main__][INFO] - Number of regex retries in iteration 147: 3 [2025-11-13 00:17:33,009][__main__][INFO] - agents played in iteration 147 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 00:17:33,931][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:17:33,958][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:17:33,985][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:17:34,007][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:17:34,008][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 00:17:34,008][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 00:17:34,625][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 00:17:35,084][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 00:17:35,589][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 00:17:36,089][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 00:17:36,589][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 00:17:37,089][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 00:17:37,590][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 00:17:38,091][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 00:17:38,594][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 00:17:39,101][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 00:17:39,603][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 00:17:40,105][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 00:17:40,606][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 00:17:41,114][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 00:17:41,617][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 00:17:42,121][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 00:17:42,630][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 00:17:43,133][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 00:17:43,643][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 00:17:44,148][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 00:17:44,650][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 00:17:45,153][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 00:17:45,654][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 00:17:46,159][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 00:17:46,660][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 00:17:47,161][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 00:17:47,664][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 00:17:48,164][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 00:17:48,665][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 00:17:49,164][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 00:17:49,665][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 00:17:50,166][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 00:17:50,666][mllm.training.trainer_common][INFO] - 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Processing mini-batch 43 of 64 [2025-11-13 00:17:56,685][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 00:17:57,188][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 00:17:57,692][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 00:17:58,198][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 00:17:58,697][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 00:17:59,197][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 00:17:59,704][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 00:18:00,202][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 00:18:00,699][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 00:18:01,200][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 00:18:01,702][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 00:18:02,204][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 00:18:02,707][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 00:18:03,207][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 00:18:03,718][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 00:18:04,215][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 00:18:04,714][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 00:18:05,213][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 00:18:05,711][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 00:18:06,214][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 00:18:06,717][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10877 tokens. [2025-11-13 00:18:07,406][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.27%, Current % of VRAM taken: 59.72%, Block Peak % of device VRAM: 62.32%, ΔTime: 00:00:32 [2025-11-13 00:18:08,175][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 00:18:08,177][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 00:18:08,179][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 00:18:09,072][__main__][INFO] - Iteration 148 took 49s (26.81% Gen, 71.37% Train). Generation: 13s, Training: 35s. Estimated remaining time: 38h 55m 20s. Estimated total time: 41h 3m 48s. Time estimates for 10 more iterations: 8m 12s, 100 more iterations: 1h 22m 7s, 500 more iterations: 6h 50m 38s. [2025-11-13 00:18:09,074][__main__][INFO] - Starting iteration 148. [2025-11-13 00:18:09,541][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 14 and human policies 1. [2025-11-13 00:18:09,542][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 00:18:16,094][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 00:18:23,623][__main__][INFO] - Number of regex retries in iteration 148: 1 [2025-11-13 00:18:23,624][__main__][INFO] - agents played in iteration 148 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 00:18:24,418][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:18:24,446][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:18:24,471][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:18:24,494][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:18:24,494][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 00:18:24,495][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 00:18:25,111][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 00:18:25,566][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 00:18:26,071][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 00:18:26,571][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 00:18:27,073][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 00:18:27,573][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 00:18:28,075][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 00:18:28,579][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 00:18:29,081][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 00:18:29,581][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 00:18:30,080][mllm.training.trainer_common][INFO] - 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Processing mini-batch 43 of 64 [2025-11-13 00:18:47,186][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 00:18:47,686][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 00:18:48,193][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 00:18:48,696][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 00:18:49,198][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 00:18:49,700][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 00:18:50,199][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 00:18:50,696][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 00:18:51,197][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 00:18:51,696][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 00:18:52,197][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 00:18:52,698][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 00:18:53,198][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 00:18:53,701][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 00:18:54,201][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 00:18:54,701][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 00:18:55,200][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 00:18:55,701][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 00:18:56,204][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 00:18:56,706][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 00:18:57,211][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10873 tokens. [2025-11-13 00:18:57,921][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.21%, Current % of VRAM taken: 59.66%, Block Peak % of device VRAM: 62.32%, ΔTime: 00:00:32 [2025-11-13 00:18:58,726][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 00:18:58,728][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 00:18:58,730][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 00:18:59,736][__main__][INFO] - Iteration 149 took 50s (28.05% Gen, 69.94% Train). Generation: 14s, Training: 35s. Estimated remaining time: 39h 40m 28s. Estimated total time: 41h 49m 46s. Time estimates for 10 more iterations: 8m 21s, 100 more iterations: 1h 23m 39s, 500 more iterations: 6h 58m 17s. [2025-11-13 00:18:59,739][__main__][INFO] - Starting iteration 149. [2025-11-13 00:19:00,232][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 14 and human policies 1. [2025-11-13 00:19:00,232][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 00:19:09,594][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 00:19:14,300][__main__][INFO] - Number of regex retries in iteration 149: 1 [2025-11-13 00:19:14,301][__main__][INFO] - agents played in iteration 149 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 00:19:15,085][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:19:15,115][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:19:15,142][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:19:15,165][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:19:15,166][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 00:19:15,166][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 00:19:15,840][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 00:19:16,300][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 00:19:16,807][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 00:19:17,335][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 00:19:17,836][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 00:19:18,340][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 00:19:18,842][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 00:19:19,341][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 00:19:19,850][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 00:19:20,349][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 00:19:20,849][mllm.training.trainer_common][INFO] - 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Processing mini-batch 43 of 64 [2025-11-13 00:19:37,978][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 00:19:38,476][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 00:19:38,975][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 00:19:39,478][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 00:19:39,979][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 00:19:40,484][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 00:19:40,987][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 00:19:41,488][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 00:19:41,990][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 00:19:42,491][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 00:19:42,991][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 00:19:43,494][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 00:19:43,995][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 00:19:44,495][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 00:19:44,995][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 00:19:45,495][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 00:19:45,998][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 00:19:46,503][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 00:19:47,004][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 00:19:47,510][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 00:19:48,011][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10879 tokens. [2025-11-13 00:19:48,727][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.23%, Current % of VRAM taken: 59.69%, Block Peak % of device VRAM: 62.41%, ΔTime: 00:00:32 [2025-11-13 00:19:49,496][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 00:19:49,498][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 00:19:49,500][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 00:19:50,482][__main__][INFO] - Iteration 150 took 50s (28.00% Gen, 70.05% Train). Generation: 14s, Training: 35s. Estimated remaining time: 39h 42m 23s. Estimated total time: 41h 52m 31s. Time estimates for 10 more iterations: 8m 22s, 100 more iterations: 1h 23m 45s, 500 more iterations: 6h 58m 45s. [2025-11-13 00:19:50,484][__main__][INFO] - Starting iteration 150. [2025-11-13 00:19:50,985][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 14 and human policies 1. [2025-11-13 00:19:50,986][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 00:20:05,748][__main__][INFO] - Number of regex retries in iteration 150: 0 [2025-11-13 00:20:05,749][__main__][INFO] - agents played in iteration 150 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 00:20:06,590][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:20:06,618][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:20:06,644][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:20:06,667][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:20:06,667][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 00:20:06,668][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 00:20:07,292][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 00:20:07,750][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 00:20:08,261][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 00:20:08,765][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 00:20:09,271][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 00:20:09,772][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 00:20:10,272][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 00:20:10,775][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 00:20:11,278][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 00:20:11,778][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 00:20:12,278][mllm.training.trainer_common][INFO] - 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Processing mini-batch 43 of 64 [2025-11-13 00:20:29,354][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 00:20:29,858][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 00:20:30,361][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 00:20:30,874][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 00:20:31,379][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 00:20:31,884][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 00:20:32,388][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 00:20:32,893][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 00:20:33,401][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 00:20:33,904][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 00:20:34,407][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 00:20:34,911][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 00:20:35,413][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 00:20:35,911][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 00:20:36,408][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 00:20:36,906][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 00:20:37,404][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 00:20:37,902][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 00:20:38,399][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 00:20:38,898][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 00:20:39,398][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10878 tokens. [2025-11-13 00:20:40,055][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.21%, Current % of VRAM taken: 59.66%, Block Peak % of device VRAM: 62.52%, ΔTime: 00:00:32 [2025-11-13 00:20:40,822][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 00:20:40,823][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 00:20:40,825][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 00:20:42,998][__main__][INFO] - Iteration 151 took 52s (28.38% Gen, 67.44% Train). Generation: 14s, Training: 35s. Estimated remaining time: 41h 9m 40s. Estimated total time: 43h 20m 41s. Time estimates for 10 more iterations: 8m 40s, 100 more iterations: 1h 26m 41s, 500 more iterations: 7h 13m 26s. [2025-11-13 00:20:43,000][__main__][INFO] - Starting iteration 151. [2025-11-13 00:20:43,491][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 15 and human policies 1. [2025-11-13 00:20:43,493][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 00:20:58,584][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 00:20:59,379][__main__][INFO] - Number of regex retries in iteration 151: 1 [2025-11-13 00:20:59,380][__main__][INFO] - agents played in iteration 151 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 00:21:00,156][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.63%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:21:00,180][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.63%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:21:00,205][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.63%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:21:00,227][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.63%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:21:00,227][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 00:21:00,228][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 00:21:00,843][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 00:21:01,298][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 00:21:01,800][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 00:21:02,300][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 00:21:02,798][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 00:21:03,297][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 00:21:03,798][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 00:21:04,303][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 00:21:04,804][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 00:21:05,303][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 00:21:05,801][mllm.training.trainer_common][INFO] - 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Processing mini-batch 54 of 64 [2025-11-13 00:21:28,398][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 00:21:28,898][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 00:21:29,397][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 00:21:29,897][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 00:21:30,396][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 00:21:30,893][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 00:21:31,401][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 00:21:31,904][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 00:21:32,404][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 00:21:32,911][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10877 tokens. [2025-11-13 00:21:33,603][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.17%, Current % of VRAM taken: 59.62%, Block Peak % of device VRAM: 62.49%, ΔTime: 00:00:32 [2025-11-13 00:21:34,369][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 00:21:34,370][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 00:21:34,372][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 00:21:35,311][__main__][INFO] - Iteration 152 took 51s (30.65% Gen, 67.53% Train). Generation: 15s, Training: 34s. Estimated remaining time: 40h 59m 9s. Estimated total time: 43h 11m 3s. Time estimates for 10 more iterations: 8m 38s, 100 more iterations: 1h 26m 22s, 500 more iterations: 7h 11m 50s. [2025-11-13 00:21:35,314][__main__][INFO] - Starting iteration 152. [2025-11-13 00:21:36,015][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 15 and human policies 1. [2025-11-13 00:21:36,015][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 00:21:50,376][__main__][INFO] - Number of regex retries in iteration 152: 0 [2025-11-13 00:21:50,377][__main__][INFO] - agents played in iteration 152 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 00:21:51,215][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:21:51,238][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:21:51,260][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:21:51,281][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:21:51,282][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 00:21:51,283][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 00:21:51,893][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 00:21:52,354][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 00:21:52,859][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 00:21:53,356][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 00:21:53,868][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 00:21:54,366][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 00:21:54,890][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 00:21:55,388][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 00:21:55,887][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 00:21:56,387][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 00:21:56,896][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 00:22:08,497][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 00:22:08,999][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 00:22:09,500][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 00:22:10,002][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 00:22:10,503][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 00:22:11,000][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 00:22:11,501][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 00:22:12,007][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 00:22:12,508][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 00:22:13,014][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 00:22:13,516][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 00:22:14,018][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 00:22:14,523][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 00:22:15,023][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 00:22:15,522][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 00:22:16,024][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 00:22:16,531][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 00:22:17,039][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 00:22:17,542][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 00:22:18,044][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 00:22:18,550][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 00:22:19,055][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 00:22:19,559][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 00:22:20,062][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 00:22:20,562][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 00:22:21,065][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 00:22:21,566][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 00:22:22,069][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 00:22:22,578][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 00:22:23,080][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 00:22:23,597][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 00:22:24,101][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10879 tokens. [2025-11-13 00:22:24,839][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.26%, Current % of VRAM taken: 59.71%, Block Peak % of device VRAM: 62.51%, ΔTime: 00:00:32 [2025-11-13 00:22:25,608][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 00:22:25,609][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 00:22:25,611][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 00:22:26,793][__main__][INFO] - Iteration 153 took 50s (28.28% Gen, 69.39% Train). Generation: 14s, Training: 35s. Estimated remaining time: 40h 6m 11s. Estimated total time: 42h 18m 56s. Time estimates for 10 more iterations: 8m 27s, 100 more iterations: 1h 24m 37s, 500 more iterations: 7h 3m 9s. [2025-11-13 00:22:26,795][__main__][INFO] - Starting iteration 153. [2025-11-13 00:22:27,272][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 15 and human policies 1. [2025-11-13 00:22:27,273][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 00:22:41,403][__main__][INFO] - Number of regex retries in iteration 153: 0 [2025-11-13 00:22:41,404][__main__][INFO] - agents played in iteration 153 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 00:22:42,355][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:22:42,382][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:22:42,408][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:22:42,430][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:22:42,431][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 00:22:42,432][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 00:22:43,093][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 00:22:43,553][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 00:22:44,059][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 00:22:44,561][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 00:22:45,061][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 00:22:45,561][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 00:22:46,069][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 00:22:46,569][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 00:22:47,068][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 00:22:47,567][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 00:22:48,065][mllm.training.trainer_common][INFO] - 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Processing mini-batch 43 of 64 [2025-11-13 00:23:05,183][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 00:23:05,699][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 00:23:06,204][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 00:23:06,708][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 00:23:07,215][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 00:23:07,719][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 00:23:08,222][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 00:23:08,726][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 00:23:09,231][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 00:23:09,733][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 00:23:10,235][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 00:23:10,737][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 00:23:11,238][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 00:23:11,737][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 00:23:12,239][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 00:23:12,740][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 00:23:13,240][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 00:23:13,743][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 00:23:14,242][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 00:23:14,741][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 00:23:15,242][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10875 tokens. [2025-11-13 00:23:15,897][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.22%, Current % of VRAM taken: 59.67%, Block Peak % of device VRAM: 62.42%, ΔTime: 00:00:32 [2025-11-13 00:23:16,682][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 00:23:16,683][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 00:23:16,685][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 00:23:17,639][__main__][INFO] - Iteration 154 took 50s (28.05% Gen, 70.05% Train). Generation: 14s, Training: 35s. Estimated remaining time: 39h 44m 47s. Estimated total time: 41h 58m 23s. Time estimates for 10 more iterations: 8m 23s, 100 more iterations: 1h 23m 56s, 500 more iterations: 6h 59m 43s. [2025-11-13 00:23:17,641][__main__][INFO] - Starting iteration 154. [2025-11-13 00:23:18,132][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 15 and human policies 1. [2025-11-13 00:23:18,133][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 00:23:21,633][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 00:23:26,172][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 00:23:29,702][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 00:23:31,434][__main__][INFO] - Number of regex retries in iteration 154: 3 [2025-11-13 00:23:31,435][__main__][INFO] - agents played in iteration 154 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 00:23:32,302][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:23:32,326][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:23:32,351][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:23:32,373][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:23:32,374][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 00:23:32,374][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 00:23:33,005][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 00:23:33,477][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 00:23:33,983][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 00:23:34,487][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 00:23:34,995][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 00:23:35,494][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 00:23:36,016][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 00:23:36,516][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 00:23:37,015][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 00:23:37,525][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 00:23:38,023][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 00:23:38,523][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 00:23:39,022][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 00:23:39,521][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 00:23:40,025][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 00:23:40,524][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 00:23:41,024][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 00:23:41,527][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 00:23:42,026][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 00:23:42,529][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 00:23:43,030][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 00:23:43,532][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 00:23:44,035][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 00:23:44,536][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 00:23:45,038][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 00:23:45,543][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 00:23:46,045][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 00:23:46,547][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 00:23:47,048][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 00:23:47,549][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 00:23:48,050][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 00:23:48,550][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 00:23:49,049][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 00:23:49,552][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 00:23:50,053][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 00:23:50,554][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 00:23:51,054][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 00:23:51,554][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 00:23:52,055][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 00:23:52,554][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 00:23:53,055][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 00:23:53,556][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 00:23:54,057][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 00:23:54,560][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 00:23:55,063][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 00:23:55,565][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 00:23:56,084][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 00:23:56,589][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 00:23:57,093][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 00:23:57,599][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 00:23:58,103][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 00:23:58,615][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 00:23:59,118][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 00:23:59,621][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 00:24:00,123][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 00:24:00,626][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 00:24:01,128][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 00:24:01,628][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 00:24:02,128][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 00:24:02,629][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 00:24:03,126][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 00:24:03,626][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 00:24:04,127][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 00:24:04,625][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 00:24:05,125][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10878 tokens. [2025-11-13 00:24:05,776][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.19%, Current % of VRAM taken: 59.64%, Block Peak % of device VRAM: 62.24%, ΔTime: 00:00:32 [2025-11-13 00:24:06,550][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 00:24:06,551][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 00:24:06,553][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 00:24:07,582][__main__][INFO] - Iteration 155 took 49s (26.90% Gen, 71.02% Train). Generation: 13s, Training: 35s. Estimated remaining time: 38h 58m 8s. Estimated total time: 41h 12m 34s. Time estimates for 10 more iterations: 8m 14s, 100 more iterations: 1h 22m 25s, 500 more iterations: 6h 52m 5s. [2025-11-13 00:24:07,584][__main__][INFO] - Starting iteration 155. [2025-11-13 00:24:08,080][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 15 and human policies 1. [2025-11-13 00:24:08,080][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 00:24:16,174][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 00:24:16,348][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 00:24:23,306][__main__][INFO] - Number of regex retries in iteration 155: 2 [2025-11-13 00:24:23,307][__main__][INFO] - agents played in iteration 155 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 00:24:24,153][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:24:24,178][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:24:24,204][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:24:24,227][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:24:24,227][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 00:24:24,228][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 00:24:24,905][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 00:24:25,366][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 00:24:25,876][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 00:24:26,389][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 00:24:26,892][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 00:24:27,394][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 00:24:27,897][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 00:24:28,399][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 00:24:28,916][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 00:24:29,417][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 00:24:29,922][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 00:24:30,419][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 00:24:30,919][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 00:24:31,422][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 00:24:31,921][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 00:24:32,421][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 00:24:32,928][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 00:24:33,428][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 00:24:33,931][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 00:24:34,432][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 00:24:34,934][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 00:24:35,438][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 00:24:35,939][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 00:24:36,441][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 00:24:36,947][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 00:24:37,449][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 00:24:37,954][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 00:24:38,456][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 00:24:38,958][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 00:24:39,461][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 00:24:39,962][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 00:24:40,463][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 00:24:40,962][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 00:24:41,461][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 00:24:41,962][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 00:24:42,460][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 00:24:42,959][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 00:24:43,460][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 00:24:43,960][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 00:24:44,463][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 00:24:44,963][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 00:24:45,466][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 00:24:45,970][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 00:24:46,473][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 00:24:46,974][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 00:24:47,476][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 00:24:47,979][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 00:24:48,483][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 00:24:48,988][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 00:24:49,490][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 00:24:49,995][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 00:24:50,497][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 00:24:50,997][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 00:24:51,510][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 00:24:52,012][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 00:24:52,524][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 00:24:53,030][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 00:24:53,533][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 00:24:54,038][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 00:24:54,540][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 00:24:55,040][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 00:24:55,543][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 00:24:56,044][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 00:24:56,548][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 00:24:57,052][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10878 tokens. [2025-11-13 00:24:57,724][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.27%, Current % of VRAM taken: 59.72%, Block Peak % of device VRAM: 62.41%, ΔTime: 00:00:32 [2025-11-13 00:24:58,510][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 00:24:58,512][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 00:24:58,514][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 00:24:59,694][__main__][INFO] - Iteration 156 took 51s (29.50% Gen, 68.21% Train). Generation: 15s, Training: 35s. Estimated remaining time: 40h 45m 25s. Estimated total time: 43h 0m 43s. Time estimates for 10 more iterations: 8m 36s, 100 more iterations: 1h 26m 1s, 500 more iterations: 7h 10m 7s. [2025-11-13 00:24:59,696][__main__][INFO] - Starting iteration 156. [2025-11-13 00:25:00,183][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 15 and human policies 1. [2025-11-13 00:25:00,183][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 00:25:04,339][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 00:25:14,112][__main__][INFO] - Number of regex retries in iteration 156: 1 [2025-11-13 00:25:14,112][__main__][INFO] - agents played in iteration 156 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 00:25:15,053][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:25:15,081][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:25:15,107][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:25:15,129][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:25:15,130][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 00:25:15,131][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 00:25:15,749][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 00:25:16,220][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 00:25:16,727][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 00:25:17,229][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 00:25:17,736][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 00:25:18,239][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 00:25:18,741][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 00:25:19,239][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 00:25:19,737][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 00:25:20,239][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 00:25:20,739][mllm.training.trainer_common][INFO] - 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Processing mini-batch 21 of 64 [2025-11-13 00:25:26,739][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 00:25:27,240][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 00:25:27,741][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 00:25:28,249][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 00:25:28,766][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 00:25:29,268][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 00:25:29,771][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 00:25:30,274][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 00:25:30,782][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 00:25:31,283][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 00:25:31,800][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 00:25:32,302][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 00:25:32,804][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 00:25:33,309][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 00:25:33,809][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 00:25:34,310][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 00:25:34,814][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 00:25:35,314][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 00:25:35,816][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 00:25:36,316][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 00:25:36,818][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 00:25:37,333][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 00:25:37,834][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 00:25:38,334][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 00:25:38,834][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 00:25:39,336][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 00:25:39,840][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 00:25:40,341][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 00:25:40,850][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 00:25:41,356][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 00:25:41,861][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 00:25:42,364][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 00:25:42,867][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 00:25:43,371][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 00:25:43,875][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 00:25:44,377][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 00:25:44,880][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 00:25:45,386][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 00:25:45,890][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 00:25:46,396][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 00:25:46,899][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 00:25:47,400][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 00:25:47,905][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10879 tokens. [2025-11-13 00:25:48,552][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.25%, Current % of VRAM taken: 59.71%, Block Peak % of device VRAM: 62.30%, ΔTime: 00:00:32 [2025-11-13 00:25:49,345][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 00:25:49,346][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 00:25:49,348][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 00:25:50,322][__main__][INFO] - Iteration 157 took 50s (27.78% Gen, 70.27% Train). Generation: 13s, Training: 35s. Estimated remaining time: 39h 30m 50s. Estimated total time: 41h 46m 59s. Time estimates for 10 more iterations: 8m 21s, 100 more iterations: 1h 23m 33s, 500 more iterations: 6h 57m 49s. [2025-11-13 00:25:50,325][__main__][INFO] - Starting iteration 157. [2025-11-13 00:25:50,806][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 15 and human policies 1. [2025-11-13 00:25:50,807][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 00:25:54,796][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 00:26:05,361][__main__][INFO] - Number of regex retries in iteration 157: 1 [2025-11-13 00:26:05,362][__main__][INFO] - agents played in iteration 157 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 00:26:06,145][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:26:06,172][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:26:06,196][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:26:06,220][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:26:06,220][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 00:26:06,222][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 00:26:06,883][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 00:26:07,343][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 00:26:07,851][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 00:26:08,355][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 00:26:08,852][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 00:26:09,349][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 00:26:09,849][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 00:26:10,347][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 00:26:10,846][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 00:26:11,358][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 00:26:11,861][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 00:26:12,363][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 00:26:12,866][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 00:26:13,367][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 00:26:13,869][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 00:26:14,369][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 00:26:14,869][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 00:26:15,371][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 00:26:15,873][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 00:26:16,373][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 00:26:16,872][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 00:26:17,372][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 00:26:23,401][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 00:26:23,903][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 00:26:24,416][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 00:26:24,916][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 00:26:25,426][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 00:26:25,926][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 00:26:26,425][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 00:26:26,938][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 00:26:27,440][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 00:26:27,940][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 00:26:28,440][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 00:26:28,940][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 00:26:29,444][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 00:26:29,944][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 00:26:30,441][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 00:26:30,940][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 00:26:31,441][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 00:26:31,941][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 00:26:32,441][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 00:26:32,941][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 00:26:33,445][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 00:26:33,946][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 00:26:34,450][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 00:26:34,957][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 00:26:35,461][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 00:26:35,964][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 00:26:36,470][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 00:26:36,975][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 00:26:37,482][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 00:26:37,984][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 00:26:38,486][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 00:26:38,989][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10877 tokens. [2025-11-13 00:26:39,661][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.19%, Current % of VRAM taken: 59.65%, Block Peak % of device VRAM: 62.30%, ΔTime: 00:00:32 [2025-11-13 00:26:40,442][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 00:26:40,445][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 00:26:40,446][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 00:26:41,406][__main__][INFO] - Iteration 158 took 50s (28.76% Gen, 69.34% Train). Generation: 14s, Training: 35s. Estimated remaining time: 39h 53m 1s. Estimated total time: 42h 10m 1s. Time estimates for 10 more iterations: 8m 26s, 100 more iterations: 1h 24m 20s, 500 more iterations: 7h 1m 40s. [2025-11-13 00:26:41,409][__main__][INFO] - Starting iteration 158. [2025-11-13 00:26:41,902][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 15 and human policies 1. [2025-11-13 00:26:41,903][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 00:26:46,326][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 00:26:46,405][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 00:26:50,162][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 00:26:57,361][__main__][INFO] - Number of regex retries in iteration 158: 3 [2025-11-13 00:26:57,362][__main__][INFO] - agents played in iteration 158 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 00:26:58,217][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:26:58,246][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:26:58,274][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:26:58,298][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:26:58,298][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 00:26:58,299][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 00:26:58,931][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 00:26:59,384][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 00:26:59,884][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 00:27:00,382][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 00:27:00,879][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 00:27:01,378][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 00:27:01,875][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 00:27:02,373][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 00:27:02,870][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 00:27:03,373][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 00:27:03,877][mllm.training.trainer_common][INFO] - 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Processing mini-batch 21 of 64 [2025-11-13 00:27:09,893][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 00:27:10,395][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 00:27:10,897][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 00:27:11,399][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 00:27:11,905][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 00:27:12,408][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 00:27:12,908][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 00:27:13,412][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 00:27:13,915][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 00:27:14,419][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 00:27:14,928][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 00:27:15,428][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 00:27:15,944][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 00:27:16,445][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 00:27:16,943][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 00:27:17,445][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 00:27:17,944][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 00:27:18,445][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 00:27:18,945][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 00:27:19,443][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 00:27:19,956][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 00:27:20,456][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 00:27:20,958][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 00:27:21,457][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 00:27:21,957][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 00:27:22,459][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 00:27:22,963][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 00:27:23,465][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 00:27:23,968][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 00:27:24,469][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 00:27:24,972][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 00:27:25,476][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 00:27:25,980][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 00:27:26,481][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 00:27:26,982][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 00:27:27,486][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 00:27:27,995][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 00:27:28,499][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 00:27:29,004][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 00:27:29,506][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 00:27:30,006][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 00:27:30,510][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 00:27:31,015][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10878 tokens. [2025-11-13 00:27:31,743][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.26%, Current % of VRAM taken: 59.71%, Block Peak % of device VRAM: 62.41%, ΔTime: 00:00:32 [2025-11-13 00:27:32,484][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 00:27:32,486][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 00:27:32,487][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 00:27:33,554][__main__][INFO] - Iteration 159 took 51s (29.93% Gen, 68.00% Train). Generation: 15s, Training: 35s. Estimated remaining time: 40h 44m 44s. Estimated total time: 43h 2m 36s. Time estimates for 10 more iterations: 8m 36s, 100 more iterations: 1h 26m 5s, 500 more iterations: 7h 10m 26s. [2025-11-13 00:27:33,556][__main__][INFO] - Starting iteration 159. [2025-11-13 00:27:34,060][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 15 and human policies 1. [2025-11-13 00:27:34,061][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 00:27:47,418][__main__][INFO] - Number of regex retries in iteration 159: 0 [2025-11-13 00:27:47,419][__main__][INFO] - agents played in iteration 159 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 00:27:48,323][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:27:48,350][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:27:48,375][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:27:48,399][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:27:48,400][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 00:27:48,401][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 00:27:49,031][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 00:27:49,487][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 00:27:49,997][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 00:27:50,499][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 00:27:51,000][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 00:27:51,502][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 00:27:51,999][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 00:27:52,497][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 00:27:52,996][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 00:27:53,496][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 00:27:53,995][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 00:27:54,493][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 00:27:54,994][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 00:27:55,494][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 00:27:55,994][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 00:27:56,493][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 00:27:56,992][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 00:27:57,493][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 00:27:57,992][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 00:27:58,491][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 00:27:58,991][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 00:27:59,494][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 00:28:05,510][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 00:28:06,019][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 00:28:06,523][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 00:28:07,023][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 00:28:07,523][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 00:28:08,022][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 00:28:08,538][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 00:28:09,038][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 00:28:09,542][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 00:28:10,044][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 00:28:10,546][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 00:28:11,045][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 00:28:11,549][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 00:28:12,050][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 00:28:12,560][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 00:28:13,061][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 00:28:13,563][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 00:28:14,061][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 00:28:14,565][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 00:28:15,068][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 00:28:15,566][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 00:28:16,064][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 00:28:16,570][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 00:28:17,074][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 00:28:17,575][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 00:28:18,078][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 00:28:18,578][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 00:28:19,079][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 00:28:19,581][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 00:28:20,084][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 00:28:20,589][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 00:28:21,091][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10879 tokens. [2025-11-13 00:28:21,791][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.19%, Current % of VRAM taken: 59.65%, Block Peak % of device VRAM: 62.24%, ΔTime: 00:00:32 [2025-11-13 00:28:22,559][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 00:28:22,561][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 00:28:22,562][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 00:28:23,459][__main__][INFO] - Iteration 160 took 49s (27.04% Gen, 71.14% Train). Generation: 13s, Training: 35s. Estimated remaining time: 38h 51m 18s. Estimated total time: 41h 10m 0s. Time estimates for 10 more iterations: 8m 14s, 100 more iterations: 1h 22m 20s, 500 more iterations: 6h 51m 40s. [2025-11-13 00:28:23,461][__main__][INFO] - Starting iteration 160. [2025-11-13 00:28:23,948][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 15 and human policies 1. [2025-11-13 00:28:23,949][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 00:28:34,553][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 00:28:37,079][__main__][INFO] - Number of regex retries in iteration 160: 1 [2025-11-13 00:28:37,080][__main__][INFO] - agents played in iteration 160 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 00:28:37,934][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:28:37,967][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:28:37,995][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:28:38,019][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:28:38,019][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 00:28:38,020][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 00:28:38,712][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 00:28:39,172][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 00:28:39,679][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 00:28:40,180][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 00:28:40,681][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 00:28:41,183][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 00:28:41,684][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 00:28:42,184][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 00:28:42,685][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 00:28:43,186][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 00:28:43,686][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 00:28:55,190][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 00:28:55,693][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 00:28:56,195][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 00:28:56,698][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 00:28:57,202][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 00:28:57,704][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 00:28:58,207][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 00:28:58,712][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 00:28:59,215][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 00:28:59,716][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 00:29:00,217][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 00:29:00,718][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 00:29:01,234][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 00:29:01,736][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 00:29:02,247][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 00:29:02,748][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 00:29:03,248][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 00:29:03,750][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 00:29:04,250][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 00:29:04,753][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 00:29:05,254][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 00:29:05,755][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 00:29:06,268][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 00:29:06,769][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 00:29:07,272][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 00:29:07,776][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 00:29:08,277][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 00:29:08,780][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 00:29:09,284][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 00:29:09,785][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 00:29:10,287][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 00:29:10,787][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10880 tokens. [2025-11-13 00:29:11,510][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.18%, Current % of VRAM taken: 59.63%, Block Peak % of device VRAM: 62.31%, ΔTime: 00:00:32 [2025-11-13 00:29:12,279][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 00:29:12,281][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 00:29:12,283][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 00:29:14,205][__main__][INFO] - Iteration 161 took 50s (26.13% Gen, 70.04% Train). Generation: 13s, Training: 35s. Estimated remaining time: 39h 33m 19s. Estimated total time: 41h 52m 52s. Time estimates for 10 more iterations: 8m 22s, 100 more iterations: 1h 23m 45s, 500 more iterations: 6h 58m 48s. [2025-11-13 00:29:14,207][__main__][INFO] - Starting iteration 161. [2025-11-13 00:29:14,707][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 16 and human policies 1. [2025-11-13 00:29:14,708][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 00:29:22,505][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 00:29:30,003][__main__][INFO] - Number of regex retries in iteration 161: 1 [2025-11-13 00:29:30,003][__main__][INFO] - agents played in iteration 161 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 00:29:30,823][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:29:30,848][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:29:30,872][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:29:30,895][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:29:30,895][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 00:29:30,896][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 00:29:31,561][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 00:29:32,019][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 00:29:32,528][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 00:29:33,034][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 00:29:33,540][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 00:29:34,057][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 00:29:34,559][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 00:29:35,061][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 00:29:35,565][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 00:29:36,066][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 00:29:36,570][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 00:29:37,070][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 00:29:37,570][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 00:29:38,072][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 00:29:38,571][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 00:29:39,075][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 00:29:39,575][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 00:29:40,075][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 00:29:40,581][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 00:29:41,079][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 00:29:41,580][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 00:29:42,082][mllm.training.trainer_common][INFO] - 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Processing mini-batch 43 of 64 [2025-11-13 00:29:53,671][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 00:29:54,170][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 00:29:54,669][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 00:29:55,168][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 00:29:55,669][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 00:29:56,170][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 00:29:56,673][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 00:29:57,173][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 00:29:57,682][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 00:29:58,185][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 00:29:58,685][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 00:29:59,184][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 00:29:59,684][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 00:30:00,187][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 00:30:00,686][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 00:30:01,184][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 00:30:01,689][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 00:30:02,193][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 00:30:02,693][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 00:30:03,191][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 00:30:03,689][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10876 tokens. [2025-11-13 00:30:04,418][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.17%, Current % of VRAM taken: 59.63%, Block Peak % of device VRAM: 62.41%, ΔTime: 00:00:32 [2025-11-13 00:30:05,168][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 00:30:05,169][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 00:30:05,171][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 00:30:06,139][__main__][INFO] - Iteration 162 took 51s (29.74% Gen, 68.38% Train). Generation: 15s, Training: 35s. Estimated remaining time: 40h 31m 11s. Estimated total time: 42h 51m 36s. Time estimates for 10 more iterations: 8m 34s, 100 more iterations: 1h 25m 43s, 500 more iterations: 7h 8m 36s. [2025-11-13 00:30:06,141][__main__][INFO] - Starting iteration 162. [2025-11-13 00:30:06,641][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 16 and human policies 1. [2025-11-13 00:30:06,642][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 00:30:10,558][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 00:30:21,184][__main__][INFO] - Number of regex retries in iteration 162: 1 [2025-11-13 00:30:21,184][__main__][INFO] - agents played in iteration 162 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 00:30:22,018][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:30:22,046][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:30:22,073][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:30:22,096][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:30:22,097][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 00:30:22,098][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 00:30:22,767][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 00:30:23,224][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 00:30:23,730][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 00:30:24,240][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 00:30:24,741][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 00:30:25,241][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 00:30:25,739][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 00:30:26,241][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 00:30:26,743][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 00:30:27,245][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 00:30:27,744][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 00:30:39,311][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 00:30:39,815][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 00:30:40,318][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 00:30:40,822][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 00:30:41,324][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 00:30:41,826][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 00:30:42,331][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 00:30:42,834][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 00:30:43,337][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 00:30:43,862][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 00:30:44,367][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 00:30:44,879][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 00:30:45,382][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 00:30:45,882][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 00:30:46,396][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 00:30:46,909][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 00:30:47,417][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 00:30:47,928][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 00:30:48,438][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 00:30:48,940][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 00:30:49,441][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 00:30:49,941][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 00:30:50,449][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 00:30:50,950][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 00:30:51,448][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 00:30:51,947][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 00:30:52,446][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 00:30:52,949][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 00:30:53,449][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 00:30:53,950][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 00:30:54,462][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 00:30:54,963][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10877 tokens. [2025-11-13 00:30:55,624][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.23%, Current % of VRAM taken: 59.68%, Block Peak % of device VRAM: 62.34%, ΔTime: 00:00:32 [2025-11-13 00:30:56,386][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 00:30:56,387][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 00:30:56,389][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 00:30:57,358][__main__][INFO] - Iteration 163 took 50s (28.67% Gen, 69.42% Train). Generation: 14s, Training: 35s. Estimated remaining time: 39h 54m 34s. Estimated total time: 42h 15m 50s. Time estimates for 10 more iterations: 8m 27s, 100 more iterations: 1h 24m 31s, 500 more iterations: 7h 2m 38s. [2025-11-13 00:30:57,360][__main__][INFO] - Starting iteration 163. [2025-11-13 00:30:57,848][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 16 and human policies 1. [2025-11-13 00:30:57,848][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 00:31:02,942][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 00:31:11,298][__main__][INFO] - Number of regex retries in iteration 163: 1 [2025-11-13 00:31:11,299][__main__][INFO] - agents played in iteration 163 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 00:31:12,195][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:31:12,222][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:31:12,248][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:31:12,271][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:31:12,271][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 00:31:12,272][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 00:31:12,922][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 00:31:13,379][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 00:31:13,895][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 00:31:14,397][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 00:31:14,901][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 00:31:15,404][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 00:31:15,906][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 00:31:16,409][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 00:31:16,911][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 00:31:17,414][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 00:31:17,916][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 00:31:18,415][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 00:31:18,916][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 00:31:19,416][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 00:31:19,917][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 00:31:20,421][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 00:31:20,922][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 00:31:21,422][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 00:31:21,922][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 00:31:22,421][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 00:31:22,920][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 00:31:23,418][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 00:31:29,421][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 00:31:29,921][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 00:31:30,423][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 00:31:30,927][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 00:31:31,430][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 00:31:31,932][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 00:31:32,435][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 00:31:32,935][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 00:31:33,438][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 00:31:33,939][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 00:31:34,441][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 00:31:34,946][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 00:31:35,447][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 00:31:35,948][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 00:31:36,448][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 00:31:36,947][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 00:31:37,449][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 00:31:37,952][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 00:31:38,456][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 00:31:38,971][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 00:31:39,472][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 00:31:39,984][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 00:31:40,485][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 00:31:40,986][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 00:31:41,501][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 00:31:42,003][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 00:31:42,506][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 00:31:43,004][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 00:31:43,506][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 00:31:44,012][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 00:31:44,513][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 00:31:45,013][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10878 tokens. [2025-11-13 00:31:45,684][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.19%, Current % of VRAM taken: 59.64%, Block Peak % of device VRAM: 62.31%, ΔTime: 00:00:32 [2025-11-13 00:31:46,453][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 00:31:46,455][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 00:31:46,457][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 00:31:47,438][__main__][INFO] - Iteration 164 took 49s (27.12% Gen, 70.90% Train). Generation: 13s, Training: 35s. Estimated remaining time: 38h 57m 26s. Estimated total time: 41h 19m 32s. Time estimates for 10 more iterations: 8m 15s, 100 more iterations: 1h 22m 39s, 500 more iterations: 6h 53m 15s. [2025-11-13 00:31:47,440][__main__][INFO] - Starting iteration 164. [2025-11-13 00:31:47,934][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 16 and human policies 1. [2025-11-13 00:31:47,934][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 00:32:01,945][__main__][INFO] - Number of regex retries in iteration 164: 0 [2025-11-13 00:32:01,946][__main__][INFO] - agents played in iteration 164 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 00:32:02,768][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:32:02,791][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:32:02,813][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:32:02,834][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:32:02,835][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 00:32:02,836][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 00:32:03,481][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 00:32:03,936][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 00:32:04,443][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 00:32:04,943][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 00:32:05,444][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 00:32:05,944][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 00:32:06,447][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 00:32:06,950][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 00:32:07,450][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 00:32:07,949][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 00:32:08,453][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 00:32:20,004][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 00:32:20,502][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 00:32:21,002][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 00:32:21,501][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 00:32:22,014][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 00:32:22,517][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 00:32:23,021][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 00:32:23,522][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 00:32:24,024][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 00:32:24,528][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 00:32:25,029][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 00:32:25,531][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 00:32:26,035][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 00:32:26,538][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 00:32:27,040][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 00:32:27,543][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 00:32:28,042][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 00:32:28,543][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 00:32:29,040][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 00:32:29,540][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 00:32:30,040][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 00:32:30,542][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 00:32:31,043][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 00:32:31,544][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 00:32:32,042][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 00:32:32,544][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 00:32:33,046][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 00:32:33,545][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 00:32:34,044][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 00:32:34,545][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 00:32:35,046][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 00:32:35,546][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10879 tokens. [2025-11-13 00:32:36,224][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.16%, Current % of VRAM taken: 59.61%, Block Peak % of device VRAM: 62.24%, ΔTime: 00:00:32 [2025-11-13 00:32:36,980][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 00:32:36,982][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 00:32:36,983][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 00:32:38,027][__main__][INFO] - Iteration 165 took 50s (27.97% Gen, 69.94% Train). Generation: 14s, Training: 35s. Estimated remaining time: 39h 21m 46s. Estimated total time: 41h 44m 42s. Time estimates for 10 more iterations: 8m 20s, 100 more iterations: 1h 23m 29s, 500 more iterations: 6h 57m 27s. [2025-11-13 00:32:38,029][__main__][INFO] - Starting iteration 165. [2025-11-13 00:32:38,520][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 16 and human policies 1. [2025-11-13 00:32:38,521][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 00:32:48,944][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 00:32:52,490][__main__][INFO] - Number of regex retries in iteration 165: 1 [2025-11-13 00:32:52,491][__main__][INFO] - agents played in iteration 165 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 00:32:53,296][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.53%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:32:53,322][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.53%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:32:53,344][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.53%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:32:53,366][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.53%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:32:53,367][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 00:32:53,368][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 00:32:54,018][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 00:32:54,476][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 00:32:54,982][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 00:32:55,483][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 00:32:56,003][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 00:32:56,503][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 00:32:57,004][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 00:32:57,507][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 00:32:58,008][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 00:32:58,521][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 00:32:59,027][mllm.training.trainer_common][INFO] - 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Processing mini-batch 54 of 64 [2025-11-13 00:33:21,638][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 00:33:22,143][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 00:33:22,643][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 00:33:23,142][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 00:33:23,644][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 00:33:24,146][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 00:33:24,646][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 00:33:25,152][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 00:33:25,652][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 00:33:26,152][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10877 tokens. [2025-11-13 00:33:26,826][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.22%, Current % of VRAM taken: 59.67%, Block Peak % of device VRAM: 62.41%, ΔTime: 00:00:32 [2025-11-13 00:33:27,613][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 00:33:27,615][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 00:33:27,616][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 00:33:28,530][__main__][INFO] - Iteration 166 took 50s (27.93% Gen, 70.24% Train). Generation: 13s, Training: 35s. Estimated remaining time: 39h 16m 42s. Estimated total time: 41h 40m 29s. Time estimates for 10 more iterations: 8m 20s, 100 more iterations: 1h 23m 20s, 500 more iterations: 6h 56m 44s. [2025-11-13 00:33:28,532][__main__][INFO] - Starting iteration 166. [2025-11-13 00:33:29,021][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 16 and human policies 1. [2025-11-13 00:33:29,021][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 00:33:42,987][__main__][INFO] - Number of regex retries in iteration 166: 0 [2025-11-13 00:33:42,988][__main__][INFO] - agents played in iteration 166 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 00:33:43,855][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:33:43,882][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:33:43,908][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:33:43,931][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:33:43,932][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 00:33:43,933][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 00:33:44,583][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 00:33:45,041][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 00:33:45,548][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 00:33:46,051][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 00:33:46,555][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 00:33:47,059][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 00:33:47,561][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 00:33:48,063][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 00:33:48,569][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 00:33:49,067][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 00:33:49,567][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 00:34:01,091][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 00:34:01,603][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 00:34:02,103][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 00:34:02,606][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 00:34:03,114][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 00:34:03,613][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 00:34:04,122][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 00:34:04,622][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 00:34:05,119][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 00:34:05,629][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 00:34:06,124][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 00:34:06,624][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 00:34:07,124][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 00:34:07,622][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 00:34:08,132][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 00:34:08,631][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 00:34:09,133][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 00:34:09,635][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 00:34:10,137][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 00:34:10,640][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 00:34:11,143][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 00:34:11,645][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 00:34:12,150][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 00:34:12,651][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 00:34:13,153][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 00:34:13,657][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 00:34:14,157][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 00:34:14,661][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 00:34:15,164][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 00:34:15,665][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 00:34:16,169][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 00:34:16,672][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10877 tokens. [2025-11-13 00:34:17,353][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.18%, Current % of VRAM taken: 59.63%, Block Peak % of device VRAM: 62.28%, ΔTime: 00:00:32 [2025-11-13 00:34:18,112][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 00:34:18,114][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 00:34:18,116][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 00:34:19,102][__main__][INFO] - Iteration 167 took 50s (27.89% Gen, 70.14% Train). Generation: 13s, Training: 35s. Estimated remaining time: 39h 19m 27s. Estimated total time: 41h 44m 4s. Time estimates for 10 more iterations: 8m 20s, 100 more iterations: 1h 23m 28s, 500 more iterations: 6h 57m 20s. [2025-11-13 00:34:19,104][__main__][INFO] - Starting iteration 167. [2025-11-13 00:34:19,606][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 16 and human policies 1. [2025-11-13 00:34:19,607][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 00:34:31,178][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 00:34:34,856][__main__][INFO] - Number of regex retries in iteration 167: 1 [2025-11-13 00:34:34,857][__main__][INFO] - agents played in iteration 167 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 00:34:35,738][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:34:35,763][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:34:35,788][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:34:35,810][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:34:35,811][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 00:34:35,812][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 00:34:36,490][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 00:34:36,948][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 00:34:37,458][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 00:34:37,961][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 00:34:38,467][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 00:34:38,968][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 00:34:39,471][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 00:34:39,971][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 00:34:40,473][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 00:34:40,974][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 00:34:41,479][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 00:34:41,985][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 00:34:42,493][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 00:34:42,994][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 00:34:43,499][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 00:34:44,001][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 00:34:44,505][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 00:34:45,024][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 00:34:45,523][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 00:34:46,024][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 00:34:46,525][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 00:34:47,024][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 00:34:47,532][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 00:34:48,034][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 00:34:48,541][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 00:34:49,042][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 00:34:49,542][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 00:34:50,045][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 00:34:50,545][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 00:34:51,046][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 00:34:51,553][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 00:34:52,052][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 00:34:52,553][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 00:34:53,056][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 00:34:53,558][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 00:34:54,064][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 00:34:54,566][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 00:34:55,064][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 00:34:55,567][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 00:34:56,071][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 00:34:56,577][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 00:34:57,088][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 00:34:57,597][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 00:34:58,119][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 00:34:58,625][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 00:34:59,126][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 00:34:59,629][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 00:35:00,132][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 00:35:00,639][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 00:35:01,142][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 00:35:01,645][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 00:35:02,150][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 00:35:02,651][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 00:35:03,154][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 00:35:03,659][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 00:35:04,160][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 00:35:04,662][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 00:35:05,163][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 00:35:05,663][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 00:35:06,168][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 00:35:06,667][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 00:35:07,168][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 00:35:07,668][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 00:35:08,169][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 00:35:08,670][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10877 tokens. [2025-11-13 00:35:09,325][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.18%, Current % of VRAM taken: 59.64%, Block Peak % of device VRAM: 62.31%, ΔTime: 00:00:32 [2025-11-13 00:35:10,087][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 00:35:10,089][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 00:35:10,091][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 00:35:10,998][__main__][INFO] - Iteration 168 took 51s (29.67% Gen, 68.56% Train). Generation: 15s, Training: 35s. Estimated remaining time: 40h 24m 8s. Estimated total time: 42h 49m 37s. Time estimates for 10 more iterations: 8m 33s, 100 more iterations: 1h 25m 39s, 500 more iterations: 7h 8m 16s. [2025-11-13 00:35:11,000][__main__][INFO] - Starting iteration 168. [2025-11-13 00:35:11,480][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 16 and human policies 1. [2025-11-13 00:35:11,480][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 00:35:15,185][mllm.models.large_language_model_local][WARNING] - Response Proposal: 5 hats, 0 books, 5 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 00:35:24,637][__main__][INFO] - Number of regex retries in iteration 168: 1 [2025-11-13 00:35:24,637][__main__][INFO] - agents played in iteration 168 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 00:35:25,488][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:35:25,512][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:35:25,536][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:35:25,558][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:35:25,558][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 00:35:25,559][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 00:35:26,191][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 00:35:26,643][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 00:35:27,148][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 00:35:27,643][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 00:35:28,143][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 00:35:28,646][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 00:35:29,143][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 00:35:29,641][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 00:35:30,144][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 00:35:30,653][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 00:35:31,164][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 00:35:42,760][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 00:35:43,262][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 00:35:43,763][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 00:35:44,264][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 00:35:44,762][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 00:35:45,262][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 00:35:45,763][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 00:35:46,264][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 00:35:46,764][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 00:35:47,264][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 00:35:47,765][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 00:35:48,269][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 00:35:48,769][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 00:35:49,269][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 00:35:49,771][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 00:35:50,272][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 00:35:50,805][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 00:35:51,309][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 00:35:51,813][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 00:35:52,330][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 00:35:52,835][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 00:35:53,339][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 00:35:53,844][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 00:35:54,343][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 00:35:54,850][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 00:35:55,350][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 00:35:55,850][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 00:35:56,353][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 00:35:56,855][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 00:35:57,360][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 00:35:57,864][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 00:35:58,373][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10880 tokens. [2025-11-13 00:35:59,028][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.28%, Current % of VRAM taken: 59.73%, Block Peak % of device VRAM: 62.43%, ΔTime: 00:00:32 [2025-11-13 00:35:59,802][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 00:35:59,803][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 00:35:59,805][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 00:36:00,724][__main__][INFO] - Iteration 169 took 49s (26.72% Gen, 71.41% Train). Generation: 13s, Training: 35s. Estimated remaining time: 38h 35m 54s. Estimated total time: 41h 2m 13s. Time estimates for 10 more iterations: 8m 12s, 100 more iterations: 1h 22m 4s, 500 more iterations: 6h 50m 22s. [2025-11-13 00:36:00,726][__main__][INFO] - Starting iteration 169. [2025-11-13 00:36:01,214][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 16 and human policies 1. [2025-11-13 00:36:01,215][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 00:36:07,548][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 00:36:16,759][__main__][INFO] - Number of regex retries in iteration 169: 1 [2025-11-13 00:36:16,760][__main__][INFO] - agents played in iteration 169 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 00:36:17,586][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:36:17,611][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:36:17,636][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:36:17,658][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:36:17,659][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 00:36:17,660][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 00:36:18,301][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 00:36:18,755][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 00:36:19,259][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 00:36:19,761][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 00:36:20,261][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 00:36:20,761][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 00:36:21,266][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 00:36:21,765][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 00:36:22,265][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 00:36:22,766][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 00:36:23,270][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 00:36:23,773][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 00:36:24,276][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 00:36:24,783][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 00:36:25,285][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 00:36:25,785][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 00:36:26,288][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 00:36:26,792][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 00:36:27,296][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 00:36:27,798][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 00:36:28,304][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 00:36:28,808][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 00:36:29,307][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 00:36:29,807][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 00:36:30,316][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 00:36:30,815][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 00:36:31,315][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 00:36:31,828][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 00:36:32,327][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 00:36:32,848][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 00:36:33,349][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 00:36:33,853][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 00:36:34,355][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 00:36:34,857][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 00:36:35,359][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 00:36:35,859][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 00:36:36,361][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 00:36:36,867][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 00:36:37,367][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 00:36:37,868][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 00:36:38,367][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 00:36:38,866][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 00:36:39,368][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 00:36:39,868][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 00:36:40,367][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 00:36:40,869][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 00:36:41,371][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 00:36:41,871][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 00:36:42,370][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 00:36:42,870][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 00:36:43,377][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 00:36:43,879][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 00:36:44,383][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 00:36:44,884][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 00:36:45,384][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 00:36:45,888][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 00:36:46,386][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 00:36:46,888][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 00:36:47,393][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 00:36:47,893][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 00:36:48,393][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 00:36:48,893][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 00:36:49,394][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 00:36:49,896][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 00:36:50,398][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10878 tokens. [2025-11-13 00:36:51,057][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.21%, Current % of VRAM taken: 59.66%, Block Peak % of device VRAM: 62.20%, ΔTime: 00:00:32 [2025-11-13 00:36:51,824][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 00:36:51,827][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 00:36:51,828][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 00:36:52,828][__main__][INFO] - Iteration 170 took 51s (30.12% Gen, 67.94% Train). Generation: 15s, Training: 35s. Estimated remaining time: 40h 33m 32s. Estimated total time: 43h 0m 43s. Time estimates for 10 more iterations: 8m 36s, 100 more iterations: 1h 26m 1s, 500 more iterations: 7h 10m 7s. [2025-11-13 00:36:52,831][__main__][INFO] - Starting iteration 170. [2025-11-13 00:36:53,330][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 16 and human policies 1. [2025-11-13 00:36:53,331][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 00:36:57,636][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 00:37:08,161][__main__][INFO] - Number of regex retries in iteration 170: 1 [2025-11-13 00:37:08,162][__main__][INFO] - agents played in iteration 170 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 00:37:08,993][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:37:09,020][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:37:09,046][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:37:09,069][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:37:09,070][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 00:37:09,071][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 00:37:09,719][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 00:37:10,183][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 00:37:10,688][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 00:37:11,188][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 00:37:11,687][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 00:37:12,186][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 00:37:12,686][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 00:37:13,185][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 00:37:13,686][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 00:37:14,189][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 00:37:14,690][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 00:37:15,194][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 00:37:15,697][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 00:37:16,198][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 00:37:16,703][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 00:37:17,206][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 00:37:17,708][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 00:37:18,219][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 00:37:18,724][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 00:37:19,252][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 00:37:19,755][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 00:37:20,262][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 00:37:20,768][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 00:37:21,272][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 00:37:21,778][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 00:37:22,279][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 00:37:22,781][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 00:37:23,284][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 00:37:23,784][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 00:37:24,287][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 00:37:24,785][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 00:37:25,284][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 00:37:25,784][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 00:37:26,283][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 00:37:26,781][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 00:37:27,283][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 00:37:27,784][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 00:37:28,282][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 00:37:28,782][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 00:37:29,282][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 00:37:29,783][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 00:37:30,283][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 00:37:30,783][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 00:37:31,285][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 00:37:31,784][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 00:37:32,284][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 00:37:32,784][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 00:37:33,283][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 00:37:33,789][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 00:37:34,294][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 00:37:34,800][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 00:37:35,306][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 00:37:35,809][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 00:37:36,317][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 00:37:36,820][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 00:37:37,319][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 00:37:37,837][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 00:37:38,339][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 00:37:38,852][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 00:37:39,355][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 00:37:39,858][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 00:37:40,362][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 00:37:40,862][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 00:37:41,363][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 00:37:41,862][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10883 tokens. [2025-11-13 00:37:42,519][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.19%, Current % of VRAM taken: 59.65%, Block Peak % of device VRAM: 62.30%, ΔTime: 00:00:32 [2025-11-13 00:37:43,304][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 00:37:43,305][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 00:37:43,307][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 00:37:45,068][__main__][INFO] - Iteration 171 took 51s (28.67% Gen, 67.93% Train). Generation: 14s, Training: 35s. Estimated remaining time: 40h 38m 51s. Estimated total time: 43h 6m 55s. Time estimates for 10 more iterations: 8m 37s, 100 more iterations: 1h 26m 13s, 500 more iterations: 7h 11m 9s. [2025-11-13 00:37:45,070][__main__][INFO] - Starting iteration 171. [2025-11-13 00:37:45,547][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 17 and human policies 1. [2025-11-13 00:37:45,548][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 00:37:55,999][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 00:38:00,373][__main__][INFO] - Number of regex retries in iteration 171: 1 [2025-11-13 00:38:00,374][__main__][INFO] - agents played in iteration 171 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 00:38:01,153][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:38:01,190][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:38:01,213][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:38:01,235][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:38:01,235][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 00:38:01,236][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 00:38:01,873][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 00:38:02,330][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 00:38:02,853][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 00:38:03,352][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 00:38:03,852][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 00:38:04,356][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 00:38:04,859][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 00:38:05,364][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 00:38:05,864][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 00:38:06,365][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 00:38:06,873][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 00:38:07,372][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 00:38:07,875][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 00:38:08,377][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 00:38:08,878][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 00:38:09,382][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 00:38:09,884][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 00:38:10,389][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 00:38:10,892][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 00:38:11,392][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 00:38:11,893][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 00:38:12,395][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 00:38:12,899][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 00:38:13,403][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 00:38:13,906][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 00:38:14,410][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 00:38:14,916][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 00:38:15,419][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 00:38:15,922][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 00:38:16,423][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 00:38:16,924][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 00:38:17,438][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 00:38:17,940][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 00:38:18,456][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 00:38:18,958][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 00:38:19,460][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 00:38:19,961][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 00:38:20,462][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 00:38:20,963][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 00:38:21,463][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 00:38:21,963][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 00:38:22,475][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 00:38:22,976][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 00:38:23,479][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 00:38:23,981][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 00:38:24,480][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 00:38:24,983][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 00:38:25,483][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 00:38:25,985][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 00:38:26,489][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 00:38:26,989][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 00:38:27,491][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 00:38:27,989][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 00:38:28,490][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 00:38:28,992][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 00:38:29,491][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 00:38:29,996][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 00:38:30,498][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 00:38:30,998][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 00:38:31,500][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 00:38:32,003][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 00:38:32,506][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 00:38:33,010][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 00:38:33,512][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 00:38:34,011][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10878 tokens. [2025-11-13 00:38:34,666][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.23%, Current % of VRAM taken: 59.68%, Block Peak % of device VRAM: 62.41%, ΔTime: 00:00:32 [2025-11-13 00:38:35,436][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 00:38:35,437][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 00:38:35,439][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 00:38:36,431][__main__][INFO] - Iteration 172 took 50s (29.13% Gen, 68.91% Train). Generation: 14s, Training: 35s. Estimated remaining time: 39h 55m 17s. Estimated total time: 42h 24m 12s. Time estimates for 10 more iterations: 8m 28s, 100 more iterations: 1h 24m 48s, 500 more iterations: 7h 4m 2s. [2025-11-13 00:38:36,433][__main__][INFO] - Starting iteration 172. [2025-11-13 00:38:36,915][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 17 and human policies 1. [2025-11-13 00:38:36,916][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 00:38:43,503][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 00:38:44,005][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 00:38:51,581][__main__][INFO] - Number of regex retries in iteration 172: 2 [2025-11-13 00:38:51,582][__main__][INFO] - agents played in iteration 172 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 00:38:52,431][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:38:52,458][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:38:52,484][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:38:52,507][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:38:52,507][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 00:38:52,508][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 00:38:53,137][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 00:38:53,595][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 00:38:54,099][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 00:38:54,601][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 00:38:55,103][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 00:38:55,604][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 00:38:56,108][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 00:38:56,611][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 00:38:57,117][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 00:38:57,619][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 00:38:58,121][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 00:38:58,624][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 00:38:59,127][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 00:38:59,631][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 00:39:00,131][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 00:39:00,643][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 00:39:01,144][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 00:39:01,670][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 00:39:02,171][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 00:39:02,676][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 00:39:03,177][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 00:39:03,678][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 00:39:04,185][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 00:39:04,687][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 00:39:05,191][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 00:39:05,697][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 00:39:06,202][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 00:39:06,705][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 00:39:07,207][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 00:39:07,717][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 00:39:08,221][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 00:39:08,727][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 00:39:09,229][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 00:39:09,730][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 00:39:10,231][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 00:39:10,733][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 00:39:11,229][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 00:39:11,730][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 00:39:12,242][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 00:39:12,741][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 00:39:13,244][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 00:39:13,745][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 00:39:14,245][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 00:39:14,748][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 00:39:15,249][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 00:39:15,748][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 00:39:16,251][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 00:39:16,754][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 00:39:17,267][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 00:39:17,768][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 00:39:18,268][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 00:39:18,771][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 00:39:19,273][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 00:39:19,778][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 00:39:20,280][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 00:39:20,779][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 00:39:21,280][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 00:39:21,782][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 00:39:22,289][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 00:39:22,796][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 00:39:23,297][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 00:39:23,802][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 00:39:24,305][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 00:39:24,805][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 00:39:25,307][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10879 tokens. [2025-11-13 00:39:26,050][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.22%, Current % of VRAM taken: 59.67%, Block Peak % of device VRAM: 62.49%, ΔTime: 00:00:32 [2025-11-13 00:39:26,840][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 00:39:26,841][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 00:39:26,843][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 00:39:27,844][__main__][INFO] - Iteration 173 took 50s (28.80% Gen, 69.23% Train). Generation: 14s, Training: 35s. Estimated remaining time: 39h 56m 42s. Estimated total time: 42h 26m 28s. Time estimates for 10 more iterations: 8m 29s, 100 more iterations: 1h 24m 52s, 500 more iterations: 7h 4m 24s. [2025-11-13 00:39:27,846][__main__][INFO] - Starting iteration 173. [2025-11-13 00:39:28,312][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 17 and human policies 1. [2025-11-13 00:39:28,313][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 00:39:43,123][__main__][INFO] - Number of regex retries in iteration 173: 0 [2025-11-13 00:39:43,123][__main__][INFO] - agents played in iteration 173 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 00:39:43,964][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:39:43,987][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:39:44,011][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:39:44,032][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:39:44,033][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 00:39:44,034][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 00:39:44,688][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 00:39:45,144][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 00:39:45,651][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 00:39:46,159][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 00:39:46,656][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 00:39:47,174][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 00:39:47,673][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 00:39:48,173][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 00:39:48,674][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 00:39:49,174][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 00:39:49,673][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 00:39:50,172][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 00:39:50,673][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 00:39:51,182][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 00:39:51,681][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 00:39:52,180][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 00:39:52,681][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 00:39:53,184][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 00:39:53,685][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 00:39:54,184][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 00:39:54,684][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 00:39:55,190][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 00:39:55,691][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 00:39:56,193][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 00:39:56,693][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 00:39:57,193][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 00:39:57,697][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 00:39:58,198][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 00:39:58,702][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 00:39:59,209][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 00:39:59,711][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 00:40:00,212][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 00:40:00,714][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 00:40:01,218][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 00:40:01,723][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 00:40:02,224][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 00:40:02,726][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 00:40:03,229][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 00:40:03,731][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 00:40:04,235][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 00:40:04,736][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 00:40:05,236][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 00:40:05,739][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 00:40:06,241][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 00:40:06,742][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 00:40:07,243][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 00:40:07,743][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 00:40:08,253][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 00:40:08,754][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 00:40:09,259][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 00:40:09,767][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 00:40:10,269][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 00:40:10,781][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 00:40:11,284][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 00:40:11,787][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 00:40:12,292][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 00:40:12,795][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 00:40:13,296][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 00:40:13,799][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 00:40:14,300][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 00:40:14,801][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 00:40:15,303][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 00:40:15,804][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 00:40:16,310][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 00:40:16,808][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10880 tokens. [2025-11-13 00:40:17,492][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.17%, Current % of VRAM taken: 59.62%, Block Peak % of device VRAM: 62.29%, ΔTime: 00:00:32 [2025-11-13 00:40:18,252][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 00:40:18,253][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 00:40:18,255][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 00:40:19,192][__main__][INFO] - Iteration 174 took 50s (29.11% Gen, 69.05% Train). Generation: 14s, Training: 35s. Estimated remaining time: 39h 53m 25s. Estimated total time: 42h 24m 2s. Time estimates for 10 more iterations: 8m 28s, 100 more iterations: 1h 24m 48s, 500 more iterations: 7h 4m 0s. [2025-11-13 00:40:19,195][__main__][INFO] - Starting iteration 174. [2025-11-13 00:40:19,687][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 17 and human policies 1. [2025-11-13 00:40:19,688][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 00:40:23,242][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 00:40:33,762][__main__][INFO] - Number of regex retries in iteration 174: 1 [2025-11-13 00:40:33,763][__main__][INFO] - agents played in iteration 174 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 00:40:34,551][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:40:34,579][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:40:34,605][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:40:34,627][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:40:34,628][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 00:40:34,629][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 00:40:35,293][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 00:40:35,772][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 00:40:36,279][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 00:40:36,782][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 00:40:37,284][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 00:40:37,784][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 00:40:38,301][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 00:40:38,800][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 00:40:39,301][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 00:40:39,809][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 00:40:40,312][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 00:40:51,840][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 00:40:52,345][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 00:40:52,849][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 00:40:53,356][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 00:40:53,859][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 00:40:54,371][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 00:40:54,874][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 00:40:55,387][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 00:40:55,889][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 00:40:56,389][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 00:40:56,894][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 00:40:57,392][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 00:40:57,892][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 00:40:58,395][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 00:40:58,898][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 00:40:59,399][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 00:40:59,905][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 00:41:00,424][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 00:41:00,930][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 00:41:01,436][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 00:41:01,943][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 00:41:02,446][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 00:41:02,950][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 00:41:03,464][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 00:41:03,966][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 00:41:04,469][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 00:41:04,977][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 00:41:05,480][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 00:41:05,983][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 00:41:06,484][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 00:41:06,985][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 00:41:07,501][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10879 tokens. [2025-11-13 00:41:08,151][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.25%, Current % of VRAM taken: 59.70%, Block Peak % of device VRAM: 62.49%, ΔTime: 00:00:32 [2025-11-13 00:41:08,934][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 00:41:08,935][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 00:41:08,937][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 00:41:09,852][__main__][INFO] - Iteration 175 took 50s (28.06% Gen, 70.12% Train). Generation: 14s, Training: 35s. Estimated remaining time: 39h 16m 46s. Estimated total time: 41h 48m 14s. Time estimates for 10 more iterations: 8m 21s, 100 more iterations: 1h 23m 36s, 500 more iterations: 6h 58m 2s. [2025-11-13 00:41:09,854][__main__][INFO] - Starting iteration 175. [2025-11-13 00:41:10,336][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 17 and human policies 1. [2025-11-13 00:41:10,337][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 00:41:26,287][__main__][INFO] - Number of regex retries in iteration 175: 0 [2025-11-13 00:41:26,288][__main__][INFO] - agents played in iteration 175 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 00:41:27,140][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.62%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:41:27,167][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.62%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:41:27,194][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.62%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:41:27,217][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.62%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:41:27,218][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 00:41:27,218][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 00:41:27,881][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 00:41:28,341][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 00:41:28,848][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 00:41:29,350][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 00:41:29,854][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 00:41:30,357][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 00:41:30,860][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 00:41:31,363][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 00:41:31,866][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 00:41:32,371][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 00:41:32,871][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 00:41:44,425][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 00:41:44,927][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 00:41:45,427][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 00:41:45,933][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 00:41:46,449][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 00:41:46,966][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 00:41:47,480][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 00:41:48,003][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 00:41:48,514][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 00:41:49,015][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 00:41:49,518][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 00:41:50,020][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 00:41:50,526][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 00:41:51,029][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 00:41:51,535][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 00:41:52,040][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 00:41:52,544][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 00:41:53,046][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 00:41:53,550][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 00:41:54,052][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 00:41:54,553][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 00:41:55,055][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 00:41:55,556][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 00:41:56,059][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 00:41:56,562][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 00:41:57,063][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 00:41:57,566][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 00:41:58,065][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 00:41:58,570][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 00:41:59,069][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 00:41:59,568][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 00:42:00,081][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10879 tokens. [2025-11-13 00:42:00,741][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.16%, Current % of VRAM taken: 59.61%, Block Peak % of device VRAM: 62.50%, ΔTime: 00:00:32 [2025-11-13 00:42:01,499][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 00:42:01,500][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 00:42:01,502][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 00:42:02,419][__main__][INFO] - Iteration 176 took 52s (30.63% Gen, 67.61% Train). Generation: 15s, Training: 35s. Estimated remaining time: 40h 51m 48s. Estimated total time: 43h 24m 9s. Time estimates for 10 more iterations: 8m 40s, 100 more iterations: 1h 26m 48s, 500 more iterations: 7h 14m 1s. [2025-11-13 00:42:02,421][__main__][INFO] - Starting iteration 176. [2025-11-13 00:42:02,890][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 17 and human policies 1. [2025-11-13 00:42:02,890][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 00:42:15,869][__main__][INFO] - Number of regex retries in iteration 176: 0 [2025-11-13 00:42:15,870][__main__][INFO] - agents played in iteration 176 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 00:42:16,794][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:42:16,820][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:42:16,846][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:42:16,869][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:42:16,869][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 00:42:16,870][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 00:42:17,509][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 00:42:17,963][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 00:42:18,479][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 00:42:18,982][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 00:42:19,489][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 00:42:19,991][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 00:42:20,495][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 00:42:21,001][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 00:42:21,503][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 00:42:22,008][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 00:42:22,510][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 00:42:23,011][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 00:42:23,523][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 00:42:24,025][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 00:42:24,528][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 00:42:25,027][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 00:42:25,526][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 00:42:26,034][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 00:42:26,551][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 00:42:27,051][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 00:42:27,554][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 00:42:28,056][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 00:42:28,559][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 00:42:29,065][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 00:42:29,566][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 00:42:30,069][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 00:42:30,570][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 00:42:31,073][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 00:42:31,573][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 00:42:32,075][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 00:42:32,580][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 00:42:33,081][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 00:42:33,583][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 00:42:34,098][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 00:42:34,601][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 00:42:35,114][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 00:42:35,618][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 00:42:36,120][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 00:42:36,628][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 00:42:37,131][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 00:42:37,633][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 00:42:38,137][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 00:42:38,641][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 00:42:39,148][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 00:42:39,651][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 00:42:40,159][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 00:42:40,665][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 00:42:41,169][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 00:42:41,674][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 00:42:42,177][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 00:42:42,682][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 00:42:43,185][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 00:42:43,689][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 00:42:44,191][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 00:42:44,695][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 00:42:45,195][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 00:42:45,717][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 00:42:46,219][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 00:42:46,722][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 00:42:47,225][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 00:42:47,726][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 00:42:48,230][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 00:42:48,730][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 00:42:49,229][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 00:42:49,731][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10880 tokens. [2025-11-13 00:42:50,396][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.24%, Current % of VRAM taken: 59.70%, Block Peak % of device VRAM: 62.48%, ΔTime: 00:00:32 [2025-11-13 00:42:51,168][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 00:42:51,171][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 00:42:51,173][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 00:42:52,108][__main__][INFO] - Iteration 177 took 49s (26.37% Gen, 71.73% Train). Generation: 12s, Training: 35s. Estimated remaining time: 38h 27m 45s. Estimated total time: 41h 0m 56s. Time estimates for 10 more iterations: 8m 12s, 100 more iterations: 1h 22m 1s, 500 more iterations: 6h 50m 9s. [2025-11-13 00:42:52,110][__main__][INFO] - Starting iteration 177. [2025-11-13 00:42:52,588][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 17 and human policies 1. [2025-11-13 00:42:52,588][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 00:43:05,451][__main__][INFO] - Number of regex retries in iteration 177: 0 [2025-11-13 00:43:05,452][__main__][INFO] - agents played in iteration 177 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 00:43:06,300][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:43:06,328][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:43:06,354][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:43:06,378][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:43:06,378][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 00:43:06,379][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 00:43:06,999][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 00:43:07,453][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 00:43:07,961][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 00:43:08,462][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 00:43:08,961][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 00:43:09,463][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 00:43:09,962][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 00:43:10,462][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 00:43:10,962][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 00:43:11,466][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 00:43:11,969][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 00:43:12,471][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 00:43:12,972][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 00:43:13,475][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 00:43:13,975][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 00:43:14,480][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 00:43:14,982][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 00:43:15,487][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 00:43:15,989][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 00:43:16,490][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 00:43:16,992][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 00:43:17,502][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 00:43:18,003][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 00:43:18,515][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 00:43:19,018][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 00:43:19,520][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 00:43:20,025][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 00:43:20,527][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 00:43:21,029][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 00:43:21,529][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 00:43:22,030][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 00:43:22,538][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 00:43:23,039][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 00:43:23,542][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 00:43:24,044][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 00:43:24,546][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 00:43:25,049][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 00:43:25,553][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 00:43:26,054][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 00:43:26,559][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 00:43:27,061][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 00:43:27,562][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 00:43:28,067][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 00:43:28,569][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 00:43:29,073][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 00:43:29,575][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 00:43:30,077][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 00:43:30,583][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 00:43:31,088][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 00:43:31,596][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 00:43:32,099][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 00:43:32,603][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 00:43:33,107][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 00:43:33,609][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 00:43:34,112][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 00:43:34,615][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 00:43:35,117][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 00:43:35,637][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 00:43:36,141][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 00:43:36,644][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 00:43:37,156][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 00:43:37,660][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 00:43:38,166][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 00:43:38,669][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 00:43:39,171][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10879 tokens. [2025-11-13 00:43:39,855][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.28%, Current % of VRAM taken: 59.74%, Block Peak % of device VRAM: 62.41%, ΔTime: 00:00:32 [2025-11-13 00:43:40,617][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 00:43:40,619][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 00:43:40,621][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 00:43:41,864][__main__][INFO] - Iteration 178 took 49s (26.11% Gen, 71.37% Train). Generation: 12s, Training: 35s. Estimated remaining time: 38h 29m 50s. Estimated total time: 41h 3m 50s. Time estimates for 10 more iterations: 8m 12s, 100 more iterations: 1h 22m 7s, 500 more iterations: 6h 50m 38s. [2025-11-13 00:43:41,866][__main__][INFO] - Starting iteration 178. [2025-11-13 00:43:42,376][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 17 and human policies 1. [2025-11-13 00:43:42,377][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 00:43:46,537][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 00:43:48,284][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 00:43:50,338][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 00:43:57,611][__main__][INFO] - Number of regex retries in iteration 178: 3 [2025-11-13 00:43:57,612][__main__][INFO] - agents played in iteration 178 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 00:43:58,441][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:43:58,466][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:43:58,491][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:43:58,513][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:43:58,513][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 00:43:58,514][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 00:43:59,134][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 00:43:59,607][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 00:44:00,111][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 00:44:00,611][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 00:44:01,115][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 00:44:01,614][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 00:44:02,128][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 00:44:02,629][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 00:44:03,127][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 00:44:03,629][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 00:44:04,132][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 00:44:04,632][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 00:44:05,134][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 00:44:05,637][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 00:44:06,150][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 00:44:06,653][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 00:44:07,155][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 00:44:07,658][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 00:44:08,159][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 00:44:08,659][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 00:44:09,161][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 00:44:09,663][mllm.training.trainer_common][INFO] - 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Processing mini-batch 43 of 64 [2025-11-13 00:44:21,206][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 00:44:21,709][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 00:44:22,210][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 00:44:22,711][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 00:44:23,224][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 00:44:23,728][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 00:44:24,234][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 00:44:24,740][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 00:44:25,245][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 00:44:25,752][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 00:44:26,250][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 00:44:26,754][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 00:44:27,266][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 00:44:27,764][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 00:44:28,265][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 00:44:28,767][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 00:44:29,267][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 00:44:29,772][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 00:44:30,271][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 00:44:30,769][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 00:44:31,268][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10873 tokens. [2025-11-13 00:44:31,955][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.16%, Current % of VRAM taken: 59.61%, Block Peak % of device VRAM: 62.32%, ΔTime: 00:00:32 [2025-11-13 00:44:32,737][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 00:44:32,739][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 00:44:32,740][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 00:44:33,744][__main__][INFO] - Iteration 179 took 51s (29.66% Gen, 68.39% Train). Generation: 15s, Training: 35s. Estimated remaining time: 40h 13m 33s. Estimated total time: 42h 48m 25s. Time estimates for 10 more iterations: 8m 33s, 100 more iterations: 1h 25m 36s, 500 more iterations: 7h 8m 4s. [2025-11-13 00:44:33,746][__main__][INFO] - Starting iteration 179. [2025-11-13 00:44:34,216][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 17 and human policies 1. [2025-11-13 00:44:34,217][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 00:44:48,055][__main__][INFO] - Number of regex retries in iteration 179: 0 [2025-11-13 00:44:48,056][__main__][INFO] - agents played in iteration 179 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 00:44:48,884][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:44:48,908][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:44:48,933][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:44:48,954][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:44:48,955][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 00:44:48,956][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 00:44:49,574][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 00:44:50,029][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 00:44:50,538][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 00:44:51,038][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 00:44:51,537][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 00:44:52,048][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 00:44:52,548][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 00:44:53,048][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 00:44:53,548][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 00:44:54,046][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 00:44:54,549][mllm.training.trainer_common][INFO] - 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Processing mini-batch 43 of 64 [2025-11-13 00:45:11,655][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 00:45:12,156][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 00:45:12,661][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 00:45:13,165][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 00:45:13,669][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 00:45:14,173][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 00:45:14,678][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 00:45:15,184][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 00:45:15,688][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 00:45:16,189][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 00:45:16,692][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 00:45:17,196][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 00:45:17,701][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 00:45:18,204][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 00:45:18,705][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 00:45:19,207][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 00:45:19,720][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 00:45:20,222][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 00:45:20,735][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 00:45:21,238][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 00:45:21,740][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10878 tokens. [2025-11-13 00:45:22,455][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.18%, Current % of VRAM taken: 59.64%, Block Peak % of device VRAM: 62.34%, ΔTime: 00:00:32 [2025-11-13 00:45:23,216][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 00:45:23,217][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 00:45:23,220][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 00:45:24,127][__main__][INFO] - Iteration 180 took 49s (27.73% Gen, 70.45% Train). Generation: 13s, Training: 35s. Estimated remaining time: 38h 59m 51s. Estimated total time: 41h 35m 33s. Time estimates for 10 more iterations: 8m 19s, 100 more iterations: 1h 23m 11s, 500 more iterations: 6h 55m 55s. [2025-11-13 00:45:24,129][__main__][INFO] - Starting iteration 180. [2025-11-13 00:45:24,639][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 17 and human policies 1. [2025-11-13 00:45:24,640][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 00:45:36,467][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 00:45:38,399][__main__][INFO] - Number of regex retries in iteration 180: 1 [2025-11-13 00:45:38,400][__main__][INFO] - agents played in iteration 180 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 00:45:39,260][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:45:39,286][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:45:39,312][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:45:39,334][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:45:39,335][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 00:45:39,336][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 00:45:39,940][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 00:45:40,396][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 00:45:40,900][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 00:45:41,401][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 00:45:41,909][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 00:45:42,409][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 00:45:42,911][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 00:45:43,424][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 00:45:43,925][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 00:45:44,427][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 00:45:44,924][mllm.training.trainer_common][INFO] - 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Processing mini-batch 43 of 64 [2025-11-13 00:46:01,986][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 00:46:02,487][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 00:46:02,988][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 00:46:03,486][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 00:46:03,986][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 00:46:04,493][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 00:46:04,995][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 00:46:05,497][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 00:46:06,000][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 00:46:06,503][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 00:46:07,006][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 00:46:07,509][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 00:46:08,010][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 00:46:08,514][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 00:46:09,016][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 00:46:09,524][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 00:46:10,040][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 00:46:10,544][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 00:46:11,066][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 00:46:11,569][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 00:46:12,077][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10878 tokens. [2025-11-13 00:46:12,769][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.32%, Current % of VRAM taken: 59.77%, Block Peak % of device VRAM: 62.49%, ΔTime: 00:00:32 [2025-11-13 00:46:13,537][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 00:46:13,539][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 00:46:13,541][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 00:46:15,561][__main__][INFO] - Iteration 181 took 50s (27.02% Gen, 69.01% Train). Generation: 13s, Training: 35s. Estimated remaining time: 39h 49m 33s. Estimated total time: 42h 26m 7s. Time estimates for 10 more iterations: 8m 29s, 100 more iterations: 1h 24m 52s, 500 more iterations: 7h 4m 21s. [2025-11-13 00:46:15,563][__main__][INFO] - Starting iteration 181. [2025-11-13 00:46:16,042][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 18 and human policies 1. [2025-11-13 00:46:16,042][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 00:46:23,287][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 00:46:24,600][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 00:46:31,125][__main__][INFO] - Number of regex retries in iteration 181: 2 [2025-11-13 00:46:31,125][__main__][INFO] - agents played in iteration 181 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 00:46:31,960][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.62%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:46:31,982][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.62%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:46:32,006][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.62%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:46:32,028][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.62%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:46:32,028][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 00:46:32,029][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 00:46:32,652][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 00:46:33,107][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 00:46:33,612][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 00:46:34,112][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 00:46:34,608][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 00:46:35,110][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 00:46:35,609][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 00:46:36,108][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 00:46:36,607][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 00:46:37,105][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 00:46:37,608][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 00:46:49,176][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 00:46:49,677][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 00:46:50,176][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 00:46:50,683][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 00:46:51,184][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 00:46:51,685][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 00:46:52,185][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 00:46:52,687][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 00:46:53,190][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 00:46:53,692][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 00:46:54,191][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 00:46:54,692][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 00:46:55,189][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 00:46:55,691][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 00:46:56,191][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 00:46:56,694][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 00:46:57,200][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 00:46:57,701][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 00:46:58,204][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 00:46:58,712][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 00:46:59,216][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 00:46:59,717][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 00:47:00,217][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 00:47:00,718][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 00:47:01,222][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 00:47:01,727][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 00:47:02,228][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 00:47:02,744][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 00:47:03,246][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 00:47:03,748][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 00:47:04,254][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 00:47:04,755][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10879 tokens. [2025-11-13 00:47:05,465][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.20%, Current % of VRAM taken: 59.65%, Block Peak % of device VRAM: 62.24%, ΔTime: 00:00:32 [2025-11-13 00:47:06,202][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 00:47:06,203][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 00:47:06,205][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 00:47:07,079][__main__][INFO] - Iteration 182 took 51s (29.55% Gen, 68.73% Train). Generation: 15s, Training: 35s. Estimated remaining time: 39h 54m 28s. Estimated total time: 42h 31m 53s. Time estimates for 10 more iterations: 8m 30s, 100 more iterations: 1h 25m 3s, 500 more iterations: 7h 5m 18s. [2025-11-13 00:47:07,081][__main__][INFO] - Starting iteration 182. [2025-11-13 00:47:07,558][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 18 and human policies 1. [2025-11-13 00:47:07,558][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 00:47:21,364][__main__][INFO] - Number of regex retries in iteration 182: 0 [2025-11-13 00:47:21,364][__main__][INFO] - agents played in iteration 182 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 00:47:22,213][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:47:22,239][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:47:22,264][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:47:22,287][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:47:22,287][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 00:47:22,289][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 00:47:22,912][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 00:47:23,368][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 00:47:23,874][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 00:47:24,377][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 00:47:24,884][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 00:47:25,383][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 00:47:25,884][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 00:47:26,379][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 00:47:26,881][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 00:47:27,387][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 00:47:27,887][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 00:47:28,388][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 00:47:28,890][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 00:47:29,389][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 00:47:29,888][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 00:47:30,387][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 00:47:30,887][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 00:47:31,393][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 00:47:31,896][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 00:47:32,400][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 00:47:32,904][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 00:47:33,406][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 00:47:39,435][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 00:47:39,938][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 00:47:40,443][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 00:47:40,945][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 00:47:41,447][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 00:47:41,947][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 00:47:42,446][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 00:47:42,947][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 00:47:43,448][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 00:47:43,948][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 00:47:44,471][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 00:47:44,972][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 00:47:45,470][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 00:47:45,971][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 00:47:46,471][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 00:47:46,975][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 00:47:47,476][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 00:47:47,976][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 00:47:48,478][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 00:47:48,981][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 00:47:49,485][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 00:47:49,989][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 00:47:50,491][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 00:47:51,006][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 00:47:51,512][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 00:47:52,015][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 00:47:52,518][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 00:47:53,021][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 00:47:53,522][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 00:47:54,023][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 00:47:54,525][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 00:47:55,028][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10879 tokens. [2025-11-13 00:47:55,713][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.18%, Current % of VRAM taken: 59.63%, Block Peak % of device VRAM: 62.28%, ΔTime: 00:00:32 [2025-11-13 00:47:56,488][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 00:47:56,490][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 00:47:56,491][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 00:47:57,423][__main__][INFO] - Iteration 183 took 49s (27.69% Gen, 70.44% Train). Generation: 13s, Training: 35s. Estimated remaining time: 38h 55m 2s. Estimated total time: 41h 33m 18s. Time estimates for 10 more iterations: 8m 18s, 100 more iterations: 1h 23m 6s, 500 more iterations: 6h 55m 33s. [2025-11-13 00:47:57,425][__main__][INFO] - Starting iteration 183. [2025-11-13 00:47:57,920][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 18 and human policies 1. [2025-11-13 00:47:57,921][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 00:48:12,652][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 ballots did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 00:48:14,184][__main__][INFO] - Number of regex retries in iteration 183: 1 [2025-11-13 00:48:14,185][__main__][INFO] - agents played in iteration 183 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 00:48:14,974][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:48:15,002][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:48:15,029][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:48:15,051][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:48:15,052][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 00:48:15,053][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 00:48:15,669][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 00:48:16,123][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 00:48:16,628][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 00:48:17,131][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 00:48:17,631][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 00:48:18,136][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 00:48:18,640][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 00:48:19,140][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 00:48:19,648][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 00:48:20,150][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 00:48:20,649][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 00:48:21,162][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 00:48:21,662][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 00:48:22,174][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 00:48:22,673][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 00:48:23,172][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 00:48:23,673][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 00:48:24,171][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 00:48:24,667][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 00:48:25,170][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 00:48:25,669][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 00:48:26,185][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 00:48:26,686][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 00:48:27,189][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 00:48:27,689][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 00:48:28,188][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 00:48:28,697][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 00:48:29,204][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 00:48:29,710][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 00:48:30,214][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 00:48:30,718][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 00:48:31,226][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 00:48:31,729][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 00:48:32,235][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 00:48:32,738][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 00:48:33,238][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 00:48:33,743][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 00:48:34,253][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 00:48:34,751][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 00:48:35,256][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 00:48:35,757][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 00:48:36,257][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 00:48:36,761][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 00:48:37,262][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 00:48:37,763][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 00:48:38,263][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 00:48:38,765][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 00:48:39,286][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 00:48:39,786][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 00:48:40,287][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 00:48:40,791][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 00:48:41,292][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 00:48:41,795][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 00:48:42,294][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 00:48:42,796][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 00:48:43,302][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 00:48:43,806][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 00:48:44,311][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 00:48:44,815][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 00:48:45,316][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 00:48:45,821][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 00:48:46,324][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 00:48:46,826][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 00:48:47,331][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 00:48:47,831][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10875 tokens. [2025-11-13 00:48:48,506][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.30%, Current % of VRAM taken: 59.75%, Block Peak % of device VRAM: 62.41%, ΔTime: 00:00:32 [2025-11-13 00:48:49,266][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 00:48:49,267][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 00:48:49,269][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 00:48:50,182][__main__][INFO] - Iteration 184 took 52s (31.12% Gen, 67.13% Train). Generation: 16s, Training: 35s. Estimated remaining time: 40h 53m 59s. Estimated total time: 43h 33m 7s. Time estimates for 10 more iterations: 8m 42s, 100 more iterations: 1h 27m 6s, 500 more iterations: 7h 15m 31s. [2025-11-13 00:48:50,184][__main__][INFO] - Starting iteration 184. [2025-11-13 00:48:50,669][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 18 and human policies 1. [2025-11-13 00:48:50,670][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 00:48:54,507][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 00:48:59,942][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 00:49:01,494][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 00:49:04,434][__main__][INFO] - Number of regex retries in iteration 184: 3 [2025-11-13 00:49:04,435][__main__][INFO] - agents played in iteration 184 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 00:49:05,376][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:49:05,403][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:49:05,430][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:49:05,453][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:49:05,453][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 00:49:05,454][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 00:49:06,097][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 00:49:06,556][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 00:49:07,075][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 00:49:07,578][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 00:49:08,090][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 00:49:08,588][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 00:49:09,089][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 00:49:09,601][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 00:49:10,104][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 00:49:10,607][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 00:49:11,106][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 00:49:11,608][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 00:49:12,111][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 00:49:12,612][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 00:49:13,113][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 00:49:13,615][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 00:49:14,116][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 00:49:14,619][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 00:49:15,119][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 00:49:15,619][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 00:49:16,121][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 00:49:16,621][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 00:49:17,121][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 00:49:17,628][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 00:49:18,130][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 00:49:18,633][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 00:49:19,138][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 00:49:19,641][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 00:49:20,146][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 00:49:20,650][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 00:49:21,153][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 00:49:21,654][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 00:49:22,154][mllm.training.trainer_common][INFO] - 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Processing mini-batch 54 of 64 [2025-11-13 00:49:33,722][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 00:49:34,225][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 00:49:34,731][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 00:49:35,232][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 00:49:35,739][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 00:49:36,242][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 00:49:36,744][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 00:49:37,249][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 00:49:37,750][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 00:49:38,251][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10879 tokens. [2025-11-13 00:49:38,917][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.16%, Current % of VRAM taken: 59.61%, Block Peak % of device VRAM: 62.28%, ΔTime: 00:00:32 [2025-11-13 00:49:39,676][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 00:49:39,678][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 00:49:39,680][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 00:49:40,691][__main__][INFO] - Iteration 185 took 50s (27.52% Gen, 70.46% Train). Generation: 13s, Training: 35s. Estimated remaining time: 39h 1m 7s. Estimated total time: 41h 41m 6s. Time estimates for 10 more iterations: 8m 20s, 100 more iterations: 1h 23m 22s, 500 more iterations: 6h 56m 51s. [2025-11-13 00:49:40,693][__main__][INFO] - Starting iteration 185. [2025-11-13 00:49:41,169][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 18 and human policies 1. [2025-11-13 00:49:41,170][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 00:49:54,640][__main__][INFO] - Number of regex retries in iteration 185: 0 [2025-11-13 00:49:54,641][__main__][INFO] - agents played in iteration 185 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 00:49:55,417][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:49:55,444][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:49:55,470][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:49:55,493][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:49:55,493][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 00:49:55,494][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 00:49:56,127][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 00:49:56,583][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 00:49:57,095][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 00:49:57,597][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 00:49:58,113][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 00:49:58,615][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 00:49:59,114][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 00:49:59,631][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 00:50:00,134][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 00:50:00,637][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 00:50:01,137][mllm.training.trainer_common][INFO] - 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Processing mini-batch 43 of 64 [2025-11-13 00:50:18,212][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 00:50:18,711][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 00:50:19,225][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 00:50:19,725][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 00:50:20,224][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 00:50:20,723][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 00:50:21,223][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 00:50:21,729][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 00:50:22,229][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 00:50:22,729][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 00:50:23,242][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 00:50:23,744][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 00:50:24,247][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 00:50:24,748][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 00:50:25,249][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 00:50:25,752][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 00:50:26,252][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 00:50:26,754][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 00:50:27,261][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 00:50:27,766][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 00:50:28,275][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10877 tokens. [2025-11-13 00:50:28,971][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.17%, Current % of VRAM taken: 59.62%, Block Peak % of device VRAM: 62.41%, ΔTime: 00:00:32 [2025-11-13 00:50:29,737][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 00:50:29,739][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 00:50:29,741][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 00:50:30,658][__main__][INFO] - Iteration 186 took 49s (27.22% Gen, 70.92% Train). Generation: 13s, Training: 35s. Estimated remaining time: 38h 33m 39s. Estimated total time: 41h 14m 28s. Time estimates for 10 more iterations: 8m 14s, 100 more iterations: 1h 22m 28s, 500 more iterations: 6h 52m 24s. [2025-11-13 00:50:30,660][__main__][INFO] - Starting iteration 186. [2025-11-13 00:50:31,149][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 18 and human policies 1. [2025-11-13 00:50:31,150][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 00:50:45,735][__main__][INFO] - Number of regex retries in iteration 186: 0 [2025-11-13 00:50:45,736][__main__][INFO] - agents played in iteration 186 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 00:50:46,568][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:50:46,597][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:50:46,622][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:50:46,647][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:50:46,648][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 00:50:46,649][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 00:50:47,306][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 00:50:47,763][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 00:50:48,272][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 00:50:48,775][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 00:50:49,280][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 00:50:49,784][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 00:50:50,286][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 00:50:50,787][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 00:50:51,288][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 00:50:51,788][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 00:50:52,290][mllm.training.trainer_common][INFO] - 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Processing mini-batch 54 of 64 [2025-11-13 00:51:14,848][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 00:51:15,349][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 00:51:15,855][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 00:51:16,358][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 00:51:16,862][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 00:51:17,365][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 00:51:17,866][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 00:51:18,369][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 00:51:18,872][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 00:51:19,375][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10870 tokens. [2025-11-13 00:51:20,074][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.22%, Current % of VRAM taken: 59.68%, Block Peak % of device VRAM: 62.32%, ΔTime: 00:00:32 [2025-11-13 00:51:20,842][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 00:51:20,843][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 00:51:20,846][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 00:51:21,764][__main__][INFO] - Iteration 187 took 50s (28.82% Gen, 69.37% Train). Generation: 14s, Training: 35s. Estimated remaining time: 39h 29m 5s. Estimated total time: 42h 10m 45s. Time estimates for 10 more iterations: 8m 26s, 100 more iterations: 1h 24m 21s, 500 more iterations: 7h 1m 47s. [2025-11-13 00:51:21,766][__main__][INFO] - Starting iteration 187. [2025-11-13 00:51:22,245][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 18 and human policies 1. [2025-11-13 00:51:22,246][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 00:51:37,422][__main__][INFO] - Number of regex retries in iteration 187: 0 [2025-11-13 00:51:37,422][__main__][INFO] - agents played in iteration 187 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 00:51:38,257][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:51:38,281][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:51:38,308][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:51:38,331][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:51:38,331][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 00:51:38,332][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 00:51:38,970][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 00:51:39,429][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 00:51:39,933][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 00:51:40,433][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 00:51:40,934][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 00:51:41,433][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 00:51:41,936][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 00:51:42,436][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 00:51:42,940][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 00:51:43,444][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 00:51:43,949][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 00:51:55,494][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 00:51:55,998][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 00:51:56,502][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 00:51:57,003][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 00:51:57,505][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 00:51:58,007][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 00:51:58,509][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 00:51:59,017][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 00:51:59,518][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 00:52:00,015][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 00:52:00,518][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 00:52:01,022][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 00:52:01,522][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 00:52:02,022][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 00:52:02,521][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 00:52:03,029][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 00:52:03,536][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 00:52:04,037][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 00:52:04,542][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 00:52:05,042][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 00:52:05,555][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 00:52:06,060][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 00:52:06,565][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 00:52:07,071][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 00:52:07,574][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 00:52:08,077][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 00:52:08,580][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 00:52:09,082][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 00:52:09,586][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 00:52:10,088][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 00:52:10,590][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 00:52:11,093][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10875 tokens. [2025-11-13 00:52:11,778][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.23%, Current % of VRAM taken: 59.68%, Block Peak % of device VRAM: 62.34%, ΔTime: 00:00:32 [2025-11-13 00:52:12,547][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 00:52:12,548][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 00:52:12,550][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 00:52:13,469][__main__][INFO] - Iteration 188 took 51s (29.63% Gen, 68.58% Train). Generation: 15s, Training: 35s. Estimated remaining time: 39h 58m 41s. Estimated total time: 42h 41m 12s. Time estimates for 10 more iterations: 8m 32s, 100 more iterations: 1h 25m 22s, 500 more iterations: 7h 6m 52s. [2025-11-13 00:52:13,471][__main__][INFO] - Starting iteration 188. [2025-11-13 00:52:13,943][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 18 and human policies 1. [2025-11-13 00:52:13,944][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 00:52:18,373][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 00:52:22,594][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 00:52:27,415][__main__][INFO] - Number of regex retries in iteration 188: 2 [2025-11-13 00:52:27,416][__main__][INFO] - agents played in iteration 188 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 00:52:28,205][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:52:28,229][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:52:28,251][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:52:28,274][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:52:28,275][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 00:52:28,276][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 00:52:28,913][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 00:52:29,377][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 00:52:29,879][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 00:52:30,382][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 00:52:30,883][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 00:52:31,383][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 00:52:31,887][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 00:52:32,386][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 00:52:32,888][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 00:52:33,388][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 00:52:33,887][mllm.training.trainer_common][INFO] - 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Processing mini-batch 54 of 64 [2025-11-13 00:52:56,527][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 00:52:57,030][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 00:52:57,530][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 00:52:58,033][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 00:52:58,535][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 00:52:59,036][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 00:52:59,546][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 00:53:00,049][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 00:53:00,552][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 00:53:01,064][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10878 tokens. [2025-11-13 00:53:01,760][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.22%, Current % of VRAM taken: 59.68%, Block Peak % of device VRAM: 62.31%, ΔTime: 00:00:32 [2025-11-13 00:53:02,550][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 00:53:02,552][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 00:53:02,554][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 00:53:03,732][__main__][INFO] - Iteration 189 took 49s (27.06% Gen, 70.57% Train). Generation: 13s, Training: 35s. Estimated remaining time: 38h 46m 5s. Estimated total time: 41h 29m 27s. Time estimates for 10 more iterations: 8m 17s, 100 more iterations: 1h 22m 58s, 500 more iterations: 6h 54m 54s. [2025-11-13 00:53:03,734][__main__][INFO] - Starting iteration 189. [2025-11-13 00:53:04,219][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 18 and human policies 1. [2025-11-13 00:53:04,219][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 00:53:11,585][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 00:53:19,519][__main__][INFO] - Number of regex retries in iteration 189: 1 [2025-11-13 00:53:19,520][__main__][INFO] - agents played in iteration 189 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 00:53:20,360][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:53:20,383][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:53:20,407][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:53:20,430][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:53:20,430][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 00:53:20,431][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 00:53:21,070][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 00:53:21,526][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 00:53:22,029][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 00:53:22,529][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 00:53:23,030][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 00:53:23,529][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 00:53:24,029][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 00:53:24,530][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 00:53:25,030][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 00:53:25,528][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 00:53:26,032][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 00:53:37,610][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 00:53:38,110][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 00:53:38,609][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 00:53:39,109][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 00:53:39,608][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 00:53:40,113][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 00:53:40,616][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 00:53:41,119][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 00:53:41,625][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 00:53:42,128][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 00:53:42,630][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 00:53:43,131][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 00:53:43,632][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 00:53:44,135][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 00:53:44,636][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 00:53:45,137][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 00:53:45,640][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 00:53:46,139][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 00:53:46,643][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 00:53:47,143][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 00:53:47,646][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 00:53:48,158][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 00:53:48,659][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 00:53:49,175][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 00:53:49,677][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 00:53:50,177][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 00:53:50,684][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 00:53:51,187][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 00:53:51,690][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 00:53:52,192][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 00:53:52,694][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 00:53:53,202][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10874 tokens. [2025-11-13 00:53:53,862][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.20%, Current % of VRAM taken: 59.65%, Block Peak % of device VRAM: 62.49%, ΔTime: 00:00:32 [2025-11-13 00:53:54,623][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 00:53:54,625][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 00:53:54,626][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 00:53:55,767][__main__][INFO] - Iteration 190 took 51s (29.68% Gen, 68.10% Train). Generation: 15s, Training: 35s. Estimated remaining time: 40h 13m 12s. Estimated total time: 42h 57m 26s. Time estimates for 10 more iterations: 8m 35s, 100 more iterations: 1h 25m 54s, 500 more iterations: 7h 9m 34s. [2025-11-13 00:53:55,769][__main__][INFO] - Starting iteration 190. [2025-11-13 00:53:56,234][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 18 and human policies 1. [2025-11-13 00:53:56,235][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 00:54:02,973][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 00:54:10,405][__main__][INFO] - Number of regex retries in iteration 190: 1 [2025-11-13 00:54:10,405][__main__][INFO] - agents played in iteration 190 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 00:54:11,280][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:54:11,302][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:54:11,324][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:54:11,346][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:54:11,347][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 00:54:11,347][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 00:54:12,011][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 00:54:12,468][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 00:54:12,979][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 00:54:13,484][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 00:54:13,997][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 00:54:14,499][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 00:54:15,001][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 00:54:15,502][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 00:54:16,004][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 00:54:16,509][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 00:54:17,010][mllm.training.trainer_common][INFO] - 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Processing mini-batch 54 of 64 [2025-11-13 00:54:39,633][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 00:54:40,139][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 00:54:40,642][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 00:54:41,144][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 00:54:41,646][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 00:54:42,147][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 00:54:42,649][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 00:54:43,151][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 00:54:43,654][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 00:54:44,160][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10879 tokens. [2025-11-13 00:54:44,840][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.18%, Current % of VRAM taken: 59.63%, Block Peak % of device VRAM: 62.49%, ΔTime: 00:00:32 [2025-11-13 00:54:45,630][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 00:54:45,631][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 00:54:45,633][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 00:54:47,408][__main__][INFO] - Iteration 191 took 51s (27.69% Gen, 68.84% Train). Generation: 14s, Training: 35s. Estimated remaining time: 39h 53m 38s. Estimated total time: 42h 38m 44s. Time estimates for 10 more iterations: 8m 31s, 100 more iterations: 1h 25m 17s, 500 more iterations: 7h 6m 27s. [2025-11-13 00:54:47,410][__main__][INFO] - Starting iteration 191. [2025-11-13 00:54:47,912][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 19 and human policies 1. [2025-11-13 00:54:47,913][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 00:54:51,854][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 00:55:00,188][__main__][INFO] - Number of regex retries in iteration 191: 1 [2025-11-13 00:55:00,188][__main__][INFO] - agents played in iteration 191 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 00:55:01,111][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:55:01,135][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:55:01,161][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:55:01,183][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:55:01,184][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 00:55:01,185][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 00:55:01,837][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 00:55:02,300][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 00:55:02,809][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 00:55:03,315][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 00:55:03,819][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 00:55:04,327][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 00:55:04,832][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 00:55:05,339][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 00:55:05,842][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 00:55:06,343][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 00:55:06,842][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 00:55:18,370][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 00:55:18,870][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 00:55:19,371][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 00:55:19,873][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 00:55:20,380][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 00:55:20,893][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 00:55:21,392][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 00:55:21,890][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 00:55:22,394][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 00:55:22,892][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 00:55:23,401][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 00:55:23,901][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 00:55:24,400][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 00:55:24,914][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 00:55:25,417][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 00:55:25,921][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 00:55:26,423][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 00:55:26,926][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 00:55:27,432][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 00:55:27,934][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 00:55:28,436][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 00:55:28,941][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 00:55:29,442][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 00:55:29,945][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 00:55:30,447][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 00:55:30,949][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 00:55:31,454][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 00:55:31,953][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 00:55:32,456][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 00:55:32,960][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 00:55:33,460][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 00:55:33,963][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10879 tokens. [2025-11-13 00:55:34,663][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.23%, Current % of VRAM taken: 59.68%, Block Peak % of device VRAM: 62.52%, ΔTime: 00:00:32 [2025-11-13 00:55:35,436][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 00:55:35,437][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 00:55:35,440][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 00:55:36,483][__main__][INFO] - Iteration 192 took 48s (25.27% Gen, 72.58% Train). Generation: 12s, Training: 35s. Estimated remaining time: 37h 42m 38s. Estimated total time: 40h 28m 33s. Time estimates for 10 more iterations: 8m 5s, 100 more iterations: 1h 20m 57s, 500 more iterations: 6h 44m 45s. [2025-11-13 00:55:36,485][__main__][INFO] - Starting iteration 192. [2025-11-13 00:55:36,971][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 19 and human policies 1. [2025-11-13 00:55:36,972][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 00:55:52,685][__main__][INFO] - Number of regex retries in iteration 192: 0 [2025-11-13 00:55:52,686][__main__][INFO] - agents played in iteration 192 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 00:55:53,551][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:55:53,580][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:55:53,609][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:55:53,634][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:55:53,635][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 00:55:53,636][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 00:55:54,287][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 00:55:54,745][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 00:55:55,256][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 00:55:55,764][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 00:55:56,266][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 00:55:56,769][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 00:55:57,273][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 00:55:57,778][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 00:55:58,279][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 00:55:58,784][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 00:55:59,291][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 00:55:59,791][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 00:56:00,306][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 00:56:00,807][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 00:56:01,308][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 00:56:01,809][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 00:56:02,309][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 00:56:02,809][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 00:56:03,309][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 00:56:03,808][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 00:56:04,318][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 00:56:04,819][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 00:56:10,822][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 00:56:11,323][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 00:56:11,826][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 00:56:12,329][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 00:56:12,829][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 00:56:13,329][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 00:56:13,827][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 00:56:14,327][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 00:56:14,828][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 00:56:15,326][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 00:56:15,825][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 00:56:16,324][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 00:56:16,823][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 00:56:17,329][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 00:56:17,829][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 00:56:18,331][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 00:56:18,834][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 00:56:19,335][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 00:56:19,836][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 00:56:20,338][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 00:56:20,842][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 00:56:21,347][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 00:56:21,851][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 00:56:22,355][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 00:56:22,857][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 00:56:23,361][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 00:56:23,865][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 00:56:24,369][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 00:56:24,872][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 00:56:25,383][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 00:56:25,890][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 00:56:26,400][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10869 tokens. [2025-11-13 00:56:27,093][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.23%, Current % of VRAM taken: 59.68%, Block Peak % of device VRAM: 62.35%, ΔTime: 00:00:32 [2025-11-13 00:56:27,867][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 00:56:27,869][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 00:56:27,871][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 00:56:28,789][__main__][INFO] - Iteration 193 took 51s (30.32% Gen, 67.90% Train). Generation: 15s, Training: 35s. Estimated remaining time: 40h 24m 8s. Estimated total time: 43h 10m 55s. Time estimates for 10 more iterations: 8m 38s, 100 more iterations: 1h 26m 21s, 500 more iterations: 7h 11m 49s. [2025-11-13 00:56:28,791][__main__][INFO] - Starting iteration 193. [2025-11-13 00:56:29,261][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 19 and human policies 1. [2025-11-13 00:56:29,261][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 00:56:34,701][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 00:56:42,858][__main__][INFO] - Number of regex retries in iteration 193: 1 [2025-11-13 00:56:42,859][__main__][INFO] - agents played in iteration 193 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 00:56:43,807][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:56:43,833][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:56:43,859][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:56:43,881][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:56:43,882][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 00:56:43,883][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 00:56:44,526][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 00:56:44,992][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 00:56:45,504][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 00:56:46,010][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 00:56:46,517][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 00:56:47,020][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 00:56:47,531][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 00:56:48,034][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 00:56:48,537][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 00:56:49,039][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 00:56:49,539][mllm.training.trainer_common][INFO] - 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Processing mini-batch 43 of 64 [2025-11-13 00:57:06,634][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 00:57:07,138][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 00:57:07,638][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 00:57:08,137][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 00:57:08,637][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 00:57:09,136][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 00:57:09,641][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 00:57:10,144][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 00:57:10,647][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 00:57:11,149][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 00:57:11,652][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 00:57:12,154][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 00:57:12,656][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 00:57:13,157][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 00:57:13,662][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 00:57:14,163][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 00:57:14,666][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 00:57:15,171][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 00:57:15,672][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 00:57:16,173][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 00:57:16,673][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10879 tokens. [2025-11-13 00:57:17,377][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.17%, Current % of VRAM taken: 59.62%, Block Peak % of device VRAM: 62.42%, ΔTime: 00:00:32 [2025-11-13 00:57:18,139][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 00:57:18,141][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 00:57:18,143][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 00:57:19,065][__main__][INFO] - Iteration 194 took 49s (27.30% Gen, 70.84% Train). Generation: 13s, Training: 35s. Estimated remaining time: 38h 42m 38s. Estimated total time: 41h 30m 15s. Time estimates for 10 more iterations: 8m 18s, 100 more iterations: 1h 23m 0s, 500 more iterations: 6h 55m 2s. [2025-11-13 00:57:19,067][__main__][INFO] - Starting iteration 194. [2025-11-13 00:57:19,532][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 19 and human policies 1. [2025-11-13 00:57:19,533][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 00:57:33,936][__main__][INFO] - Number of regex retries in iteration 194: 0 [2025-11-13 00:57:33,937][__main__][INFO] - agents played in iteration 194 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 00:57:34,720][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:57:34,756][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:57:34,783][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:57:34,807][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:57:34,808][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 00:57:34,809][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 00:57:35,432][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 00:57:35,888][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 00:57:36,405][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 00:57:36,905][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 00:57:37,406][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 00:57:37,914][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 00:57:38,416][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 00:57:38,928][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 00:57:39,432][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 00:57:39,933][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 00:57:40,441][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 00:57:40,942][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 00:57:41,445][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 00:57:41,946][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 00:57:42,447][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 00:57:42,951][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 00:57:43,452][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 00:57:43,952][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 00:57:44,458][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 00:57:44,956][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 00:57:45,455][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 00:57:45,952][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 00:57:51,959][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 00:57:52,460][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 00:57:52,960][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 00:57:53,464][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 00:57:53,968][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 00:57:54,470][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 00:57:54,981][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 00:57:55,484][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 00:57:55,981][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 00:57:56,482][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 00:57:56,982][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 00:57:57,486][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 00:57:57,988][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 00:57:58,491][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 00:57:59,001][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 00:57:59,499][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 00:58:00,012][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 00:58:00,511][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 00:58:01,009][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 00:58:01,513][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 00:58:02,012][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 00:58:02,522][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 00:58:03,022][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 00:58:03,526][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 00:58:04,033][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 00:58:04,538][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 00:58:05,042][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 00:58:05,543][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 00:58:06,047][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 00:58:06,550][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 00:58:07,052][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 00:58:07,556][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10880 tokens. [2025-11-13 00:58:08,318][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.19%, Current % of VRAM taken: 59.65%, Block Peak % of device VRAM: 62.31%, ΔTime: 00:00:32 [2025-11-13 00:58:09,056][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 00:58:09,058][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 00:58:09,060][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 00:58:10,144][__main__][INFO] - Iteration 195 took 50s (28.46% Gen, 69.40% Train). Generation: 14s, Training: 35s. Estimated remaining time: 39h 22m 7s. Estimated total time: 42h 10m 36s. Time estimates for 10 more iterations: 8m 26s, 100 more iterations: 1h 24m 21s, 500 more iterations: 7h 1m 46s. [2025-11-13 00:58:10,148][__main__][INFO] - Starting iteration 195. [2025-11-13 00:58:10,636][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 19 and human policies 1. [2025-11-13 00:58:10,637][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 00:58:26,065][__main__][INFO] - Number of regex retries in iteration 195: 0 [2025-11-13 00:58:26,065][__main__][INFO] - agents played in iteration 195 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 00:58:26,895][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:58:26,920][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:58:26,945][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:58:26,967][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:58:26,968][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 00:58:26,968][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 00:58:27,593][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 00:58:28,050][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 00:58:28,557][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 00:58:29,058][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 00:58:29,559][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 00:58:30,064][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 00:58:30,569][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 00:58:31,073][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 00:58:31,578][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 00:58:32,081][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 00:58:32,585][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 00:58:44,166][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 00:58:44,667][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 00:58:45,169][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 00:58:45,672][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 00:58:46,172][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 00:58:46,674][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 00:58:47,178][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 00:58:47,679][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 00:58:48,182][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 00:58:48,688][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 00:58:49,190][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 00:58:49,692][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 00:58:50,195][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 00:58:50,697][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 00:58:51,200][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 00:58:51,707][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 00:58:52,234][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 00:58:52,733][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 00:58:53,233][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 00:58:53,734][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 00:58:54,233][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 00:58:54,743][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 00:58:55,248][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 00:58:55,756][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 00:58:56,259][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 00:58:56,762][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 00:58:57,269][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 00:58:57,774][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 00:58:58,277][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 00:58:58,782][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 00:58:59,286][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 00:58:59,789][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10879 tokens. [2025-11-13 00:59:00,562][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.23%, Current % of VRAM taken: 59.68%, Block Peak % of device VRAM: 62.41%, ΔTime: 00:00:32 [2025-11-13 00:59:01,299][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 00:59:01,301][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 00:59:01,302][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 00:59:02,179][__main__][INFO] - Iteration 196 took 51s (29.93% Gen, 68.36% Train). Generation: 15s, Training: 35s. Estimated remaining time: 40h 7m 52s. Estimated total time: 42h 57m 12s. Time estimates for 10 more iterations: 8m 35s, 100 more iterations: 1h 25m 54s, 500 more iterations: 7h 9m 32s. [2025-11-13 00:59:02,181][__main__][INFO] - Starting iteration 196. [2025-11-13 00:59:02,654][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 19 and human policies 1. [2025-11-13 00:59:02,655][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 00:59:06,705][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 00:59:17,912][__main__][INFO] - Number of regex retries in iteration 196: 1 [2025-11-13 00:59:17,912][__main__][INFO] - agents played in iteration 196 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 00:59:18,777][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:59:18,805][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:59:18,831][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:59:18,854][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 00:59:18,854][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 00:59:18,855][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 00:59:19,474][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 00:59:19,943][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 00:59:20,448][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 00:59:20,949][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 00:59:21,450][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 00:59:21,951][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 00:59:22,459][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 00:59:22,956][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 00:59:23,455][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 00:59:23,968][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 00:59:24,474][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 00:59:24,979][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 00:59:25,484][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 00:59:25,987][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 00:59:26,490][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 00:59:26,993][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 00:59:27,496][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 00:59:28,011][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 00:59:28,515][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 00:59:29,020][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 00:59:29,524][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 00:59:30,027][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 00:59:36,049][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 00:59:36,550][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 00:59:37,055][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 00:59:37,555][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 00:59:38,054][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 00:59:38,563][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 00:59:39,063][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 00:59:39,566][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 00:59:40,068][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 00:59:40,567][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 00:59:41,069][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 00:59:41,571][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 00:59:42,071][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 00:59:42,576][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 00:59:43,082][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 00:59:43,586][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 00:59:44,086][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 00:59:44,586][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 00:59:45,088][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 00:59:45,593][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 00:59:46,095][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 00:59:46,599][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 00:59:47,103][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 00:59:47,606][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 00:59:48,109][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 00:59:48,612][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 00:59:49,115][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 00:59:49,618][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 00:59:50,120][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 00:59:50,622][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 00:59:51,123][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 00:59:51,629][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10873 tokens. [2025-11-13 00:59:52,386][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.17%, Current % of VRAM taken: 59.62%, Block Peak % of device VRAM: 62.49%, ΔTime: 00:00:32 [2025-11-13 00:59:53,133][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 00:59:53,136][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 00:59:53,138][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 00:59:54,179][__main__][INFO] - Iteration 197 took 51s (29.61% Gen, 68.37% Train). Generation: 15s, Training: 35s. Estimated remaining time: 40h 6m 5s. Estimated total time: 42h 56m 17s. Time estimates for 10 more iterations: 8m 35s, 100 more iterations: 1h 25m 52s, 500 more iterations: 7h 9m 22s. [2025-11-13 00:59:54,182][__main__][INFO] - Starting iteration 197. [2025-11-13 00:59:54,657][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 19 and human policies 1. [2025-11-13 00:59:54,658][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 01:00:09,626][__main__][INFO] - Number of regex retries in iteration 197: 0 [2025-11-13 01:00:09,627][__main__][INFO] - agents played in iteration 197 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 01:00:10,420][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.54%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:00:10,447][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.54%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:00:10,473][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.54%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:00:10,496][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.54%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:00:10,496][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 01:00:10,497][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 01:00:11,137][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 01:00:11,593][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 01:00:12,096][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 01:00:12,597][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 01:00:13,095][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 01:00:13,594][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 01:00:14,093][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 01:00:14,592][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 01:00:15,091][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 01:00:15,592][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 01:00:16,090][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 01:00:16,591][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 01:00:17,088][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 01:00:17,589][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 01:00:18,089][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 01:00:18,586][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 01:00:19,089][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 01:00:19,592][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 01:00:20,092][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 01:00:20,599][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 01:00:21,103][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 01:00:21,603][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 01:00:22,108][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 01:00:22,609][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 01:00:23,111][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 01:00:23,618][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 01:00:24,121][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 01:00:24,625][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 01:00:25,127][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 01:00:25,627][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 01:00:26,144][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 01:00:26,646][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 01:00:27,170][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 01:00:27,670][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 01:00:28,173][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 01:00:28,677][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 01:00:29,177][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 01:00:29,682][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 01:00:30,181][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 01:00:30,681][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 01:00:31,183][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 01:00:31,683][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 01:00:32,184][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 01:00:32,684][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 01:00:33,183][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 01:00:33,686][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 01:00:34,186][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 01:00:34,687][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 01:00:35,194][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 01:00:35,695][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 01:00:36,194][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 01:00:36,694][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 01:00:37,196][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 01:00:37,700][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 01:00:38,203][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 01:00:38,704][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 01:00:39,210][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 01:00:39,716][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 01:00:40,229][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 01:00:40,735][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 01:00:41,239][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 01:00:41,757][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 01:00:42,261][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 01:00:42,777][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 01:00:43,281][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10878 tokens. [2025-11-13 01:00:44,043][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.30%, Current % of VRAM taken: 59.75%, Block Peak % of device VRAM: 62.41%, ΔTime: 00:00:32 [2025-11-13 01:00:44,778][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 01:00:44,780][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 01:00:44,781][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 01:00:45,730][__main__][INFO] - Iteration 198 took 51s (29.31% Gen, 68.83% Train). Generation: 14s, Training: 35s. Estimated remaining time: 39h 42m 36s. Estimated total time: 42h 33m 41s. Time estimates for 10 more iterations: 8m 30s, 100 more iterations: 1h 25m 7s, 500 more iterations: 7h 5m 36s. [2025-11-13 01:00:45,732][__main__][INFO] - Starting iteration 198. [2025-11-13 01:00:46,240][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 19 and human policies 1. [2025-11-13 01:00:46,241][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 01:01:00,284][__main__][INFO] - Number of regex retries in iteration 198: 0 [2025-11-13 01:01:00,284][__main__][INFO] - agents played in iteration 198 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 01:01:01,253][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:01:01,275][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:01:01,299][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:01:01,321][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:01:01,321][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 01:01:01,322][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 01:01:01,933][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 01:01:02,390][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 01:01:02,893][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 01:01:03,393][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 01:01:03,895][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 01:01:04,395][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 01:01:04,893][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 01:01:05,395][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 01:01:05,895][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 01:01:06,396][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 01:01:06,896][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 01:01:07,395][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 01:01:07,898][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 01:01:08,398][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 01:01:08,898][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 01:01:09,396][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 01:01:09,896][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 01:01:10,397][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 01:01:10,896][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 01:01:11,393][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 01:01:11,893][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 01:01:12,391][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 01:01:12,897][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 01:01:13,398][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 01:01:13,901][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 01:01:14,406][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 01:01:14,906][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 01:01:15,402][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 01:01:15,907][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 01:01:16,407][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 01:01:16,908][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 01:01:17,407][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 01:01:17,907][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 01:01:18,413][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 01:01:18,913][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 01:01:19,413][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 01:01:19,915][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 01:01:20,414][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 01:01:20,916][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 01:01:21,417][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 01:01:21,917][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 01:01:22,419][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 01:01:22,919][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 01:01:23,420][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 01:01:23,922][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 01:01:24,422][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 01:01:24,923][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 01:01:25,425][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 01:01:25,926][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 01:01:26,432][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 01:01:26,932][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 01:01:27,432][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 01:01:27,933][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 01:01:28,436][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 01:01:28,939][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 01:01:29,445][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 01:01:29,948][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 01:01:30,452][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 01:01:30,955][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 01:01:31,461][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 01:01:31,977][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 01:01:32,483][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 01:01:32,995][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 01:01:33,499][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 01:01:34,003][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10878 tokens. [2025-11-13 01:01:34,718][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.25%, Current % of VRAM taken: 59.70%, Block Peak % of device VRAM: 62.28%, ΔTime: 00:00:32 [2025-11-13 01:01:35,457][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 01:01:35,459][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 01:01:35,461][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 01:01:36,412][__main__][INFO] - Iteration 199 took 50s (27.99% Gen, 70.11% Train). Generation: 14s, Training: 35s. Estimated remaining time: 38h 56m 43s. Estimated total time: 41h 48m 38s. Time estimates for 10 more iterations: 8m 21s, 100 more iterations: 1h 23m 37s, 500 more iterations: 6h 58m 6s. [2025-11-13 01:01:36,415][__main__][INFO] - Starting iteration 199. [2025-11-13 01:01:36,897][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 19 and human policies 1. [2025-11-13 01:01:36,897][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 01:01:40,380][mllm.models.large_language_model_local][WARNING] - Response Proposal: 5 hats, 5 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 01:01:40,552][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 01:01:45,888][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 01:01:51,414][__main__][INFO] - Number of regex retries in iteration 199: 3 [2025-11-13 01:01:51,415][__main__][INFO] - agents played in iteration 199 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 01:01:52,400][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:01:52,427][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:01:52,453][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:01:52,476][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:01:52,477][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 01:01:52,478][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 01:01:53,124][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 01:01:53,580][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 01:01:54,094][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 01:01:54,594][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 01:01:55,093][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 01:01:55,595][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 01:01:56,095][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 01:01:56,594][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 01:01:57,095][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 01:01:57,596][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 01:01:58,098][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 01:01:58,597][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 01:01:59,097][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 01:01:59,608][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 01:02:00,109][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 01:02:00,607][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 01:02:01,107][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 01:02:01,610][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 01:02:02,113][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 01:02:02,615][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 01:02:03,115][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 01:02:03,618][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 01:02:04,117][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 01:02:04,618][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 01:02:05,123][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 01:02:05,626][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 01:02:06,126][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 01:02:06,628][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 01:02:07,130][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 01:02:07,631][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 01:02:08,135][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 01:02:08,636][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 01:02:09,135][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 01:02:09,636][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 01:02:10,138][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 01:02:10,635][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 01:02:11,134][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 01:02:11,633][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 01:02:12,131][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 01:02:12,631][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 01:02:13,128][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 01:02:13,649][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 01:02:14,159][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 01:02:14,662][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 01:02:15,166][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 01:02:15,669][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 01:02:16,170][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 01:02:16,671][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 01:02:17,176][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 01:02:17,676][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 01:02:18,190][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 01:02:18,690][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 01:02:19,190][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 01:02:19,697][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 01:02:20,199][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 01:02:20,710][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 01:02:21,215][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 01:02:21,719][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 01:02:22,221][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 01:02:22,722][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 01:02:23,223][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 01:02:23,724][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 01:02:24,227][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 01:02:24,731][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 01:02:25,232][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10876 tokens. [2025-11-13 01:02:25,948][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.15%, Current % of VRAM taken: 59.61%, Block Peak % of device VRAM: 62.41%, ΔTime: 00:00:32 [2025-11-13 01:02:26,725][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 01:02:26,726][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 01:02:26,728][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 01:02:27,734][__main__][INFO] - Iteration 200 took 50s (28.56% Gen, 69.46% Train). Generation: 14s, Training: 35s. Estimated remaining time: 39h 29m 8s. Estimated total time: 42h 21m 54s. Time estimates for 10 more iterations: 8m 28s, 100 more iterations: 1h 24m 43s, 500 more iterations: 7h 3m 39s. [2025-11-13 01:02:27,736][__main__][INFO] - Starting iteration 200. [2025-11-13 01:02:28,204][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 19 and human policies 1. [2025-11-13 01:02:28,204][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 01:02:41,712][__main__][INFO] - Number of regex retries in iteration 200: 0 [2025-11-13 01:02:41,713][__main__][INFO] - agents played in iteration 200 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 01:02:42,565][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:02:42,591][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:02:42,617][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:02:42,640][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:02:42,640][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 01:02:42,641][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 01:02:43,320][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 01:02:43,778][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 01:02:44,308][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 01:02:44,809][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 01:02:45,308][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 01:02:45,818][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 01:02:46,320][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 01:02:46,818][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 01:02:47,316][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 01:02:47,815][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 01:02:48,316][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 01:02:48,814][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 01:02:49,312][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 01:02:49,811][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 01:02:50,309][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 01:02:50,813][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 01:02:51,308][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 01:02:51,806][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 01:02:52,316][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 01:02:52,814][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 01:02:53,311][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 01:02:53,806][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 01:02:54,305][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 01:02:54,816][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 01:02:55,314][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 01:02:55,812][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 01:02:56,311][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 01:02:56,810][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 01:02:57,312][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 01:02:57,812][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 01:02:58,315][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 01:02:58,824][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 01:02:59,328][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 01:02:59,832][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 01:03:00,331][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 01:03:00,834][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 01:03:01,340][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 01:03:01,842][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 01:03:02,342][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 01:03:02,849][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 01:03:03,354][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 01:03:03,856][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 01:03:04,358][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 01:03:04,857][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 01:03:05,358][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 01:03:05,858][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 01:03:06,358][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 01:03:06,861][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 01:03:07,362][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 01:03:07,863][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 01:03:08,373][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 01:03:08,874][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 01:03:09,389][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 01:03:09,890][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 01:03:10,390][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 01:03:10,894][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 01:03:11,395][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 01:03:11,904][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 01:03:12,405][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 01:03:12,905][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 01:03:13,422][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 01:03:13,927][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 01:03:14,428][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 01:03:14,928][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 01:03:15,429][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10879 tokens. [2025-11-13 01:03:16,148][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.14%, Current % of VRAM taken: 59.59%, Block Peak % of device VRAM: 62.24%, ΔTime: 00:00:32 [2025-11-13 01:03:16,896][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 01:03:16,897][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 01:03:16,899][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 01:03:18,731][__main__][INFO] - Iteration 201 took 50s (26.73% Gen, 69.64% Train). Generation: 13s, Training: 35s. Estimated remaining time: 39h 12m 47s. Estimated total time: 42h 6m 24s. Time estimates for 10 more iterations: 8m 25s, 100 more iterations: 1h 24m 12s, 500 more iterations: 7h 1m 4s. [2025-11-13 01:03:18,733][__main__][INFO] - Starting iteration 201. [2025-11-13 01:03:19,250][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 20 and human policies 1. [2025-11-13 01:03:19,251][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 01:03:24,059][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 01:03:26,561][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 01:03:33,899][__main__][INFO] - Number of regex retries in iteration 201: 2 [2025-11-13 01:03:33,900][__main__][INFO] - agents played in iteration 201 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 01:03:34,759][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:03:34,787][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:03:34,813][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:03:34,837][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:03:34,837][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 01:03:34,838][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 01:03:35,491][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 01:03:36,092][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 01:03:36,596][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 01:03:37,094][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 01:03:37,594][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 01:03:38,092][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 01:03:38,590][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 01:03:39,089][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 01:03:39,590][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 01:03:40,098][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 01:03:40,596][mllm.training.trainer_common][INFO] - 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Processing mini-batch 21 of 64 [2025-11-13 01:03:46,610][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 01:03:47,109][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 01:03:47,625][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 01:03:48,125][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 01:03:48,622][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 01:03:49,120][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 01:03:49,618][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 01:03:50,123][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 01:03:50,622][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 01:03:51,121][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 01:03:51,625][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 01:03:52,128][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 01:03:52,631][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 01:03:53,138][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 01:03:53,640][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 01:03:54,142][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 01:03:54,645][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 01:03:55,148][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 01:03:55,650][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 01:03:56,152][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 01:03:56,655][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 01:03:57,156][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 01:03:57,657][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 01:03:58,158][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 01:03:58,659][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 01:03:59,160][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 01:03:59,662][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 01:04:00,160][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 01:04:00,682][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 01:04:01,186][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 01:04:01,687][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 01:04:02,188][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 01:04:02,689][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 01:04:03,193][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 01:04:03,693][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 01:04:04,199][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 01:04:04,712][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 01:04:05,213][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 01:04:05,726][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 01:04:06,228][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 01:04:06,730][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 01:04:07,233][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 01:04:07,734][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10878 tokens. [2025-11-13 01:04:08,457][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.26%, Current % of VRAM taken: 59.71%, Block Peak % of device VRAM: 62.41%, ΔTime: 00:00:32 [2025-11-13 01:04:09,202][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 01:04:09,204][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 01:04:09,205][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 01:04:10,238][__main__][INFO] - Iteration 202 took 50s (28.73% Gen, 69.24% Train). Generation: 14s, Training: 35s. Estimated remaining time: 39h 34m 54s. Estimated total time: 42h 29m 23s. Time estimates for 10 more iterations: 8m 29s, 100 more iterations: 1h 24m 58s, 500 more iterations: 7h 4m 53s. [2025-11-13 01:04:10,240][__main__][INFO] - Starting iteration 202. [2025-11-13 01:04:10,722][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 20 and human policies 1. [2025-11-13 01:04:10,723][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 01:04:15,178][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 01:04:24,443][__main__][INFO] - Number of regex retries in iteration 202: 1 [2025-11-13 01:04:24,444][__main__][INFO] - agents played in iteration 202 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 01:04:25,359][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:04:25,386][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:04:25,412][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:04:25,435][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:04:25,435][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 01:04:25,436][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 01:04:26,086][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 01:04:26,541][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 01:04:27,046][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 01:04:27,545][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 01:04:28,053][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 01:04:28,556][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 01:04:29,060][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 01:04:29,562][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 01:04:30,061][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 01:04:30,561][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 01:04:31,059][mllm.training.trainer_common][INFO] - 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Processing mini-batch 21 of 64 [2025-11-13 01:04:37,058][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 01:04:37,556][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 01:04:38,055][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 01:04:38,559][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 01:04:39,055][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 01:04:39,555][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 01:04:40,063][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 01:04:40,561][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 01:04:41,057][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 01:04:41,555][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 01:04:42,056][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 01:04:42,562][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 01:04:43,062][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 01:04:43,559][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 01:04:44,060][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 01:04:44,558][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 01:04:45,061][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 01:04:45,563][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 01:04:46,066][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 01:04:46,576][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 01:04:47,079][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 01:04:47,579][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 01:04:48,087][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 01:04:48,590][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 01:04:49,091][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 01:04:49,596][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 01:04:50,099][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 01:04:50,602][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 01:04:51,105][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 01:04:51,606][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 01:04:52,108][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 01:04:52,608][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 01:04:53,109][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 01:04:53,609][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 01:04:54,109][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 01:04:54,610][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 01:04:55,111][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 01:04:55,611][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 01:04:56,112][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 01:04:56,614][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 01:04:57,120][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 01:04:57,621][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 01:04:58,124][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10879 tokens. [2025-11-13 01:04:58,836][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.22%, Current % of VRAM taken: 59.67%, Block Peak % of device VRAM: 62.50%, ΔTime: 00:00:32 [2025-11-13 01:04:59,612][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 01:04:59,614][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 01:04:59,616][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 01:05:00,561][__main__][INFO] - Iteration 203 took 49s (27.53% Gen, 70.57% Train). Generation: 13s, Training: 35s. Estimated remaining time: 38h 36m 38s. Estimated total time: 41h 31m 57s. Time estimates for 10 more iterations: 8m 18s, 100 more iterations: 1h 23m 3s, 500 more iterations: 6h 55m 19s. [2025-11-13 01:05:00,563][__main__][INFO] - Starting iteration 203. [2025-11-13 01:05:01,037][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 20 and human policies 1. [2025-11-13 01:05:01,038][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 01:05:14,595][__main__][INFO] - Number of regex retries in iteration 203: 0 [2025-11-13 01:05:14,596][__main__][INFO] - agents played in iteration 203 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 01:05:15,381][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:05:15,404][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:05:15,426][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:05:15,448][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:05:15,449][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 01:05:15,450][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 01:05:16,146][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 01:05:16,607][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 01:05:17,112][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 01:05:17,616][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 01:05:18,114][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 01:05:18,618][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 01:05:19,118][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 01:05:19,617][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 01:05:20,115][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 01:05:20,619][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 01:05:21,122][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 01:05:21,626][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 01:05:22,138][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 01:05:22,638][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 01:05:23,153][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 01:05:23,653][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 01:05:24,153][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 01:05:24,668][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 01:05:25,168][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 01:05:25,670][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 01:05:26,168][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 01:05:26,668][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 01:05:27,170][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 01:05:27,670][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 01:05:28,172][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 01:05:28,677][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 01:05:29,176][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 01:05:29,679][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 01:05:30,176][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 01:05:30,676][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 01:05:31,179][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 01:05:31,682][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 01:05:32,181][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 01:05:32,682][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 01:05:33,183][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 01:05:33,686][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 01:05:34,188][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 01:05:34,687][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 01:05:35,190][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 01:05:35,688][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 01:05:36,188][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 01:05:36,688][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 01:05:37,187][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 01:05:37,685][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 01:05:38,184][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 01:05:38,687][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 01:05:39,196][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 01:05:39,698][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 01:05:40,200][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 01:05:40,702][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 01:05:41,202][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 01:05:41,706][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 01:05:42,209][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 01:05:42,711][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 01:05:43,215][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 01:05:43,717][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 01:05:44,219][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 01:05:44,720][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 01:05:45,218][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 01:05:45,716][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 01:05:46,215][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 01:05:46,714][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 01:05:47,213][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 01:05:47,713][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 01:05:48,213][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10878 tokens. [2025-11-13 01:05:48,962][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.09%, Current % of VRAM taken: 59.54%, Block Peak % of device VRAM: 62.30%, ΔTime: 00:00:32 [2025-11-13 01:05:49,756][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 01:05:49,757][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 01:05:49,759][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 01:05:50,946][__main__][INFO] - Iteration 204 took 49s (27.16% Gen, 70.45% Train). Generation: 13s, Training: 35s. Estimated remaining time: 38h 39m 20s. Estimated total time: 41h 35m 29s. Time estimates for 10 more iterations: 8m 19s, 100 more iterations: 1h 23m 10s, 500 more iterations: 6h 55m 54s. [2025-11-13 01:05:50,949][__main__][INFO] - Starting iteration 204. [2025-11-13 01:05:51,448][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 20 and human policies 1. [2025-11-13 01:05:51,449][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 01:05:55,511][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 01:05:56,542][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 01:06:00,404][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 01:06:05,971][__main__][INFO] - Number of regex retries in iteration 204: 3 [2025-11-13 01:06:05,971][__main__][INFO] - agents played in iteration 204 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 01:06:06,812][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.52%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:06:06,837][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.52%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:06:06,862][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.52%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:06:06,884][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.52%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:06:06,884][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 01:06:06,886][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 01:06:07,574][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 01:06:08,040][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 01:06:08,549][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 01:06:09,052][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 01:06:09,570][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 01:06:10,074][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 01:06:10,578][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 01:06:11,079][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 01:06:11,582][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 01:06:12,089][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 01:06:12,589][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 01:06:13,092][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 01:06:13,593][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 01:06:14,092][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 01:06:14,594][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 01:06:15,098][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 01:06:15,600][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 01:06:16,100][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 01:06:16,599][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 01:06:17,098][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 01:06:17,598][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 01:06:18,097][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 01:06:18,597][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 01:06:19,095][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 01:06:19,595][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 01:06:20,099][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 01:06:20,597][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 01:06:21,096][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 01:06:21,594][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 01:06:22,093][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 01:06:22,595][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 01:06:23,096][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 01:06:23,596][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 01:06:24,097][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 01:06:24,596][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 01:06:25,096][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 01:06:25,599][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 01:06:26,098][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 01:06:26,599][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 01:06:27,099][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 01:06:27,598][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 01:06:28,095][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 01:06:28,595][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 01:06:29,099][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 01:06:29,602][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 01:06:30,101][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 01:06:30,602][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 01:06:31,098][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 01:06:31,597][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 01:06:32,097][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 01:06:32,597][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 01:06:33,102][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 01:06:33,602][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 01:06:34,107][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 01:06:34,613][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 01:06:35,114][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 01:06:35,619][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 01:06:36,121][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 01:06:36,624][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 01:06:37,129][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 01:06:37,632][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 01:06:38,135][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 01:06:38,637][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 01:06:39,139][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 01:06:39,640][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10880 tokens. [2025-11-13 01:06:40,363][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.26%, Current % of VRAM taken: 59.72%, Block Peak % of device VRAM: 62.41%, ΔTime: 00:00:32 [2025-11-13 01:06:41,178][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 01:06:41,179][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 01:06:41,181][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 01:06:42,124][__main__][INFO] - Iteration 205 took 50s (28.66% Gen, 69.48% Train). Generation: 14s, Training: 35s. Estimated remaining time: 39h 16m 48s. Estimated total time: 42h 13m 48s. Time estimates for 10 more iterations: 8m 26s, 100 more iterations: 1h 24m 27s, 500 more iterations: 7h 2m 18s. [2025-11-13 01:06:42,127][__main__][INFO] - Starting iteration 205. [2025-11-13 01:06:42,640][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 20 and human policies 1. [2025-11-13 01:06:42,640][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 01:06:47,152][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 01:06:57,874][__main__][INFO] - Number of regex retries in iteration 205: 1 [2025-11-13 01:06:57,875][__main__][INFO] - agents played in iteration 205 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 01:06:58,709][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:06:58,737][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:06:58,763][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:06:58,786][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:06:58,787][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 01:06:58,788][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 01:06:59,511][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 01:06:59,971][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 01:07:00,481][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 01:07:00,986][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 01:07:01,489][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 01:07:01,991][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 01:07:02,496][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 01:07:03,000][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 01:07:03,508][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 01:07:04,009][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 01:07:04,512][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 01:07:05,018][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 01:07:05,524][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 01:07:06,029][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 01:07:06,531][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 01:07:07,032][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 01:07:07,534][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 01:07:08,036][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 01:07:08,538][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 01:07:09,037][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 01:07:09,535][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 01:07:10,034][mllm.training.trainer_common][INFO] - 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Processing mini-batch 43 of 64 [2025-11-13 01:07:21,550][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 01:07:22,063][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 01:07:22,563][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 01:07:23,068][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 01:07:23,569][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 01:07:24,070][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 01:07:24,594][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 01:07:25,095][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 01:07:25,596][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 01:07:26,095][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 01:07:26,599][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 01:07:27,106][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 01:07:27,609][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 01:07:28,111][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 01:07:28,618][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 01:07:29,121][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 01:07:29,622][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 01:07:30,125][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 01:07:30,630][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 01:07:31,136][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 01:07:31,635][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10877 tokens. [2025-11-13 01:07:32,358][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.19%, Current % of VRAM taken: 59.64%, Block Peak % of device VRAM: 62.41%, ΔTime: 00:00:32 [2025-11-13 01:07:33,126][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 01:07:33,136][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 01:07:33,137][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 01:07:34,066][__main__][INFO] - Iteration 206 took 51s (29.62% Gen, 68.57% Train). Generation: 15s, Training: 35s. Estimated remaining time: 39h 53m 27s. Estimated total time: 42h 51m 19s. Time estimates for 10 more iterations: 8m 34s, 100 more iterations: 1h 25m 42s, 500 more iterations: 7h 8m 33s. [2025-11-13 01:07:34,068][__main__][INFO] - Starting iteration 206. [2025-11-13 01:07:34,568][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 20 and human policies 1. [2025-11-13 01:07:34,569][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 01:07:47,760][__main__][INFO] - Number of regex retries in iteration 206: 0 [2025-11-13 01:07:47,760][__main__][INFO] - agents played in iteration 206 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 01:07:48,562][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:07:48,589][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:07:48,615][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:07:48,638][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:07:48,639][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 01:07:48,640][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 01:07:49,338][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 01:07:49,800][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 01:07:50,307][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 01:07:50,817][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 01:07:51,319][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 01:07:51,820][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 01:07:52,319][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 01:07:52,823][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 01:07:53,327][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 01:07:53,827][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 01:07:54,327][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 01:08:05,839][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 01:08:06,341][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 01:08:06,840][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 01:08:07,341][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 01:08:07,844][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 01:08:08,345][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 01:08:08,845][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 01:08:09,348][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 01:08:09,848][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 01:08:10,348][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 01:08:10,847][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 01:08:11,348][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 01:08:11,852][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 01:08:12,351][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 01:08:12,849][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 01:08:13,350][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 01:08:13,851][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 01:08:14,351][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 01:08:14,850][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 01:08:15,349][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 01:08:15,851][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 01:08:16,350][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 01:08:16,848][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 01:08:17,350][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 01:08:17,848][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 01:08:18,349][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 01:08:18,850][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 01:08:19,349][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 01:08:19,847][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 01:08:20,346][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 01:08:20,845][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 01:08:21,350][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10879 tokens. [2025-11-13 01:08:22,081][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.23%, Current % of VRAM taken: 59.68%, Block Peak % of device VRAM: 62.41%, ΔTime: 00:00:32 [2025-11-13 01:08:22,866][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 01:08:22,868][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 01:08:22,870][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 01:08:23,849][__main__][INFO] - Iteration 207 took 49s (26.77% Gen, 71.24% Train). Generation: 13s, Training: 35s. Estimated remaining time: 38h 5m 22s. Estimated total time: 41h 4m 5s. Time estimates for 10 more iterations: 8m 12s, 100 more iterations: 1h 22m 8s, 500 more iterations: 6h 50m 40s. [2025-11-13 01:08:23,851][__main__][INFO] - Starting iteration 207. [2025-11-13 01:08:24,354][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 20 and human policies 1. [2025-11-13 01:08:24,354][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 01:08:29,359][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 01:08:39,798][__main__][INFO] - Number of regex retries in iteration 207: 1 [2025-11-13 01:08:39,798][__main__][INFO] - agents played in iteration 207 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 01:08:40,675][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:08:40,698][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:08:40,721][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:08:40,743][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:08:40,744][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 01:08:40,745][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 01:08:41,442][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 01:08:41,903][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 01:08:42,413][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 01:08:42,917][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 01:08:43,427][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 01:08:43,931][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 01:08:44,433][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 01:08:44,940][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 01:08:45,441][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 01:08:45,946][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 01:08:46,447][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 01:08:46,951][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 01:08:47,453][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 01:08:47,952][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 01:08:48,452][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 01:08:48,957][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 01:08:49,458][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 01:08:49,962][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 01:08:50,464][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 01:08:50,963][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 01:08:51,468][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 01:08:51,971][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 01:08:57,981][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 01:08:58,481][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 01:08:58,980][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 01:08:59,480][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 01:08:59,978][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 01:09:00,476][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 01:09:00,977][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 01:09:01,476][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 01:09:01,974][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 01:09:02,474][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 01:09:02,974][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 01:09:03,477][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 01:09:03,976][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 01:09:04,476][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 01:09:04,977][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 01:09:05,476][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 01:09:05,976][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 01:09:06,474][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 01:09:06,972][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 01:09:07,471][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 01:09:07,969][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 01:09:08,468][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 01:09:08,968][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 01:09:09,467][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 01:09:09,969][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 01:09:10,468][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 01:09:10,967][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 01:09:11,465][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 01:09:11,963][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 01:09:12,464][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 01:09:12,962][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 01:09:13,462][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10878 tokens. [2025-11-13 01:09:14,184][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.20%, Current % of VRAM taken: 59.65%, Block Peak % of device VRAM: 62.32%, ΔTime: 00:00:32 [2025-11-13 01:09:14,967][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 01:09:14,969][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 01:09:14,971][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 01:09:15,888][__main__][INFO] - Iteration 208 took 51s (29.97% Gen, 68.25% Train). Generation: 15s, Training: 35s. Estimated remaining time: 39h 57m 10s. Estimated total time: 42h 56m 44s. Time estimates for 10 more iterations: 8m 35s, 100 more iterations: 1h 25m 53s, 500 more iterations: 7h 9m 27s. [2025-11-13 01:09:15,890][__main__][INFO] - Starting iteration 208. [2025-11-13 01:09:16,399][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 20 and human policies 1. [2025-11-13 01:09:16,400][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 01:09:30,997][__main__][INFO] - Number of regex retries in iteration 208: 0 [2025-11-13 01:09:30,998][__main__][INFO] - agents played in iteration 208 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 01:09:31,836][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:09:31,858][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:09:31,880][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:09:31,902][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:09:31,903][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 01:09:31,904][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 01:09:32,594][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 01:09:33,052][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 01:09:33,564][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 01:09:34,068][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 01:09:34,573][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 01:09:35,080][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 01:09:35,582][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 01:09:36,084][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 01:09:36,588][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 01:09:37,091][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 01:09:37,594][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 01:09:38,095][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 01:09:38,596][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 01:09:39,102][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 01:09:39,602][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 01:09:40,102][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 01:09:40,605][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 01:09:41,107][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 01:09:41,610][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 01:09:42,111][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 01:09:42,611][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 01:09:43,118][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 01:09:49,149][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 01:09:49,644][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 01:09:50,143][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 01:09:50,640][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 01:09:51,136][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 01:09:51,634][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 01:09:52,147][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 01:09:52,647][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 01:09:53,145][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 01:09:53,645][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 01:09:54,144][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 01:09:54,645][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 01:09:55,143][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 01:09:55,640][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 01:09:56,148][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 01:09:56,647][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 01:09:57,150][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 01:09:57,648][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 01:09:58,148][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 01:09:58,652][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 01:09:59,151][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 01:09:59,651][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 01:10:00,158][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 01:10:00,658][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 01:10:01,156][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 01:10:01,656][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 01:10:02,154][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 01:10:02,656][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 01:10:03,156][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 01:10:03,655][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 01:10:04,153][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 01:10:04,651][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10880 tokens. [2025-11-13 01:10:05,295][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.20%, Current % of VRAM taken: 59.65%, Block Peak % of device VRAM: 62.32%, ΔTime: 00:00:32 [2025-11-13 01:10:06,055][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 01:10:06,057][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 01:10:06,058][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 01:10:07,085][__main__][INFO] - Iteration 209 took 50s (28.80% Gen, 69.17% Train). Generation: 14s, Training: 35s. Estimated remaining time: 39h 13m 56s. Estimated total time: 42h 14m 21s. Time estimates for 10 more iterations: 8m 26s, 100 more iterations: 1h 24m 28s, 500 more iterations: 7h 2m 23s. [2025-11-13 01:10:07,087][__main__][INFO] - Starting iteration 209. [2025-11-13 01:10:07,568][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 20 and human policies 1. [2025-11-13 01:10:07,569][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 01:10:16,222][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 01:10:21,426][__main__][INFO] - Number of regex retries in iteration 209: 1 [2025-11-13 01:10:21,427][__main__][INFO] - agents played in iteration 209 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 01:10:22,227][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:10:22,254][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:10:22,281][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:10:22,305][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:10:22,306][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 01:10:22,306][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 01:10:23,017][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 01:10:23,474][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 01:10:23,981][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 01:10:24,484][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 01:10:24,990][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 01:10:25,494][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 01:10:25,999][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 01:10:26,502][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 01:10:27,001][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 01:10:27,510][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 01:10:28,015][mllm.training.trainer_common][INFO] - 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Processing mini-batch 21 of 64 [2025-11-13 01:10:34,065][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 01:10:34,565][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 01:10:35,069][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 01:10:35,575][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 01:10:36,078][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 01:10:36,579][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 01:10:37,084][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 01:10:37,584][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 01:10:38,088][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 01:10:38,590][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 01:10:39,092][mllm.training.trainer_common][INFO] - 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Processing mini-batch 43 of 64 [2025-11-13 01:10:45,099][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 01:10:45,598][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 01:10:46,099][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 01:10:46,598][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 01:10:47,097][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 01:10:47,597][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 01:10:48,097][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 01:10:48,598][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 01:10:49,097][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 01:10:49,596][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 01:10:50,096][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 01:10:50,595][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 01:10:51,095][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 01:10:51,595][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 01:10:52,095][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 01:10:52,596][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 01:10:53,095][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 01:10:53,595][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 01:10:54,095][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 01:10:54,596][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 01:10:55,100][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10880 tokens. [2025-11-13 01:10:55,758][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.28%, Current % of VRAM taken: 59.73%, Block Peak % of device VRAM: 62.34%, ΔTime: 00:00:32 [2025-11-13 01:10:56,527][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 01:10:56,529][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 01:10:56,531][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 01:10:57,442][__main__][INFO] - Iteration 210 took 49s (27.78% Gen, 70.39% Train). Generation: 13s, Training: 35s. Estimated remaining time: 38h 32m 27s. Estimated total time: 41h 33m 43s. Time estimates for 10 more iterations: 8m 18s, 100 more iterations: 1h 23m 7s, 500 more iterations: 6h 55m 37s. [2025-11-13 01:10:57,444][__main__][INFO] - Starting iteration 210. [2025-11-13 01:10:57,953][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 20 and human policies 1. [2025-11-13 01:10:57,953][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 01:11:12,708][__main__][INFO] - Number of regex retries in iteration 210: 0 [2025-11-13 01:11:12,709][__main__][INFO] - agents played in iteration 210 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 01:11:13,554][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:11:13,592][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:11:13,621][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:11:13,647][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:11:13,647][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 01:11:13,648][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 01:11:14,348][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 01:11:14,806][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 01:11:15,312][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 01:11:15,816][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 01:11:16,344][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 01:11:16,848][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 01:11:17,350][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 01:11:17,857][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 01:11:18,359][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 01:11:18,868][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 01:11:19,370][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 01:11:30,937][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 01:11:31,438][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 01:11:31,960][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 01:11:32,463][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 01:11:32,962][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 01:11:33,461][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 01:11:33,960][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 01:11:34,464][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 01:11:34,962][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 01:11:35,459][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 01:11:35,959][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 01:11:36,457][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 01:11:36,956][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 01:11:37,454][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 01:11:37,952][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 01:11:38,456][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 01:11:38,954][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 01:11:39,451][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 01:11:39,950][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 01:11:40,448][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 01:11:40,946][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 01:11:41,443][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 01:11:41,940][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 01:11:42,453][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 01:11:42,952][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 01:11:43,451][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 01:11:43,964][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 01:11:44,463][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 01:11:44,962][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 01:11:45,462][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 01:11:45,961][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 01:11:46,467][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10880 tokens. [2025-11-13 01:11:47,110][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.17%, Current % of VRAM taken: 59.63%, Block Peak % of device VRAM: 62.24%, ΔTime: 00:00:32 [2025-11-13 01:11:48,036][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 01:11:48,037][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 01:11:48,052][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 01:11:50,394][__main__][INFO] - Iteration 211 took 52s (28.14% Gen, 67.40% Train). Generation: 14s, Training: 35s. Estimated remaining time: 40h 39m 57s. Estimated total time: 43h 42m 6s. Time estimates for 10 more iterations: 8m 44s, 100 more iterations: 1h 27m 24s, 500 more iterations: 7h 17m 1s. [2025-11-13 01:11:50,397][__main__][INFO] - Starting iteration 211. [2025-11-13 01:11:50,912][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 21 and human policies 1. [2025-11-13 01:11:50,912][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 01:11:55,692][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 01:12:06,162][__main__][INFO] - Number of regex retries in iteration 211: 1 [2025-11-13 01:12:06,162][__main__][INFO] - agents played in iteration 211 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 01:12:06,996][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:12:07,019][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:12:07,042][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:12:07,064][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:12:07,065][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 01:12:07,066][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 01:12:07,767][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 01:12:08,228][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 01:12:08,749][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 01:12:09,254][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 01:12:09,759][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 01:12:10,263][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 01:12:10,771][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 01:12:11,279][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 01:12:11,782][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 01:12:12,281][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 01:12:12,783][mllm.training.trainer_common][INFO] - 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Processing mini-batch 54 of 64 [2025-11-13 01:12:35,385][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 01:12:35,884][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 01:12:36,391][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 01:12:36,889][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 01:12:37,388][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 01:12:37,886][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 01:12:38,383][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 01:12:38,885][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 01:12:39,384][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 01:12:39,882][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10875 tokens. [2025-11-13 01:12:40,532][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.21%, Current % of VRAM taken: 59.67%, Block Peak % of device VRAM: 62.31%, ΔTime: 00:00:32 [2025-11-13 01:12:41,461][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 01:12:41,463][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 01:12:41,482][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 01:12:42,694][__main__][INFO] - Iteration 212 took 51s (29.45% Gen, 68.21% Train). Generation: 15s, Training: 35s. Estimated remaining time: 40h 6m 6s. Estimated total time: 43h 9m 7s. Time estimates for 10 more iterations: 8m 37s, 100 more iterations: 1h 26m 18s, 500 more iterations: 7h 11m 31s. [2025-11-13 01:12:42,696][__main__][INFO] - Starting iteration 212. [2025-11-13 01:12:43,169][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 21 and human policies 1. [2025-11-13 01:12:43,170][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 01:12:52,071][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 01:12:57,538][__main__][INFO] - Number of regex retries in iteration 212: 1 [2025-11-13 01:12:57,539][__main__][INFO] - agents played in iteration 212 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 01:12:58,346][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:12:58,371][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:12:58,395][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:12:58,417][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:12:58,418][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 01:12:58,419][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 01:12:59,136][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 01:12:59,596][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 01:13:00,106][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 01:13:00,606][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 01:13:01,115][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 01:13:01,618][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 01:13:02,118][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 01:13:02,623][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 01:13:03,125][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 01:13:03,626][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 01:13:04,127][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 01:13:04,627][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 01:13:05,131][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 01:13:05,635][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 01:13:06,139][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 01:13:06,648][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 01:13:07,147][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 01:13:07,649][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 01:13:08,152][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 01:13:08,654][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 01:13:09,172][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 01:13:09,674][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 01:13:15,708][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 01:13:16,208][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 01:13:16,708][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 01:13:17,210][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 01:13:17,709][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 01:13:18,210][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 01:13:18,710][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 01:13:19,210][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 01:13:19,711][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 01:13:20,214][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 01:13:20,714][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 01:13:21,213][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 01:13:21,715][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 01:13:22,216][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 01:13:22,714][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 01:13:23,217][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 01:13:23,720][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 01:13:24,219][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 01:13:24,721][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 01:13:25,221][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 01:13:25,720][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 01:13:26,223][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 01:13:26,725][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 01:13:27,229][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 01:13:27,733][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 01:13:28,235][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 01:13:28,734][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 01:13:29,235][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 01:13:29,734][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 01:13:30,237][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 01:13:30,740][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 01:13:31,240][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10877 tokens. [2025-11-13 01:13:31,909][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.19%, Current % of VRAM taken: 59.64%, Block Peak % of device VRAM: 62.11%, ΔTime: 00:00:32 [2025-11-13 01:13:32,674][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 01:13:32,675][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 01:13:32,677][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 01:13:33,655][__main__][INFO] - Iteration 213 took 50s (28.46% Gen, 69.60% Train). Generation: 14s, Training: 35s. Estimated remaining time: 39h 0m 27s. Estimated total time: 42h 4m 19s. Time estimates for 10 more iterations: 8m 24s, 100 more iterations: 1h 24m 8s, 500 more iterations: 7h 0m 43s. [2025-11-13 01:13:33,657][__main__][INFO] - Starting iteration 213. [2025-11-13 01:13:34,165][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 21 and human policies 1. [2025-11-13 01:13:34,165][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 01:13:38,809][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 01:13:45,300][mllm.models.large_language_model_local][WARNING] - Response Given the values, since I have a higher value for balls (10) compared to Bob, and Bob has a higher value for hats (10) and books (1), it might be strategic to propose keeping more items that I value highly while allowing Bob to take more of the items he values. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 01:13:49,529][__main__][INFO] - Number of regex retries in iteration 213: 2 [2025-11-13 01:13:49,530][__main__][INFO] - agents played in iteration 213 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 01:13:50,413][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:13:50,437][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:13:50,460][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:13:50,482][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:13:50,483][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 01:13:50,484][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 01:13:51,179][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 01:13:51,641][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 01:13:52,149][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 01:13:52,654][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 01:13:53,159][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 01:13:53,662][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 01:13:54,179][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 01:13:54,682][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 01:13:55,195][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 01:13:55,695][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 01:13:56,197][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 01:14:07,738][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 01:14:08,240][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 01:14:08,739][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 01:14:09,238][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 01:14:09,739][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 01:14:10,241][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 01:14:10,741][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 01:14:11,240][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 01:14:11,739][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 01:14:12,238][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 01:14:12,737][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 01:14:13,235][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 01:14:13,735][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 01:14:14,234][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 01:14:14,733][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 01:14:15,232][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 01:14:15,730][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 01:14:16,229][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 01:14:16,726][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 01:14:17,225][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 01:14:17,724][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 01:14:18,222][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 01:14:18,722][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 01:14:19,219][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 01:14:19,718][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 01:14:20,220][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 01:14:20,719][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 01:14:21,216][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 01:14:21,714][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 01:14:22,212][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 01:14:22,715][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 01:14:23,214][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10878 tokens. [2025-11-13 01:14:23,867][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.18%, Current % of VRAM taken: 59.64%, Block Peak % of device VRAM: 62.34%, ΔTime: 00:00:32 [2025-11-13 01:14:24,623][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 01:14:24,625][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 01:14:24,627][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 01:14:25,591][__main__][INFO] - Iteration 214 took 51s (29.88% Gen, 68.25% Train). Generation: 15s, Training: 35s. Estimated remaining time: 39h 46m 37s. Estimated total time: 42h 51m 21s. Time estimates for 10 more iterations: 8m 34s, 100 more iterations: 1h 25m 42s, 500 more iterations: 7h 8m 33s. [2025-11-13 01:14:25,593][__main__][INFO] - Starting iteration 214. [2025-11-13 01:14:26,087][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 21 and human policies 1. [2025-11-13 01:14:26,087][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 01:14:30,100][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 01:14:40,230][__main__][INFO] - Number of regex retries in iteration 214: 1 [2025-11-13 01:14:40,231][__main__][INFO] - agents played in iteration 214 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 01:14:41,183][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:14:41,205][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:14:41,228][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:14:41,250][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:14:41,250][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 01:14:41,251][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 01:14:41,958][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 01:14:42,419][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 01:14:42,928][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 01:14:43,432][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 01:14:43,939][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 01:14:44,444][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 01:14:44,951][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 01:14:45,455][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 01:14:45,960][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 01:14:46,477][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 01:14:46,982][mllm.training.trainer_common][INFO] - 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Processing mini-batch 43 of 64 [2025-11-13 01:15:04,102][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 01:15:04,608][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 01:15:05,108][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 01:15:05,609][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 01:15:06,111][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 01:15:06,611][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 01:15:07,111][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 01:15:07,610][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 01:15:08,109][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 01:15:08,616][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 01:15:09,115][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 01:15:09,613][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 01:15:10,112][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 01:15:10,612][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 01:15:11,116][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 01:15:11,614][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 01:15:12,114][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 01:15:12,613][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 01:15:13,112][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 01:15:13,614][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 01:15:14,114][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10878 tokens. [2025-11-13 01:15:14,766][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.23%, Current % of VRAM taken: 59.68%, Block Peak % of device VRAM: 62.35%, ΔTime: 00:00:32 [2025-11-13 01:15:15,542][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 01:15:15,543][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 01:15:15,545][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 01:15:16,452][__main__][INFO] - Iteration 215 took 50s (28.08% Gen, 70.12% Train). Generation: 14s, Training: 35s. Estimated remaining time: 38h 52m 43s. Estimated total time: 41h 58m 18s. Time estimates for 10 more iterations: 8m 23s, 100 more iterations: 1h 23m 56s, 500 more iterations: 6h 59m 43s. [2025-11-13 01:15:16,454][__main__][INFO] - Starting iteration 215. [2025-11-13 01:15:16,928][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 21 and human policies 1. [2025-11-13 01:15:16,928][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 01:15:25,068][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 01:15:32,826][__main__][INFO] - Number of regex retries in iteration 215: 1 [2025-11-13 01:15:32,827][__main__][INFO] - agents played in iteration 215 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 01:15:33,626][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:15:33,653][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:15:33,681][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:15:33,704][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:15:33,704][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 01:15:33,705][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 01:15:34,367][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 01:15:34,826][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 01:15:35,333][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 01:15:35,839][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 01:15:36,343][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 01:15:36,846][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 01:15:37,354][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 01:15:37,855][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 01:15:38,356][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 01:15:38,876][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 01:15:39,378][mllm.training.trainer_common][INFO] - 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Processing mini-batch 54 of 64 [2025-11-13 01:16:02,032][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 01:16:02,530][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 01:16:03,029][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 01:16:03,531][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 01:16:04,036][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 01:16:04,536][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 01:16:05,036][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 01:16:05,539][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 01:16:06,039][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 01:16:06,539][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10880 tokens. [2025-11-13 01:16:07,188][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.28%, Current % of VRAM taken: 59.74%, Block Peak % of device VRAM: 62.39%, ΔTime: 00:00:32 [2025-11-13 01:16:07,970][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 01:16:07,972][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 01:16:07,973][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 01:16:08,954][__main__][INFO] - Iteration 216 took 52s (30.56% Gen, 67.55% Train). Generation: 15s, Training: 35s. Estimated remaining time: 40h 14m 53s. Estimated total time: 43h 21m 20s. Time estimates for 10 more iterations: 8m 40s, 100 more iterations: 1h 26m 42s, 500 more iterations: 7h 13m 33s. [2025-11-13 01:16:08,956][__main__][INFO] - Starting iteration 216. [2025-11-13 01:16:09,457][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 21 and human policies 1. [2025-11-13 01:16:09,458][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 01:16:24,856][__main__][INFO] - Number of regex retries in iteration 216: 0 [2025-11-13 01:16:24,857][__main__][INFO] - agents played in iteration 216 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 01:16:25,634][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:16:25,664][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:16:25,692][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:16:25,716][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:16:25,716][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 01:16:25,717][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 01:16:26,461][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 01:16:26,922][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 01:16:27,438][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 01:16:27,941][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 01:16:28,449][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 01:16:28,955][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 01:16:29,457][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 01:16:29,963][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 01:16:30,470][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 01:16:30,975][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 01:16:31,482][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 01:16:31,985][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 01:16:32,490][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 01:16:32,995][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 01:16:33,498][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 01:16:34,002][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 01:16:34,507][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 01:16:35,014][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 01:16:35,524][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 01:16:36,026][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 01:16:36,527][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 01:16:37,036][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 01:16:37,537][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 01:16:38,041][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 01:16:38,541][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 01:16:39,042][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 01:16:39,546][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 01:16:40,049][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 01:16:40,554][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 01:16:41,057][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 01:16:41,558][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 01:16:42,060][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 01:16:42,561][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 01:16:43,064][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 01:16:43,569][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 01:16:44,073][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 01:16:44,576][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 01:16:45,078][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 01:16:45,579][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 01:16:46,086][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 01:16:46,604][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 01:16:47,108][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 01:16:47,612][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 01:16:48,118][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 01:16:48,619][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 01:16:49,125][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 01:16:49,624][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 01:16:50,124][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 01:16:50,633][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 01:16:51,132][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 01:16:51,630][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 01:16:52,130][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 01:16:52,631][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 01:16:53,135][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 01:16:53,635][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 01:16:54,135][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 01:16:54,633][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 01:16:55,133][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 01:16:55,633][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 01:16:56,133][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 01:16:56,632][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 01:16:57,135][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 01:16:57,635][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 01:16:58,136][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 01:16:58,637][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10878 tokens. [2025-11-13 01:16:59,351][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.22%, Current % of VRAM taken: 59.67%, Block Peak % of device VRAM: 62.30%, ΔTime: 00:00:32 [2025-11-13 01:17:00,135][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 01:17:00,136][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 01:17:00,138][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 01:17:01,059][__main__][INFO] - Iteration 217 took 51s (29.84% Gen, 68.37% Train). Generation: 15s, Training: 35s. Estimated remaining time: 39h 52m 47s. Estimated total time: 43h 0m 6s. Time estimates for 10 more iterations: 8m 36s, 100 more iterations: 1h 26m 0s, 500 more iterations: 7h 10m 1s. [2025-11-13 01:17:01,061][__main__][INFO] - Starting iteration 217. [2025-11-13 01:17:01,559][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 21 and human policies 1. [2025-11-13 01:17:01,559][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 01:17:06,497][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 01:17:13,429][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 01:17:17,842][__main__][INFO] - Number of regex retries in iteration 217: 2 [2025-11-13 01:17:17,843][__main__][INFO] - agents played in iteration 217 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 01:17:18,671][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:17:18,699][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:17:18,724][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:17:18,747][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:17:18,748][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 01:17:18,748][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 01:17:19,422][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 01:17:19,884][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 01:17:20,397][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 01:17:20,903][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 01:17:21,409][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 01:17:21,909][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 01:17:22,421][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 01:17:22,927][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 01:17:23,439][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 01:17:23,942][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 01:17:24,444][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 01:17:24,947][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 01:17:25,451][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 01:17:25,955][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 01:17:26,456][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 01:17:26,959][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 01:17:27,463][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 01:17:27,965][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 01:17:28,466][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 01:17:28,968][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 01:17:29,472][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 01:17:29,976][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 01:17:36,021][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 01:17:36,522][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 01:17:37,023][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 01:17:37,522][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 01:17:38,030][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 01:17:38,535][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 01:17:39,037][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 01:17:39,540][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 01:17:40,046][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 01:17:40,549][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 01:17:41,053][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 01:17:41,555][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 01:17:42,059][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 01:17:42,559][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 01:17:43,058][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 01:17:43,558][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 01:17:44,056][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 01:17:44,556][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 01:17:45,054][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 01:17:45,553][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 01:17:46,054][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 01:17:46,554][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 01:17:47,053][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 01:17:47,554][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 01:17:48,054][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 01:17:48,556][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 01:17:49,055][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 01:17:49,555][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 01:17:50,058][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 01:17:50,557][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 01:17:51,057][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 01:17:51,557][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10877 tokens. [2025-11-13 01:17:52,250][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.19%, Current % of VRAM taken: 59.65%, Block Peak % of device VRAM: 62.08%, ΔTime: 00:00:32 [2025-11-13 01:17:53,050][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 01:17:53,052][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 01:17:53,053][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 01:17:54,047][__main__][INFO] - Iteration 218 took 52s (31.02% Gen, 67.08% Train). Generation: 16s, Training: 35s. Estimated remaining time: 40h 36m 12s. Estimated total time: 43h 44m 25s. Time estimates for 10 more iterations: 8m 44s, 100 more iterations: 1h 27m 28s, 500 more iterations: 7h 17m 24s. [2025-11-13 01:17:54,049][__main__][INFO] - Starting iteration 218. [2025-11-13 01:17:54,527][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 21 and human policies 1. [2025-11-13 01:17:54,528][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 01:18:08,823][__main__][INFO] - Number of regex retries in iteration 218: 0 [2025-11-13 01:18:08,824][__main__][INFO] - agents played in iteration 218 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 01:18:09,653][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:18:09,675][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:18:09,698][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:18:09,720][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:18:09,721][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 01:18:09,722][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 01:18:10,388][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 01:18:10,854][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 01:18:11,359][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 01:18:11,860][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 01:18:12,364][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 01:18:12,873][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 01:18:13,378][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 01:18:13,882][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 01:18:14,390][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 01:18:14,894][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 01:18:15,400][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 01:18:27,036][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 01:18:27,539][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 01:18:28,042][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 01:18:28,544][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 01:18:29,051][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 01:18:29,554][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 01:18:30,056][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 01:18:30,558][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 01:18:31,062][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 01:18:31,567][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 01:18:32,070][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 01:18:32,572][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 01:18:33,079][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 01:18:33,584][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 01:18:34,088][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 01:18:34,591][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 01:18:35,092][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 01:18:35,600][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 01:18:36,106][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 01:18:36,611][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 01:18:37,119][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 01:18:37,622][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 01:18:38,130][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 01:18:38,630][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 01:18:39,130][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 01:18:39,635][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 01:18:40,133][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 01:18:40,632][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 01:18:41,131][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 01:18:41,634][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 01:18:42,144][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 01:18:42,642][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10879 tokens. [2025-11-13 01:18:43,359][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.14%, Current % of VRAM taken: 59.59%, Block Peak % of device VRAM: 62.41%, ΔTime: 00:00:32 [2025-11-13 01:18:44,142][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 01:18:44,143][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 01:18:44,145][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 01:18:45,071][__main__][INFO] - Iteration 219 took 50s (28.28% Gen, 69.88% Train). Generation: 14s, Training: 35s. Estimated remaining time: 38h 58m 9s. Estimated total time: 42h 7m 12s. Time estimates for 10 more iterations: 8m 25s, 100 more iterations: 1h 24m 14s, 500 more iterations: 7h 1m 12s. [2025-11-13 01:18:45,073][__main__][INFO] - Starting iteration 219. [2025-11-13 01:18:45,558][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 21 and human policies 1. [2025-11-13 01:18:45,559][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 01:18:49,852][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 01:18:59,948][__main__][INFO] - Number of regex retries in iteration 219: 1 [2025-11-13 01:18:59,949][__main__][INFO] - agents played in iteration 219 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 01:19:00,743][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:19:00,770][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:19:00,797][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:19:00,820][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:19:00,820][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 01:19:00,821][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 01:19:01,470][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 01:19:01,934][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 01:19:02,441][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 01:19:02,945][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 01:19:03,467][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 01:19:03,982][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 01:19:04,493][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 01:19:04,998][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 01:19:05,502][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 01:19:06,018][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 01:19:06,520][mllm.training.trainer_common][INFO] - 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Processing mini-batch 43 of 64 [2025-11-13 01:19:23,682][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 01:19:24,185][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 01:19:24,685][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 01:19:25,186][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 01:19:25,687][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 01:19:26,187][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 01:19:26,690][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 01:19:27,197][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 01:19:27,700][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 01:19:28,201][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 01:19:28,704][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 01:19:29,209][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 01:19:29,714][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 01:19:30,216][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 01:19:30,728][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 01:19:31,229][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 01:19:31,742][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 01:19:32,242][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 01:19:32,744][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 01:19:33,255][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 01:19:33,754][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10876 tokens. [2025-11-13 01:19:34,424][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.23%, Current % of VRAM taken: 59.68%, Block Peak % of device VRAM: 62.41%, ΔTime: 00:00:32 [2025-11-13 01:19:35,188][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 01:19:35,189][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 01:19:35,191][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 01:19:36,177][__main__][INFO] - Iteration 220 took 50s (28.43% Gen, 69.62% Train). Generation: 14s, Training: 35s. Estimated remaining time: 39h 1m 2s. Estimated total time: 42h 10m 57s. Time estimates for 10 more iterations: 8m 26s, 100 more iterations: 1h 24m 21s, 500 more iterations: 7h 1m 49s. [2025-11-13 01:19:36,179][__main__][INFO] - Starting iteration 220. [2025-11-13 01:19:36,663][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 21 and human policies 1. [2025-11-13 01:19:36,663][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 01:19:51,677][__main__][INFO] - Number of regex retries in iteration 220: 0 [2025-11-13 01:19:51,677][__main__][INFO] - agents played in iteration 220 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 01:19:52,483][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:19:52,506][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:19:52,532][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:19:52,553][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:19:52,554][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 01:19:52,555][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 01:19:53,220][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 01:19:53,677][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 01:19:54,185][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 01:19:54,688][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 01:19:55,194][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 01:19:55,696][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 01:19:56,196][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 01:19:56,697][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 01:19:57,197][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 01:19:57,698][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 01:19:58,197][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 01:20:09,790][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 01:20:10,294][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 01:20:10,798][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 01:20:11,302][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 01:20:11,805][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 01:20:12,307][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 01:20:12,811][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 01:20:13,315][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 01:20:13,820][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 01:20:14,323][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 01:20:14,825][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 01:20:15,325][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 01:20:15,827][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 01:20:16,331][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 01:20:16,834][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 01:20:17,337][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 01:20:17,863][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 01:20:18,364][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 01:20:18,868][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 01:20:19,374][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 01:20:19,886][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 01:20:20,392][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 01:20:20,897][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 01:20:21,401][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 01:20:21,905][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 01:20:22,408][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 01:20:22,912][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 01:20:23,414][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 01:20:23,916][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 01:20:24,428][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 01:20:24,932][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 01:20:25,434][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10878 tokens. [2025-11-13 01:20:26,105][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.25%, Current % of VRAM taken: 59.70%, Block Peak % of device VRAM: 62.32%, ΔTime: 00:00:32 [2025-11-13 01:20:26,909][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 01:20:26,910][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 01:20:26,912][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 01:20:28,664][__main__][INFO] - Iteration 221 took 52s (28.87% Gen, 67.76% Train). Generation: 15s, Training: 35s. Estimated remaining time: 40h 9m 18s. Estimated total time: 43h 20m 5s. Time estimates for 10 more iterations: 8m 40s, 100 more iterations: 1h 26m 40s, 500 more iterations: 7h 13m 20s. [2025-11-13 01:20:28,666][__main__][INFO] - Starting iteration 221. [2025-11-13 01:20:29,151][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 22 and human policies 1. [2025-11-13 01:20:29,151][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 01:20:38,260][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 01:20:42,544][__main__][INFO] - Number of regex retries in iteration 221: 1 [2025-11-13 01:20:42,544][__main__][INFO] - agents played in iteration 221 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 01:20:43,442][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:20:43,464][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:20:43,486][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:20:43,508][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:20:43,509][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 01:20:43,510][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 01:20:44,148][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 01:20:44,605][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 01:20:45,111][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 01:20:45,611][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 01:20:46,115][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 01:20:46,618][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 01:20:47,123][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 01:20:47,627][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 01:20:48,141][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 01:20:48,639][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 01:20:49,136][mllm.training.trainer_common][INFO] - 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Processing mini-batch 54 of 64 [2025-11-13 01:21:11,824][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 01:21:12,330][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 01:21:12,832][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 01:21:13,344][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 01:21:13,847][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 01:21:14,352][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 01:21:14,857][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 01:21:15,358][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 01:21:15,871][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 01:21:16,373][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10879 tokens. [2025-11-13 01:21:17,068][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.24%, Current % of VRAM taken: 59.69%, Block Peak % of device VRAM: 62.34%, ΔTime: 00:00:32 [2025-11-13 01:21:17,853][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 01:21:17,854][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 01:21:17,856][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 01:21:18,846][__main__][INFO] - Iteration 222 took 49s (26.95% Gen, 71.06% Train). Generation: 13s, Training: 35s. Estimated remaining time: 38h 13m 11s. Estimated total time: 41h 24m 48s. Time estimates for 10 more iterations: 8m 16s, 100 more iterations: 1h 22m 49s, 500 more iterations: 6h 54m 8s. [2025-11-13 01:21:18,848][__main__][INFO] - Starting iteration 222. [2025-11-13 01:21:19,321][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 22 and human policies 1. [2025-11-13 01:21:19,322][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 01:21:28,397][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 01:21:32,547][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's optimal to propose the distribution that matches our per-item values closely while also considering Bob's values. Since we both value hats at 1 and Bob values them at 10, and we both value books and balls highly at 10, it's a balanced strategy to keep the items proportionate to their values. Here’s the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 01:21:33,515][__main__][INFO] - Number of regex retries in iteration 222: 2 [2025-11-13 01:21:33,515][__main__][INFO] - agents played in iteration 222 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 01:21:34,289][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:21:34,313][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:21:34,338][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:21:34,359][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:21:34,360][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 01:21:34,361][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 01:21:34,996][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 01:21:35,454][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 01:21:35,966][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 01:21:36,468][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 01:21:36,982][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 01:21:37,483][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 01:21:37,984][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 01:21:38,498][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 01:21:39,002][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 01:21:39,506][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 01:21:40,008][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 01:21:51,543][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 01:21:52,045][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 01:21:52,548][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 01:21:53,063][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 01:21:53,570][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 01:21:54,087][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 01:21:54,590][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 01:21:55,091][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 01:21:55,594][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 01:21:56,096][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 01:21:56,607][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 01:21:57,111][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 01:21:57,614][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 01:21:58,126][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 01:21:58,631][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 01:21:59,134][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 01:21:59,638][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 01:22:00,139][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 01:22:00,641][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 01:22:01,142][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 01:22:01,646][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 01:22:02,153][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 01:22:02,657][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 01:22:03,160][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 01:22:03,671][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 01:22:04,179][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 01:22:04,703][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 01:22:05,207][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 01:22:05,713][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 01:22:06,217][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 01:22:06,720][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 01:22:07,226][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10879 tokens. [2025-11-13 01:22:07,904][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.36%, Current % of VRAM taken: 59.81%, Block Peak % of device VRAM: 62.61%, ΔTime: 00:00:32 [2025-11-13 01:22:08,705][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 01:22:08,707][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 01:22:08,709][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 01:22:09,616][__main__][INFO] - Iteration 223 took 50s (28.22% Gen, 69.97% Train). Generation: 14s, Training: 35s. Estimated remaining time: 38h 42m 20s. Estimated total time: 41h 54m 48s. Time estimates for 10 more iterations: 8m 22s, 100 more iterations: 1h 23m 49s, 500 more iterations: 6h 59m 8s. [2025-11-13 01:22:09,619][__main__][INFO] - Starting iteration 223. [2025-11-13 01:22:10,110][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 22 and human policies 1. [2025-11-13 01:22:10,111][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 01:22:25,847][__main__][INFO] - Number of regex retries in iteration 223: 0 [2025-11-13 01:22:25,848][__main__][INFO] - agents played in iteration 223 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 01:22:26,694][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.64%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:22:26,721][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.64%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:22:26,747][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.64%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:22:26,770][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.64%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:22:26,771][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 01:22:26,772][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 01:22:27,414][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 01:22:27,870][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 01:22:28,393][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 01:22:28,898][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 01:22:29,401][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 01:22:29,901][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 01:22:30,402][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 01:22:30,917][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 01:22:31,419][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 01:22:31,919][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 01:22:32,419][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 01:22:32,928][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 01:22:33,432][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 01:22:33,936][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 01:22:34,435][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 01:22:34,937][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 01:22:35,437][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 01:22:35,937][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 01:22:36,435][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 01:22:36,936][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 01:22:37,435][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 01:22:37,939][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 01:22:38,439][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 01:22:38,945][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 01:22:39,444][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 01:22:39,944][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 01:22:40,444][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 01:22:40,946][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 01:22:41,448][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 01:22:41,949][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 01:22:42,451][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 01:22:42,952][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 01:22:43,454][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 01:22:43,957][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 01:22:44,460][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 01:22:44,961][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 01:22:45,463][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 01:22:45,963][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 01:22:46,463][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 01:22:46,975][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 01:22:47,478][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 01:22:48,004][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 01:22:48,507][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 01:22:49,010][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 01:22:49,519][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 01:22:50,023][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 01:22:50,528][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 01:22:51,030][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 01:22:51,534][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 01:22:52,043][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 01:22:52,545][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 01:22:53,047][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 01:22:53,549][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 01:22:54,050][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 01:22:54,550][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 01:22:55,051][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 01:22:55,554][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 01:22:56,060][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 01:22:56,560][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 01:22:57,059][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 01:22:57,558][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 01:22:58,058][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 01:22:58,557][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 01:22:59,059][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 01:22:59,558][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10876 tokens. [2025-11-13 01:23:00,247][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.19%, Current % of VRAM taken: 59.64%, Block Peak % of device VRAM: 62.30%, ΔTime: 00:00:32 [2025-11-13 01:23:01,020][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 01:23:01,022][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 01:23:01,024][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 01:23:02,015][__main__][INFO] - Iteration 224 took 51s (30.32% Gen, 67.77% Train). Generation: 15s, Training: 35s. Estimated remaining time: 40h 1m 56s. Estimated total time: 43h 15m 16s. Time estimates for 10 more iterations: 8m 39s, 100 more iterations: 1h 26m 30s, 500 more iterations: 7h 12m 32s. [2025-11-13 01:23:02,017][__main__][INFO] - Starting iteration 224. [2025-11-13 01:23:02,484][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 22 and human policies 1. [2025-11-13 01:23:02,485][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 01:23:17,398][__main__][INFO] - Number of regex retries in iteration 224: 0 [2025-11-13 01:23:17,399][__main__][INFO] - agents played in iteration 224 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 01:23:18,226][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:23:18,248][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:23:18,270][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:23:18,292][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:23:18,293][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 01:23:18,294][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 01:23:18,974][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 01:23:19,431][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 01:23:19,940][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 01:23:20,441][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 01:23:20,951][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 01:23:21,458][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 01:23:21,962][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 01:23:22,461][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 01:23:22,963][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 01:23:23,470][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 01:23:23,981][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 01:23:24,487][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 01:23:24,995][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 01:23:25,494][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 01:23:25,997][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 01:23:26,514][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 01:23:27,019][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 01:23:27,523][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 01:23:28,027][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 01:23:28,528][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 01:23:29,036][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 01:23:29,538][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 01:23:30,039][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 01:23:30,549][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 01:23:31,051][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 01:23:31,553][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 01:23:32,056][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 01:23:32,554][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 01:23:33,056][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 01:23:33,560][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 01:23:34,065][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 01:23:34,568][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 01:23:35,071][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 01:23:35,575][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 01:23:36,078][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 01:23:36,579][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 01:23:37,084][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 01:23:37,585][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 01:23:38,087][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 01:23:38,600][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 01:23:39,099][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 01:23:39,604][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 01:23:40,103][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 01:23:40,601][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 01:23:41,100][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 01:23:41,602][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 01:23:42,101][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 01:23:42,602][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 01:23:43,104][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 01:23:43,628][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 01:23:44,128][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 01:23:44,629][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 01:23:45,139][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 01:23:45,638][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 01:23:46,140][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 01:23:46,642][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 01:23:47,145][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 01:23:47,647][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 01:23:48,148][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 01:23:48,649][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 01:23:49,148][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 01:23:49,645][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 01:23:50,145][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 01:23:50,644][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 01:23:51,144][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10879 tokens. [2025-11-13 01:23:51,867][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.16%, Current % of VRAM taken: 59.61%, Block Peak % of device VRAM: 62.31%, ΔTime: 00:00:32 [2025-11-13 01:23:52,650][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 01:23:52,653][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 01:23:52,655][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 01:23:53,690][__main__][INFO] - Iteration 225 took 51s (29.13% Gen, 68.85% Train). Generation: 14s, Training: 35s. Estimated remaining time: 39h 26m 7s. Estimated total time: 42h 40m 19s. Time estimates for 10 more iterations: 8m 32s, 100 more iterations: 1h 25m 20s, 500 more iterations: 7h 6m 43s. [2025-11-13 01:23:53,692][__main__][INFO] - Starting iteration 225. [2025-11-13 01:23:54,171][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 22 and human policies 1. [2025-11-13 01:23:54,171][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 01:24:07,870][__main__][INFO] - Number of regex retries in iteration 225: 0 [2025-11-13 01:24:07,871][__main__][INFO] - agents played in iteration 225 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 01:24:08,658][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:24:08,685][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:24:08,711][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:24:08,734][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:24:08,735][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 01:24:08,736][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 01:24:09,389][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 01:24:09,849][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 01:24:10,356][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 01:24:10,857][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 01:24:11,360][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 01:24:11,862][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 01:24:12,364][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 01:24:12,865][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 01:24:13,367][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 01:24:13,875][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 01:24:14,377][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 01:24:14,878][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 01:24:15,382][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 01:24:15,888][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 01:24:16,388][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 01:24:16,889][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 01:24:17,389][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 01:24:17,896][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 01:24:18,399][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 01:24:18,900][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 01:24:19,401][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 01:24:19,903][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 01:24:20,408][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 01:24:20,909][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 01:24:21,412][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 01:24:21,929][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 01:24:22,432][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 01:24:22,935][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 01:24:23,437][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 01:24:23,938][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 01:24:24,444][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 01:24:24,947][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 01:24:25,454][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 01:24:25,964][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 01:24:26,468][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 01:24:26,973][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 01:24:27,475][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 01:24:27,980][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 01:24:28,487][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 01:24:28,990][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 01:24:29,494][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 01:24:29,996][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 01:24:30,498][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 01:24:31,003][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 01:24:31,503][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 01:24:32,004][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 01:24:32,513][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 01:24:33,014][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 01:24:33,515][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 01:24:34,023][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 01:24:34,542][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 01:24:35,057][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 01:24:35,561][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 01:24:36,066][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 01:24:36,568][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 01:24:37,075][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 01:24:37,583][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 01:24:38,086][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 01:24:38,590][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 01:24:39,094][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 01:24:39,597][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 01:24:40,102][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 01:24:40,606][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 01:24:41,109][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 01:24:41,611][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10878 tokens. [2025-11-13 01:24:42,301][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.27%, Current % of VRAM taken: 59.72%, Block Peak % of device VRAM: 62.41%, ΔTime: 00:00:32 [2025-11-13 01:24:43,086][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 01:24:43,088][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 01:24:43,090][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 01:24:43,972][__main__][INFO] - Iteration 226 took 49s (27.51% Gen, 70.72% Train). Generation: 13s, Training: 35s. Estimated remaining time: 38h 15m 4s. Estimated total time: 41h 30m 6s. Time estimates for 10 more iterations: 8m 18s, 100 more iterations: 1h 23m 0s, 500 more iterations: 6h 55m 1s. [2025-11-13 01:24:43,975][__main__][INFO] - Starting iteration 226. [2025-11-13 01:24:44,460][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 22 and human policies 1. [2025-11-13 01:24:44,460][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 01:24:48,143][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 01:24:54,388][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 01:24:58,706][__main__][INFO] - Number of regex retries in iteration 226: 2 [2025-11-13 01:24:58,706][__main__][INFO] - agents played in iteration 226 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 01:24:59,488][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:24:59,514][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:24:59,541][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:24:59,564][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:24:59,564][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 01:24:59,565][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 01:25:00,219][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 01:25:00,677][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 01:25:01,182][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 01:25:01,685][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 01:25:02,186][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 01:25:02,692][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 01:25:03,194][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 01:25:03,695][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 01:25:04,197][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 01:25:04,699][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 01:25:05,206][mllm.training.trainer_common][INFO] - 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Processing mini-batch 43 of 64 [2025-11-13 01:25:22,305][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 01:25:22,804][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 01:25:23,302][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 01:25:23,802][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 01:25:24,300][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 01:25:24,800][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 01:25:25,301][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 01:25:25,800][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 01:25:26,299][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 01:25:26,799][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 01:25:27,300][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 01:25:27,801][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 01:25:28,304][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 01:25:28,807][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 01:25:29,309][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 01:25:29,811][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 01:25:30,315][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 01:25:30,818][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 01:25:31,341][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 01:25:31,841][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 01:25:32,344][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10877 tokens. [2025-11-13 01:25:33,069][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.26%, Current % of VRAM taken: 59.71%, Block Peak % of device VRAM: 62.41%, ΔTime: 00:00:32 [2025-11-13 01:25:33,827][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 01:25:33,828][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 01:25:33,830][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 01:25:34,746][__main__][INFO] - Iteration 227 took 50s (28.33% Gen, 69.85% Train). Generation: 14s, Training: 35s. Estimated remaining time: 38h 38m 26s. Estimated total time: 41h 54m 19s. Time estimates for 10 more iterations: 8m 22s, 100 more iterations: 1h 23m 48s, 500 more iterations: 6h 59m 3s. [2025-11-13 01:25:34,748][__main__][INFO] - Starting iteration 227. [2025-11-13 01:25:35,249][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 22 and human policies 1. [2025-11-13 01:25:35,250][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 01:25:48,801][__main__][INFO] - Number of regex retries in iteration 227: 0 [2025-11-13 01:25:48,802][__main__][INFO] - agents played in iteration 227 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 01:25:49,575][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:25:49,600][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:25:49,623][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:25:49,645][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:25:49,646][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 01:25:49,647][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 01:25:50,333][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 01:25:50,793][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 01:25:51,303][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 01:25:51,806][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 01:25:52,309][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 01:25:52,811][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 01:25:53,312][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 01:25:53,814][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 01:25:54,316][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 01:25:54,821][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 01:25:55,322][mllm.training.trainer_common][INFO] - 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Processing mini-batch 21 of 64 [2025-11-13 01:26:01,362][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 01:26:01,862][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 01:26:02,364][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 01:26:02,875][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 01:26:03,378][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 01:26:03,878][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 01:26:04,378][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 01:26:04,878][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 01:26:05,380][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 01:26:05,881][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 01:26:06,380][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 01:26:06,880][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 01:26:07,379][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 01:26:07,879][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 01:26:08,378][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 01:26:08,878][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 01:26:09,382][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 01:26:09,880][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 01:26:10,380][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 01:26:10,879][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 01:26:11,379][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 01:26:11,886][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 01:26:12,388][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 01:26:12,890][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 01:26:13,394][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 01:26:13,896][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 01:26:14,401][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 01:26:14,902][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 01:26:15,402][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 01:26:15,903][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 01:26:16,405][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 01:26:16,904][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 01:26:17,407][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 01:26:17,906][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 01:26:18,404][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 01:26:18,907][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 01:26:19,408][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 01:26:19,909][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 01:26:20,407][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 01:26:20,909][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 01:26:21,413][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 01:26:21,914][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 01:26:22,414][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10879 tokens. [2025-11-13 01:26:23,090][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.17%, Current % of VRAM taken: 59.62%, Block Peak % of device VRAM: 62.24%, ΔTime: 00:00:32 [2025-11-13 01:26:23,904][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 01:26:23,905][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 01:26:23,907][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 01:26:24,940][__main__][INFO] - Iteration 228 took 49s (27.27% Gen, 70.64% Train). Generation: 13s, Training: 35s. Estimated remaining time: 38h 7m 54s. Estimated total time: 41h 24m 37s. Time estimates for 10 more iterations: 8m 16s, 100 more iterations: 1h 22m 49s, 500 more iterations: 6h 54m 6s. [2025-11-13 01:26:24,943][__main__][INFO] - Starting iteration 228. [2025-11-13 01:26:25,430][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 22 and human policies 1. [2025-11-13 01:26:25,431][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 01:26:40,615][__main__][INFO] - Number of regex retries in iteration 228: 0 [2025-11-13 01:26:40,616][__main__][INFO] - agents played in iteration 228 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 01:26:41,403][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:26:41,425][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:26:41,450][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:26:41,472][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:26:41,473][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 01:26:41,474][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 01:26:42,160][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 01:26:42,617][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 01:26:43,123][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 01:26:43,623][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 01:26:44,125][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 01:26:44,630][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 01:26:45,134][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 01:26:45,636][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 01:26:46,150][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 01:26:46,652][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 01:26:47,170][mllm.training.trainer_common][INFO] - 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Processing mini-batch 21 of 64 [2025-11-13 01:26:53,188][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 01:26:53,690][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 01:26:54,190][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 01:26:54,693][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 01:26:55,200][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 01:26:55,700][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 01:26:56,200][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 01:26:56,700][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 01:26:57,203][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 01:26:57,704][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 01:26:58,202][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 01:26:58,702][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 01:26:59,202][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 01:26:59,705][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 01:27:00,206][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 01:27:00,709][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 01:27:01,208][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 01:27:01,709][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 01:27:02,209][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 01:27:02,713][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 01:27:03,215][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 01:27:03,716][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 01:27:04,224][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 01:27:04,728][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 01:27:05,235][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 01:27:05,749][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 01:27:06,250][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 01:27:06,763][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 01:27:07,265][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 01:27:07,768][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 01:27:08,273][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 01:27:08,776][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 01:27:09,278][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 01:27:09,781][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 01:27:10,284][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 01:27:10,787][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 01:27:11,288][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 01:27:11,789][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 01:27:12,296][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 01:27:12,798][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 01:27:13,300][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 01:27:13,803][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 01:27:14,305][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10880 tokens. [2025-11-13 01:27:14,977][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.30%, Current % of VRAM taken: 59.75%, Block Peak % of device VRAM: 62.41%, ΔTime: 00:00:32 [2025-11-13 01:27:15,749][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 01:27:15,751][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 01:27:15,753][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 01:27:16,697][__main__][INFO] - Iteration 229 took 51s (29.62% Gen, 68.54% Train). Generation: 15s, Training: 35s. Estimated remaining time: 39h 25m 46s. Estimated total time: 42h 43m 21s. Time estimates for 10 more iterations: 8m 32s, 100 more iterations: 1h 25m 26s, 500 more iterations: 7h 7m 13s. [2025-11-13 01:27:16,699][__main__][INFO] - Starting iteration 229. [2025-11-13 01:27:17,185][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 22 and human policies 1. [2025-11-13 01:27:17,186][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 01:27:21,167][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 01:27:31,511][__main__][INFO] - Number of regex retries in iteration 229: 1 [2025-11-13 01:27:31,511][__main__][INFO] - agents played in iteration 229 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 01:27:32,344][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:27:32,372][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:27:32,398][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:27:32,422][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:27:32,422][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 01:27:32,423][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 01:27:33,076][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 01:27:33,531][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 01:27:34,036][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 01:27:34,539][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 01:27:35,042][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 01:27:35,542][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 01:27:36,055][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 01:27:36,557][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 01:27:37,068][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 01:27:37,572][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 01:27:38,077][mllm.training.trainer_common][INFO] - 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Processing mini-batch 21 of 64 [2025-11-13 01:27:44,129][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 01:27:44,630][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 01:27:45,134][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 01:27:45,631][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 01:27:46,128][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 01:27:46,626][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 01:27:47,124][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 01:27:47,621][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 01:27:48,119][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 01:27:48,617][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 01:27:49,116][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 01:27:49,617][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 01:27:50,117][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 01:27:50,617][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 01:27:51,118][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 01:27:51,618][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 01:27:52,118][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 01:27:52,616][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 01:27:53,119][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 01:27:53,618][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 01:27:54,116][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 01:27:54,616][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 01:27:55,116][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 01:27:55,615][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 01:27:56,116][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 01:27:56,617][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 01:27:57,124][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 01:27:57,627][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 01:27:58,132][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 01:27:58,637][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 01:27:59,138][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 01:27:59,640][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 01:28:00,141][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 01:28:00,642][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 01:28:01,144][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 01:28:01,645][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 01:28:02,148][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 01:28:02,651][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 01:28:03,152][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 01:28:03,677][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 01:28:04,177][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 01:28:04,678][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 01:28:05,179][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10879 tokens. [2025-11-13 01:28:05,842][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.25%, Current % of VRAM taken: 59.71%, Block Peak % of device VRAM: 62.31%, ΔTime: 00:00:32 [2025-11-13 01:28:06,642][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 01:28:06,644][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 01:28:06,646][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 01:28:07,580][__main__][INFO] - Iteration 230 took 50s (28.43% Gen, 69.72% Train). Generation: 14s, Training: 35s. Estimated remaining time: 38h 41m 20s. Estimated total time: 41h 59m 46s. Time estimates for 10 more iterations: 8m 23s, 100 more iterations: 1h 23m 59s, 500 more iterations: 6h 59m 57s. [2025-11-13 01:28:07,583][__main__][INFO] - Starting iteration 230. [2025-11-13 01:28:08,064][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 22 and human policies 1. [2025-11-13 01:28:08,065][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 01:28:14,788][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 01:28:24,011][__main__][INFO] - Number of regex retries in iteration 230: 1 [2025-11-13 01:28:24,011][__main__][INFO] - agents played in iteration 230 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 01:28:24,838][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:28:24,866][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:28:24,892][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:28:24,916][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:28:24,917][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 01:28:24,918][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 01:28:25,579][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 01:28:26,043][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 01:28:26,551][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 01:28:27,056][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 01:28:27,570][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 01:28:28,078][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 01:28:28,590][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 01:28:29,094][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 01:28:29,597][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 01:28:30,100][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 01:28:30,603][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 01:28:31,106][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 01:28:31,607][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 01:28:32,109][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 01:28:32,614][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 01:28:33,117][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 01:28:33,623][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 01:28:34,126][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 01:28:34,630][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 01:28:35,131][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 01:28:35,631][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 01:28:36,130][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 01:28:42,165][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 01:28:42,666][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 01:28:43,176][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 01:28:43,678][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 01:28:44,179][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 01:28:44,681][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 01:28:45,182][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 01:28:45,684][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 01:28:46,186][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 01:28:46,687][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 01:28:47,199][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 01:28:47,698][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 01:28:48,203][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 01:28:48,704][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 01:28:49,209][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 01:28:49,714][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 01:28:50,217][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 01:28:50,716][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 01:28:51,219][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 01:28:51,720][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 01:28:52,222][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 01:28:52,727][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 01:28:53,228][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 01:28:53,731][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 01:28:54,235][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 01:28:54,737][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 01:28:55,240][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 01:28:55,745][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 01:28:56,246][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 01:28:56,749][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 01:28:57,250][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 01:28:57,767][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10877 tokens. [2025-11-13 01:28:58,445][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.27%, Current % of VRAM taken: 59.73%, Block Peak % of device VRAM: 62.35%, ΔTime: 00:00:32 [2025-11-13 01:28:59,246][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 01:28:59,248][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 01:28:59,250][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 01:29:01,015][__main__][INFO] - Iteration 231 took 52s (30.11% Gen, 66.55% Train). Generation: 15s, Training: 35s. Estimated remaining time: 40h 48m 15s. Estimated total time: 44h 7m 35s. Time estimates for 10 more iterations: 8m 49s, 100 more iterations: 1h 28m 15s, 500 more iterations: 7h 21m 15s. [2025-11-13 01:29:01,017][__main__][INFO] - Starting iteration 231. [2025-11-13 01:29:01,502][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 23 and human policies 1. [2025-11-13 01:29:01,503][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 01:29:08,070][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 01:29:16,602][__main__][INFO] - Number of regex retries in iteration 231: 1 [2025-11-13 01:29:16,603][__main__][INFO] - agents played in iteration 231 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 01:29:17,383][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:29:17,410][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:29:17,437][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:29:17,460][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:29:17,460][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 01:29:17,461][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 01:29:18,107][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 01:29:18,575][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 01:29:19,082][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 01:29:19,584][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 01:29:20,086][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 01:29:20,587][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 01:29:21,091][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 01:29:21,594][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 01:29:22,097][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 01:29:22,609][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 01:29:23,110][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 01:29:34,645][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 01:29:35,144][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 01:29:35,643][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 01:29:36,142][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 01:29:36,641][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 01:29:37,142][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 01:29:37,640][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 01:29:38,141][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 01:29:38,641][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 01:29:39,141][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 01:29:39,642][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 01:29:40,146][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 01:29:40,649][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 01:29:41,153][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 01:29:41,658][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 01:29:42,163][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 01:29:42,666][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 01:29:43,171][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 01:29:43,688][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 01:29:44,192][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 01:29:44,706][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 01:29:45,210][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 01:29:45,715][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 01:29:46,220][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 01:29:46,726][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 01:29:47,226][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 01:29:47,727][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 01:29:48,229][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 01:29:48,737][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 01:29:49,238][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 01:29:49,738][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 01:29:50,239][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10878 tokens. [2025-11-13 01:29:50,895][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.28%, Current % of VRAM taken: 59.73%, Block Peak % of device VRAM: 62.52%, ΔTime: 00:00:32 [2025-11-13 01:29:51,673][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 01:29:51,675][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 01:29:51,677][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 01:29:52,618][__main__][INFO] - Iteration 232 took 51s (29.54% Gen, 68.61% Train). Generation: 15s, Training: 35s. Estimated remaining time: 39h 15m 39s. Estimated total time: 42h 35m 50s. Time estimates for 10 more iterations: 8m 31s, 100 more iterations: 1h 25m 11s, 500 more iterations: 7h 5m 58s. [2025-11-13 01:29:52,620][__main__][INFO] - Starting iteration 232. [2025-11-13 01:29:53,100][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 23 and human policies 1. [2025-11-13 01:29:53,102][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 01:30:05,985][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 01:30:07,007][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 01:30:07,763][__main__][INFO] - Number of regex retries in iteration 232: 2 [2025-11-13 01:30:07,764][__main__][INFO] - agents played in iteration 232 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 01:30:08,545][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:30:08,571][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:30:08,596][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:30:08,618][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:30:08,618][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 01:30:08,619][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 01:30:09,270][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 01:30:09,726][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 01:30:10,231][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 01:30:10,733][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 01:30:11,234][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 01:30:11,734][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 01:30:12,239][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 01:30:12,739][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 01:30:13,241][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 01:30:13,742][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 01:30:14,243][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 01:30:14,744][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 01:30:15,244][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 01:30:15,743][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 01:30:16,248][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 01:30:16,751][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 01:30:17,257][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 01:30:17,760][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 01:30:18,262][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 01:30:18,763][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 01:30:19,265][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 01:30:19,767][mllm.training.trainer_common][INFO] - 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Processing mini-batch 43 of 64 [2025-11-13 01:30:31,317][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 01:30:31,825][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 01:30:32,328][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 01:30:32,829][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 01:30:33,338][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 01:30:33,840][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 01:30:34,340][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 01:30:34,842][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 01:30:35,343][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 01:30:35,845][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 01:30:36,345][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 01:30:36,847][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 01:30:37,349][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 01:30:37,853][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 01:30:38,358][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 01:30:38,861][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 01:30:39,363][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 01:30:39,867][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 01:30:40,367][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 01:30:40,870][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 01:30:41,376][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10879 tokens. [2025-11-13 01:30:42,037][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.30%, Current % of VRAM taken: 59.75%, Block Peak % of device VRAM: 62.41%, ΔTime: 00:00:32 [2025-11-13 01:30:42,816][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 01:30:42,818][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 01:30:42,819][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 01:30:43,751][__main__][INFO] - Iteration 233 took 50s (28.95% Gen, 69.21% Train). Generation: 14s, Training: 35s. Estimated remaining time: 38h 51m 34s. Estimated total time: 42h 12m 36s. Time estimates for 10 more iterations: 8m 26s, 100 more iterations: 1h 24m 25s, 500 more iterations: 7h 2m 6s. [2025-11-13 01:30:43,753][__main__][INFO] - Starting iteration 233. [2025-11-13 01:30:44,239][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 23 and human policies 1. [2025-11-13 01:30:44,240][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 01:30:59,127][__main__][INFO] - Number of regex retries in iteration 233: 0 [2025-11-13 01:30:59,128][__main__][INFO] - agents played in iteration 233 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 01:30:59,932][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:30:59,954][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:30:59,978][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:31:00,001][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:31:00,001][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 01:31:00,002][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 01:31:00,693][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 01:31:01,151][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 01:31:01,660][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 01:31:02,161][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 01:31:02,664][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 01:31:03,164][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 01:31:03,665][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 01:31:04,165][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 01:31:04,670][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 01:31:05,174][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 01:31:05,676][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 01:31:17,257][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 01:31:17,756][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 01:31:18,256][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 01:31:18,755][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 01:31:19,256][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 01:31:19,756][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 01:31:20,255][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 01:31:20,755][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 01:31:21,256][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 01:31:21,760][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 01:31:22,261][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 01:31:22,759][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 01:31:23,259][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 01:31:23,764][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 01:31:24,266][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 01:31:24,767][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 01:31:25,269][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 01:31:25,776][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 01:31:26,278][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 01:31:26,780][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 01:31:27,285][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 01:31:27,796][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 01:31:28,301][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 01:31:28,810][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 01:31:29,311][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 01:31:29,812][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 01:31:30,328][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 01:31:30,828][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 01:31:31,329][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 01:31:31,831][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 01:31:32,331][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 01:31:32,838][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10875 tokens. [2025-11-13 01:31:33,506][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.21%, Current % of VRAM taken: 59.66%, Block Peak % of device VRAM: 62.35%, ΔTime: 00:00:32 [2025-11-13 01:31:34,292][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 01:31:34,294][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 01:31:34,295][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 01:31:35,476][__main__][INFO] - Iteration 234 took 51s (29.06% Gen, 68.64% Train). Generation: 14s, Training: 35s. Estimated remaining time: 39h 19m 58s. Estimated total time: 42h 41m 52s. Time estimates for 10 more iterations: 8m 32s, 100 more iterations: 1h 25m 23s, 500 more iterations: 7h 6m 58s. [2025-11-13 01:31:35,478][__main__][INFO] - Starting iteration 234. [2025-11-13 01:31:36,108][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 23 and human policies 1. [2025-11-13 01:31:36,108][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 01:31:40,721][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 01:31:51,819][__main__][INFO] - Number of regex retries in iteration 234: 1 [2025-11-13 01:31:51,820][__main__][INFO] - agents played in iteration 234 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 01:31:52,645][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:31:52,668][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:31:52,690][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:31:52,712][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:31:52,713][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 01:31:52,714][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 01:31:53,390][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 01:31:53,850][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 01:31:54,353][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 01:31:54,851][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 01:31:55,357][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 01:31:55,858][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 01:31:56,357][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 01:31:56,859][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 01:31:57,360][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 01:31:57,862][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 01:31:58,362][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 01:31:58,863][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 01:31:59,367][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 01:31:59,869][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 01:32:00,374][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 01:32:00,877][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 01:32:01,379][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 01:32:01,885][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 01:32:02,387][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 01:32:02,890][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 01:32:03,392][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 01:32:03,895][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 01:32:09,965][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 01:32:10,464][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 01:32:10,966][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 01:32:11,469][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 01:32:11,968][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 01:32:12,467][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 01:32:12,968][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 01:32:13,468][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 01:32:13,968][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 01:32:14,467][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 01:32:14,965][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 01:32:15,467][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 01:32:15,964][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 01:32:16,465][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 01:32:16,964][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 01:32:17,462][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 01:32:17,961][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 01:32:18,462][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 01:32:18,964][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 01:32:19,464][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 01:32:19,966][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 01:32:20,468][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 01:32:20,971][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 01:32:21,473][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 01:32:21,978][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 01:32:22,480][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 01:32:22,982][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 01:32:23,488][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 01:32:23,995][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 01:32:24,498][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 01:32:25,003][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 01:32:25,506][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10878 tokens. [2025-11-13 01:32:26,217][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.23%, Current % of VRAM taken: 59.68%, Block Peak % of device VRAM: 62.28%, ΔTime: 00:00:32 [2025-11-13 01:32:26,990][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 01:32:26,993][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 01:32:26,995][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 01:32:27,939][__main__][INFO] - Iteration 235 took 51s (30.31% Gen, 67.86% Train). Generation: 15s, Training: 35s. Estimated remaining time: 39h 48m 50s. Estimated total time: 43h 11m 36s. Time estimates for 10 more iterations: 8m 38s, 100 more iterations: 1h 26m 23s, 500 more iterations: 7h 11m 56s. [2025-11-13 01:32:27,941][__main__][INFO] - Starting iteration 235. [2025-11-13 01:32:28,456][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 23 and human policies 1. [2025-11-13 01:32:28,457][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 01:32:33,909][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 01:32:36,359][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 01:32:37,988][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 01:32:38,493][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 01:32:44,257][__main__][INFO] - Number of regex retries in iteration 235: 4 [2025-11-13 01:32:44,257][__main__][INFO] - agents played in iteration 235 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 01:32:45,075][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:32:45,098][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:32:45,121][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:32:45,143][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:32:45,144][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 01:32:45,144][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 01:32:45,817][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 01:32:46,276][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 01:32:46,785][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 01:32:47,288][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 01:32:47,789][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 01:32:48,291][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 01:32:48,792][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 01:32:49,294][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 01:32:49,794][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 01:32:50,298][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 01:32:50,807][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 01:32:51,308][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 01:32:51,810][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 01:32:52,321][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 01:32:52,822][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 01:32:53,333][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 01:32:53,836][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 01:32:54,336][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 01:32:54,851][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 01:32:55,350][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 01:32:55,849][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 01:32:56,350][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 01:32:56,853][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 01:32:57,359][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 01:32:57,860][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 01:32:58,361][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 01:32:58,865][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 01:32:59,366][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 01:32:59,870][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 01:33:00,370][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 01:33:00,872][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 01:33:01,375][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 01:33:01,878][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 01:33:02,377][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 01:33:02,876][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 01:33:03,378][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 01:33:03,879][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 01:33:04,375][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 01:33:04,872][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 01:33:05,369][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 01:33:05,865][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 01:33:06,369][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 01:33:06,887][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 01:33:07,387][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 01:33:07,888][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 01:33:08,389][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 01:33:08,890][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 01:33:09,393][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 01:33:09,892][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 01:33:10,390][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 01:33:10,889][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 01:33:11,388][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 01:33:11,887][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 01:33:12,385][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 01:33:12,885][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 01:33:13,387][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 01:33:13,886][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 01:33:14,389][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 01:33:14,891][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 01:33:15,395][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 01:33:15,898][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 01:33:16,403][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 01:33:16,905][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 01:33:17,407][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 01:33:17,907][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10877 tokens. [2025-11-13 01:33:18,606][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.21%, Current % of VRAM taken: 59.66%, Block Peak % of device VRAM: 62.39%, ΔTime: 00:00:32 [2025-11-13 01:33:19,350][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 01:33:19,352][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 01:33:19,354][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 01:33:20,248][__main__][INFO] - Iteration 236 took 51s (30.51% Gen, 67.76% Train). Generation: 15s, Training: 35s. Estimated remaining time: 39h 46m 0s. Estimated total time: 43h 9m 38s. Time estimates for 10 more iterations: 8m 37s, 100 more iterations: 1h 26m 19s, 500 more iterations: 7h 11m 36s. [2025-11-13 01:33:20,251][__main__][INFO] - Starting iteration 236. [2025-11-13 01:33:20,737][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 23 and human policies 1. [2025-11-13 01:33:20,737][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 01:33:28,675][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 01:33:29,650][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 0 balls Given Bob's values for hats are significantly higher than mine, and his values for books and balls are also high, I prioritize keeping all the hats for myself to maximize my points. Since I value hats at 1 and Bob values them at 10, keeping all hats ensures I get the maximum possible points from this round without risking a proportional split if my proposal exceeds the item quantity. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 01:33:33,852][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 01:33:36,909][__main__][INFO] - Number of regex retries in iteration 236: 3 [2025-11-13 01:33:36,910][__main__][INFO] - agents played in iteration 236 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 01:33:37,762][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:33:37,797][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:33:37,826][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:33:37,852][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:33:37,853][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 01:33:37,854][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 01:33:38,526][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 01:33:38,986][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 01:33:39,496][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 01:33:39,998][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 01:33:40,504][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 01:33:41,005][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 01:33:41,508][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 01:33:42,012][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 01:33:42,516][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 01:33:43,020][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 01:33:43,522][mllm.training.trainer_common][INFO] - 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Processing mini-batch 43 of 64 [2025-11-13 01:34:00,629][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 01:34:01,127][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 01:34:01,626][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 01:34:02,124][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 01:34:02,621][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 01:34:03,135][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 01:34:03,635][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 01:34:04,135][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 01:34:04,640][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 01:34:05,139][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 01:34:05,659][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 01:34:06,159][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 01:34:06,658][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 01:34:07,167][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 01:34:07,673][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 01:34:08,179][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 01:34:08,685][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 01:34:09,192][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 01:34:09,696][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 01:34:10,203][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 01:34:10,708][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10879 tokens. [2025-11-13 01:34:11,394][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.32%, Current % of VRAM taken: 59.77%, Block Peak % of device VRAM: 62.52%, ΔTime: 00:00:32 [2025-11-13 01:34:12,148][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 01:34:12,150][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 01:34:12,151][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 01:34:13,007][__main__][INFO] - Iteration 237 took 52s (30.94% Gen, 67.42% Train). Generation: 16s, Training: 35s. Estimated remaining time: 40h 9m 2s. Estimated total time: 43h 33m 33s. Time estimates for 10 more iterations: 8m 42s, 100 more iterations: 1h 27m 7s, 500 more iterations: 7h 15m 35s. [2025-11-13 01:34:13,009][__main__][INFO] - Starting iteration 237. [2025-11-13 01:34:13,493][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 23 and human policies 1. [2025-11-13 01:34:13,493][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 01:34:26,459][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 0 balls Given the values: - I value hats at 10 and Bob values them at 1. - I value books at 10 and Bob values them at 10. - I value balls at 1 and Bob values them at 10. Since I have a significantly higher value for hats compared to Bob, I should try to take all the hats. For books, although I value them, Bob values them more, so it's better to let Bob have them. Balls are the least valuable for both of us, and since Bob values them more than I do, I should not take any balls to avoid splitting them in case my proposal exceeds the total quantity. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 01:34:30,829][__main__][INFO] - Number of regex retries in iteration 237: 1 [2025-11-13 01:34:30,829][__main__][INFO] - agents played in iteration 237 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 01:34:31,677][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.62%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:34:31,705][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.62%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:34:31,732][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.62%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:34:31,755][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.62%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:34:31,756][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 01:34:31,758][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 01:34:32,432][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 01:34:32,893][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 01:34:33,405][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 01:34:33,910][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 01:34:34,414][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 01:34:34,919][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 01:34:35,422][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 01:34:35,925][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 01:34:36,429][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 01:34:36,944][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 01:34:37,446][mllm.training.trainer_common][INFO] - 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Processing mini-batch 43 of 64 [2025-11-13 01:34:54,557][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 01:34:55,058][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 01:34:55,560][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 01:34:56,063][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 01:34:56,582][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 01:34:57,078][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 01:34:57,577][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 01:34:58,077][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 01:34:58,577][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 01:34:59,083][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 01:34:59,581][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 01:35:00,088][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 01:35:00,595][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 01:35:01,100][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 01:35:01,604][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 01:35:02,106][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 01:35:02,609][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 01:35:03,119][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 01:35:03,622][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 01:35:04,124][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 01:35:04,630][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10878 tokens. [2025-11-13 01:35:05,328][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.27%, Current % of VRAM taken: 59.72%, Block Peak % of device VRAM: 62.41%, ΔTime: 00:00:32 [2025-11-13 01:35:06,099][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 01:35:06,100][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 01:35:06,102][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 01:35:07,082][__main__][INFO] - Iteration 238 took 53s (32.35% Gen, 65.82% Train). Generation: 17s, Training: 35s. Estimated remaining time: 41h 14m 6s. Estimated total time: 44h 39m 31s. Time estimates for 10 more iterations: 8m 55s, 100 more iterations: 1h 29m 19s, 500 more iterations: 7h 26m 35s. [2025-11-13 01:35:07,084][__main__][INFO] - Starting iteration 238. [2025-11-13 01:35:07,553][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 23 and human policies 1. [2025-11-13 01:35:07,554][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 01:35:19,521][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 01:35:22,624][__main__][INFO] - Number of regex retries in iteration 238: 1 [2025-11-13 01:35:22,625][__main__][INFO] - agents played in iteration 238 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 01:35:23,454][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:35:23,485][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:35:23,512][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:35:23,535][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:35:23,536][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 01:35:23,537][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 01:35:24,178][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 01:35:24,633][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 01:35:25,141][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 01:35:25,646][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 01:35:26,148][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 01:35:26,652][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 01:35:27,155][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 01:35:27,656][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 01:35:28,174][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 01:35:28,678][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 01:35:29,181][mllm.training.trainer_common][INFO] - 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Processing mini-batch 43 of 64 [2025-11-13 01:35:46,259][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 01:35:46,770][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 01:35:47,274][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 01:35:47,789][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 01:35:48,290][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 01:35:48,788][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 01:35:49,287][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 01:35:49,785][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 01:35:50,285][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 01:35:50,785][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 01:35:51,285][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 01:35:51,800][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 01:35:52,297][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 01:35:52,799][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 01:35:53,299][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 01:35:53,804][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 01:35:54,309][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 01:35:54,812][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 01:35:55,316][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 01:35:55,818][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 01:35:56,321][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10872 tokens. [2025-11-13 01:35:57,044][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.23%, Current % of VRAM taken: 59.68%, Block Peak % of device VRAM: 62.28%, ΔTime: 00:00:32 [2025-11-13 01:35:57,789][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 01:35:57,791][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 01:35:57,794][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 01:35:58,690][__main__][INFO] - Iteration 239 took 51s (29.47% Gen, 68.77% Train). Generation: 15s, Training: 35s. Estimated remaining time: 39h 10m 36s. Estimated total time: 42h 36m 53s. Time estimates for 10 more iterations: 8m 31s, 100 more iterations: 1h 25m 13s, 500 more iterations: 7h 6m 8s. [2025-11-13 01:35:58,693][__main__][INFO] - Starting iteration 239. [2025-11-13 01:35:59,168][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 23 and human policies 1. [2025-11-13 01:35:59,169][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 01:36:03,761][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 01:36:14,021][__main__][INFO] - Number of regex retries in iteration 239: 1 [2025-11-13 01:36:14,022][__main__][INFO] - agents played in iteration 239 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 01:36:14,846][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:36:14,868][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:36:14,891][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:36:14,913][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:36:14,913][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 01:36:14,914][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 01:36:15,596][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 01:36:16,052][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 01:36:16,560][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 01:36:17,072][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 01:36:17,572][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 01:36:18,085][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 01:36:18,589][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 01:36:19,091][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 01:36:19,594][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 01:36:20,101][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 01:36:20,608][mllm.training.trainer_common][INFO] - 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Processing mini-batch 43 of 64 [2025-11-13 01:36:37,709][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 01:36:38,212][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 01:36:38,712][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 01:36:39,211][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 01:36:39,717][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 01:36:40,219][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 01:36:40,722][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 01:36:41,227][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 01:36:41,730][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 01:36:42,232][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 01:36:42,733][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 01:36:43,234][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 01:36:43,736][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 01:36:44,238][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 01:36:44,739][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 01:36:45,241][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 01:36:45,745][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 01:36:46,248][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 01:36:46,751][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 01:36:47,253][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 01:36:47,757][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10878 tokens. [2025-11-13 01:36:48,477][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.26%, Current % of VRAM taken: 59.72%, Block Peak % of device VRAM: 62.31%, ΔTime: 00:00:32 [2025-11-13 01:36:49,246][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 01:36:49,248][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 01:36:49,250][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 01:36:50,128][__main__][INFO] - Iteration 240 took 50s (29.15% Gen, 69.13% Train). Generation: 14s, Training: 35s. Estimated remaining time: 39h 0m 52s. Estimated total time: 42h 28m 1s. Time estimates for 10 more iterations: 8m 29s, 100 more iterations: 1h 24m 56s, 500 more iterations: 7h 4m 40s. [2025-11-13 01:36:50,130][__main__][INFO] - Starting iteration 240. [2025-11-13 01:36:50,602][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 23 and human policies 1. [2025-11-13 01:36:50,602][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 01:36:58,926][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 01:37:04,018][__main__][INFO] - Number of regex retries in iteration 240: 1 [2025-11-13 01:37:04,018][__main__][INFO] - agents played in iteration 240 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 01:37:04,819][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:37:04,844][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:37:04,868][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:37:04,890][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:37:04,891][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 01:37:04,892][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 01:37:05,583][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 01:37:06,046][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 01:37:06,558][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 01:37:07,062][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 01:37:07,563][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 01:37:08,065][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 01:37:08,571][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 01:37:09,073][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 01:37:09,575][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 01:37:10,087][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 01:37:10,586][mllm.training.trainer_common][INFO] - 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Processing mini-batch 43 of 64 [2025-11-13 01:37:27,727][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 01:37:28,232][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 01:37:28,734][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 01:37:29,236][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 01:37:29,740][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 01:37:30,242][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 01:37:30,745][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 01:37:31,247][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 01:37:31,749][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 01:37:32,254][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 01:37:32,756][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 01:37:33,258][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 01:37:33,761][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 01:37:34,263][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 01:37:34,765][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 01:37:35,269][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 01:37:35,772][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 01:37:36,284][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 01:37:36,785][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 01:37:37,300][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 01:37:37,800][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10879 tokens. [2025-11-13 01:37:38,543][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.21%, Current % of VRAM taken: 59.66%, Block Peak % of device VRAM: 62.31%, ΔTime: 00:00:32 [2025-11-13 01:37:39,325][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 01:37:39,326][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 01:37:39,328][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 01:37:41,273][__main__][INFO] - Iteration 241 took 50s (26.48% Gen, 69.68% Train). Generation: 13s, Training: 35s. Estimated remaining time: 38h 45m 35s. Estimated total time: 42h 13m 35s. Time estimates for 10 more iterations: 8m 26s, 100 more iterations: 1h 24m 27s, 500 more iterations: 7h 2m 15s. [2025-11-13 01:37:41,275][__main__][INFO] - Starting iteration 241. [2025-11-13 01:37:41,766][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 24 and human policies 1. [2025-11-13 01:37:41,766][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 01:37:45,979][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 01:37:46,678][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 01:37:56,115][__main__][INFO] - Number of regex retries in iteration 241: 2 [2025-11-13 01:37:56,115][__main__][INFO] - agents played in iteration 241 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 01:37:56,986][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:37:57,011][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:37:57,035][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:37:57,057][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:37:57,057][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 01:37:57,058][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 01:37:57,763][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 01:37:58,229][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 01:37:58,745][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 01:37:59,249][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 01:37:59,759][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 01:38:00,265][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 01:38:00,771][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 01:38:01,274][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 01:38:01,778][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 01:38:02,281][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 01:38:02,781][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 01:38:14,372][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 01:38:14,875][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 01:38:15,386][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 01:38:15,893][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 01:38:16,400][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 01:38:16,908][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 01:38:17,407][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 01:38:17,918][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 01:38:18,420][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 01:38:18,921][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 01:38:19,422][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 01:38:19,921][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 01:38:20,424][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 01:38:20,924][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 01:38:21,424][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 01:38:21,936][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 01:38:22,437][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 01:38:22,940][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 01:38:23,443][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 01:38:23,945][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 01:38:24,446][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 01:38:24,948][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 01:38:25,447][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 01:38:25,949][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 01:38:26,449][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 01:38:26,951][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 01:38:27,451][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 01:38:27,951][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 01:38:28,453][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 01:38:28,957][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 01:38:29,465][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 01:38:29,969][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10878 tokens. [2025-11-13 01:38:30,676][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.20%, Current % of VRAM taken: 59.65%, Block Peak % of device VRAM: 62.41%, ΔTime: 00:00:32 [2025-11-13 01:38:31,474][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 01:38:31,476][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 01:38:31,478][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 01:38:32,427][__main__][INFO] - Iteration 242 took 50s (28.32% Gen, 69.80% Train). Generation: 14s, Training: 35s. Estimated remaining time: 38h 44m 13s. Estimated total time: 42h 13m 4s. Time estimates for 10 more iterations: 8m 26s, 100 more iterations: 1h 24m 26s, 500 more iterations: 7h 2m 10s. [2025-11-13 01:38:32,430][__main__][INFO] - Starting iteration 242. [2025-11-13 01:38:32,913][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 24 and human policies 1. [2025-11-13 01:38:32,913][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 01:38:38,653][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 01:38:49,405][__main__][INFO] - Number of regex retries in iteration 242: 1 [2025-11-13 01:38:49,406][__main__][INFO] - agents played in iteration 242 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 01:38:50,253][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:38:50,277][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:38:50,302][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:38:50,324][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:38:50,324][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 01:38:50,325][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 01:38:50,998][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 01:38:51,460][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 01:38:51,971][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 01:38:52,476][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 01:38:52,984][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 01:38:53,498][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 01:38:54,001][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 01:38:54,510][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 01:38:55,011][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 01:38:55,523][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 01:38:56,030][mllm.training.trainer_common][INFO] - 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Processing mini-batch 54 of 64 [2025-11-13 01:39:18,766][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 01:39:19,268][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 01:39:19,769][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 01:39:20,272][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 01:39:20,777][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 01:39:21,282][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 01:39:21,784][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 01:39:22,287][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 01:39:22,787][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 01:39:23,289][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10874 tokens. [2025-11-13 01:39:24,013][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.20%, Current % of VRAM taken: 59.65%, Block Peak % of device VRAM: 62.41%, ΔTime: 00:00:33 [2025-11-13 01:39:24,813][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 01:39:24,814][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 01:39:24,816][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 01:39:25,802][__main__][INFO] - Iteration 243 took 52s (31.18% Gen, 66.95% Train). Generation: 16s, Training: 35s. Estimated remaining time: 40h 34m 45s. Estimated total time: 44h 4m 29s. Time estimates for 10 more iterations: 8m 48s, 100 more iterations: 1h 28m 8s, 500 more iterations: 7h 20m 44s. [2025-11-13 01:39:25,804][__main__][INFO] - Starting iteration 243. [2025-11-13 01:39:26,279][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 24 and human policies 1. [2025-11-13 01:39:26,280][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 01:39:39,754][__main__][INFO] - Number of regex retries in iteration 243: 0 [2025-11-13 01:39:39,755][__main__][INFO] - agents played in iteration 243 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 01:39:40,586][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:39:40,615][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:39:40,644][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:39:40,668][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:39:40,669][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 01:39:40,671][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 01:39:41,321][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 01:39:41,779][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 01:39:42,287][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 01:39:42,789][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 01:39:43,294][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 01:39:43,797][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 01:39:44,300][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 01:39:44,807][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 01:39:45,314][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 01:39:45,817][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 01:39:46,317][mllm.training.trainer_common][INFO] - 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Processing mini-batch 43 of 64 [2025-11-13 01:40:03,421][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 01:40:03,925][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 01:40:04,437][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 01:40:04,938][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 01:40:05,443][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 01:40:05,948][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 01:40:06,452][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 01:40:06,962][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 01:40:07,462][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 01:40:07,962][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 01:40:08,474][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 01:40:08,974][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 01:40:09,474][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 01:40:09,977][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 01:40:10,479][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 01:40:10,985][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 01:40:11,487][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 01:40:11,986][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 01:40:12,488][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 01:40:12,986][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 01:40:13,488][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10877 tokens. [2025-11-13 01:40:14,169][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.23%, Current % of VRAM taken: 59.68%, Block Peak % of device VRAM: 62.42%, ΔTime: 00:00:32 [2025-11-13 01:40:14,966][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 01:40:14,968][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 01:40:14,970][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 01:40:16,070][__main__][INFO] - Iteration 244 took 49s (27.06% Gen, 70.73% Train). Generation: 13s, Training: 35s. Estimated remaining time: 37h 58m 59s. Estimated total time: 41h 29m 33s. Time estimates for 10 more iterations: 8m 17s, 100 more iterations: 1h 22m 59s, 500 more iterations: 6h 54m 55s. [2025-11-13 01:40:16,072][__main__][INFO] - Starting iteration 244. [2025-11-13 01:40:16,537][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 24 and human policies 1. [2025-11-13 01:40:16,538][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 01:40:29,652][__main__][INFO] - Number of regex retries in iteration 244: 0 [2025-11-13 01:40:29,653][__main__][INFO] - agents played in iteration 244 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 01:40:30,433][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:40:30,460][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:40:30,486][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:40:30,509][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:40:30,510][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 01:40:30,511][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 01:40:31,154][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 01:40:31,611][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 01:40:32,117][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 01:40:32,621][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 01:40:33,126][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 01:40:33,627][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 01:40:34,129][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 01:40:34,642][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 01:40:35,141][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 01:40:35,642][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 01:40:36,154][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 01:40:47,762][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 01:40:48,263][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 01:40:48,764][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 01:40:49,268][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 01:40:49,769][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 01:40:50,274][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 01:40:50,778][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 01:40:51,284][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 01:40:51,790][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 01:40:52,294][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 01:40:52,796][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 01:40:53,301][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 01:40:53,805][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 01:40:54,307][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 01:40:54,809][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 01:40:55,312][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 01:40:55,859][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 01:40:56,369][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 01:40:56,873][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 01:40:57,377][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 01:40:57,882][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 01:40:58,391][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 01:40:58,894][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 01:40:59,397][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 01:40:59,899][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 01:41:00,402][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 01:41:00,906][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 01:41:01,415][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 01:41:01,916][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 01:41:02,417][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 01:41:02,920][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 01:41:03,420][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10878 tokens. [2025-11-13 01:41:04,106][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.21%, Current % of VRAM taken: 59.66%, Block Peak % of device VRAM: 62.34%, ΔTime: 00:00:32 [2025-11-13 01:41:04,904][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 01:41:04,905][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 01:41:04,907][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 01:41:05,820][__main__][INFO] - Iteration 245 took 49s (26.61% Gen, 71.53% Train). Generation: 13s, Training: 35s. Estimated remaining time: 37h 32m 45s. Estimated total time: 41h 4m 9s. Time estimates for 10 more iterations: 8m 12s, 100 more iterations: 1h 22m 8s, 500 more iterations: 6h 50m 41s. [2025-11-13 01:41:05,822][__main__][INFO] - Starting iteration 245. [2025-11-13 01:41:06,292][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 24 and human policies 1. [2025-11-13 01:41:06,293][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 01:41:20,316][__main__][INFO] - Number of regex retries in iteration 245: 0 [2025-11-13 01:41:20,317][__main__][INFO] - agents played in iteration 245 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 01:41:21,153][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:41:21,182][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:41:21,208][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:41:21,231][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:41:21,231][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 01:41:21,232][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 01:41:21,883][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 01:41:22,338][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 01:41:22,843][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 01:41:23,369][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 01:41:23,868][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 01:41:24,368][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 01:41:24,869][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 01:41:25,368][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 01:41:25,874][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 01:41:26,378][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 01:41:26,882][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 01:41:38,474][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 01:41:38,980][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 01:41:39,481][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 01:41:39,983][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 01:41:40,484][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 01:41:40,984][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 01:41:41,487][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 01:41:41,985][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 01:41:42,485][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 01:41:42,991][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 01:41:43,492][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 01:41:43,994][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 01:41:44,494][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 01:41:44,997][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 01:41:45,500][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 01:41:46,007][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 01:41:46,510][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 01:41:47,019][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 01:41:47,520][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 01:41:48,023][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 01:41:48,524][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 01:41:49,025][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 01:41:49,528][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 01:41:50,027][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 01:41:50,530][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 01:41:51,032][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 01:41:51,534][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 01:41:52,035][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 01:41:52,533][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 01:41:53,033][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 01:41:53,536][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 01:41:54,040][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10879 tokens. [2025-11-13 01:41:54,729][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.20%, Current % of VRAM taken: 59.66%, Block Peak % of device VRAM: 62.34%, ΔTime: 00:00:32 [2025-11-13 01:41:55,496][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 01:41:55,497][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 01:41:55,499][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 01:41:56,506][__main__][INFO] - Iteration 246 took 50s (27.93% Gen, 70.06% Train). Generation: 14s, Training: 35s. Estimated remaining time: 38h 18m 29s. Estimated total time: 41h 50m 44s. Time estimates for 10 more iterations: 8m 22s, 100 more iterations: 1h 23m 41s, 500 more iterations: 6h 58m 27s. [2025-11-13 01:41:56,508][__main__][INFO] - Starting iteration 246. [2025-11-13 01:41:56,992][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 24 and human policies 1. [2025-11-13 01:41:56,993][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 01:42:10,055][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 01:42:10,917][__main__][INFO] - Number of regex retries in iteration 246: 1 [2025-11-13 01:42:10,918][__main__][INFO] - agents played in iteration 246 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 01:42:11,751][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:42:11,773][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:42:11,798][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:42:11,821][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:42:11,821][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 01:42:11,822][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 01:42:12,466][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 01:42:12,922][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 01:42:13,432][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 01:42:13,935][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 01:42:14,436][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 01:42:14,943][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 01:42:15,443][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 01:42:15,943][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 01:42:16,443][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 01:42:16,946][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 01:42:17,461][mllm.training.trainer_common][INFO] - 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Processing mini-batch 43 of 64 [2025-11-13 01:42:34,547][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 01:42:35,045][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 01:42:35,544][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 01:42:36,043][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 01:42:36,542][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 01:42:37,045][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 01:42:37,545][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 01:42:38,045][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 01:42:38,552][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 01:42:39,053][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 01:42:39,555][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 01:42:40,057][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 01:42:40,560][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 01:42:41,067][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 01:42:41,570][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 01:42:42,071][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 01:42:42,583][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 01:42:43,085][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 01:42:43,599][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 01:42:44,100][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 01:42:44,599][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10878 tokens. [2025-11-13 01:42:45,323][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.13%, Current % of VRAM taken: 59.58%, Block Peak % of device VRAM: 62.31%, ΔTime: 00:00:32 [2025-11-13 01:42:46,100][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 01:42:46,102][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 01:42:46,103][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 01:42:47,146][__main__][INFO] - Iteration 247 took 50s (27.76% Gen, 70.15% Train). Generation: 13s, Training: 35s. Estimated remaining time: 38h 14m 35s. Estimated total time: 41h 47m 41s. Time estimates for 10 more iterations: 8m 21s, 100 more iterations: 1h 23m 35s, 500 more iterations: 6h 57m 56s. [2025-11-13 01:42:47,148][__main__][INFO] - Starting iteration 247. [2025-11-13 01:42:47,620][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 24 and human policies 1. [2025-11-13 01:42:47,620][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 01:43:02,847][__main__][INFO] - Number of regex retries in iteration 247: 0 [2025-11-13 01:43:02,848][__main__][INFO] - agents played in iteration 247 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 01:43:03,684][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.53%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:43:03,708][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.53%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:43:03,732][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.53%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:43:03,754][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.53%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:43:03,755][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 01:43:03,755][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 01:43:04,414][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 01:43:04,877][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 01:43:05,384][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 01:43:05,886][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 01:43:06,395][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 01:43:06,897][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 01:43:07,417][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 01:43:07,918][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 01:43:08,419][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 01:43:08,930][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 01:43:09,433][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 01:43:21,030][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 01:43:21,532][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 01:43:22,036][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 01:43:22,538][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 01:43:23,039][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 01:43:23,540][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 01:43:24,041][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 01:43:24,545][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 01:43:25,047][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 01:43:25,550][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 01:43:26,054][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 01:43:26,555][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 01:43:27,057][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 01:43:27,559][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 01:43:28,061][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 01:43:28,564][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 01:43:29,064][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 01:43:29,565][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 01:43:30,067][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 01:43:30,567][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 01:43:31,070][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 01:43:31,571][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 01:43:32,071][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 01:43:32,574][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 01:43:33,074][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 01:43:33,577][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 01:43:34,094][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 01:43:34,598][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 01:43:35,119][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 01:43:35,621][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 01:43:36,127][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 01:43:36,629][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10879 tokens. [2025-11-13 01:43:37,363][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.20%, Current % of VRAM taken: 59.65%, Block Peak % of device VRAM: 62.35%, ΔTime: 00:00:32 [2025-11-13 01:43:38,152][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 01:43:38,154][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 01:43:38,156][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 01:43:39,048][__main__][INFO] - Iteration 248 took 51s (29.61% Gen, 68.65% Train). Generation: 15s, Training: 35s. Estimated remaining time: 39h 17m 28s. Estimated total time: 42h 51m 26s. Time estimates for 10 more iterations: 8m 34s, 100 more iterations: 1h 25m 42s, 500 more iterations: 7h 8m 34s. [2025-11-13 01:43:39,050][__main__][INFO] - Starting iteration 248. [2025-11-13 01:43:39,524][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 24 and human policies 1. [2025-11-13 01:43:39,525][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 01:43:43,865][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 01:43:54,193][__main__][INFO] - Number of regex retries in iteration 248: 1 [2025-11-13 01:43:54,194][__main__][INFO] - agents played in iteration 248 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 01:43:54,998][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:43:55,020][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:43:55,043][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:43:55,066][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:43:55,067][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 01:43:55,067][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 01:43:55,821][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 01:43:56,283][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 01:43:56,791][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 01:43:57,291][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 01:43:57,792][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 01:43:58,293][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 01:43:58,802][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 01:43:59,303][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 01:43:59,813][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 01:44:00,314][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 01:44:00,814][mllm.training.trainer_common][INFO] - 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Processing mini-batch 54 of 64 [2025-11-13 01:44:23,376][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 01:44:23,880][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 01:44:24,379][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 01:44:24,879][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 01:44:25,378][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 01:44:25,878][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 01:44:26,380][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 01:44:26,879][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 01:44:27,380][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 01:44:27,880][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10877 tokens. [2025-11-13 01:44:28,545][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.17%, Current % of VRAM taken: 59.62%, Block Peak % of device VRAM: 62.24%, ΔTime: 00:00:32 [2025-11-13 01:44:29,315][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 01:44:29,317][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 01:44:29,319][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 01:44:30,386][__main__][INFO] - Iteration 249 took 50s (28.84% Gen, 69.06% Train). Generation: 14s, Training: 35s. Estimated remaining time: 38h 48m 17s. Estimated total time: 42h 23m 5s. Time estimates for 10 more iterations: 8m 28s, 100 more iterations: 1h 24m 46s, 500 more iterations: 7h 3m 50s. [2025-11-13 01:44:30,388][__main__][INFO] - Starting iteration 249. [2025-11-13 01:44:30,860][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 24 and human policies 1. [2025-11-13 01:44:30,861][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 01:44:46,017][__main__][INFO] - Number of regex retries in iteration 249: 0 [2025-11-13 01:44:46,017][__main__][INFO] - agents played in iteration 249 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 01:44:46,810][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:44:46,833][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:44:46,856][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:44:46,878][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:44:46,879][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 01:44:46,879][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 01:44:47,569][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 01:44:48,029][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 01:44:48,542][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 01:44:49,046][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 01:44:49,551][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 01:44:50,055][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 01:44:50,558][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 01:44:51,061][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 01:44:51,563][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 01:44:52,067][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 01:44:52,570][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 01:45:04,149][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 01:45:04,652][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 01:45:05,157][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 01:45:05,661][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 01:45:06,164][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 01:45:06,667][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 01:45:07,173][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 01:45:07,680][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 01:45:08,184][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 01:45:08,689][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 01:45:09,190][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 01:45:09,698][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 01:45:10,200][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 01:45:10,702][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 01:45:11,212][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 01:45:11,713][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 01:45:12,229][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 01:45:12,729][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 01:45:13,230][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 01:45:13,741][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 01:45:14,244][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 01:45:14,747][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 01:45:15,246][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 01:45:15,745][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 01:45:16,249][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 01:45:16,750][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 01:45:17,252][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 01:45:17,753][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 01:45:18,254][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 01:45:18,754][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 01:45:19,256][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 01:45:19,759][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10874 tokens. [2025-11-13 01:45:20,456][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.26%, Current % of VRAM taken: 59.71%, Block Peak % of device VRAM: 62.42%, ΔTime: 00:00:32 [2025-11-13 01:45:21,261][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 01:45:21,263][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 01:45:21,265][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 01:45:22,241][__main__][INFO] - Iteration 250 took 51s (29.50% Gen, 68.60% Train). Generation: 15s, Training: 35s. Estimated remaining time: 39h 13m 24s. Estimated total time: 42h 49m 4s. Time estimates for 10 more iterations: 8m 33s, 100 more iterations: 1h 25m 38s, 500 more iterations: 7h 8m 10s. [2025-11-13 01:45:22,244][__main__][INFO] - Starting iteration 250. [2025-11-13 01:45:22,741][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 24 and human policies 1. [2025-11-13 01:45:22,741][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 01:45:36,649][__main__][INFO] - Number of regex retries in iteration 250: 0 [2025-11-13 01:45:36,650][__main__][INFO] - agents played in iteration 250 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 01:45:37,528][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:45:37,550][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:45:37,573][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:45:37,595][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:45:37,595][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 01:45:37,596][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 01:45:38,284][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 01:45:38,742][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 01:45:39,249][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 01:45:39,759][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 01:45:40,263][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 01:45:40,765][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 01:45:41,270][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 01:45:41,771][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 01:45:42,276][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 01:45:42,781][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 01:45:43,286][mllm.training.trainer_common][INFO] - 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Processing mini-batch 43 of 64 [2025-11-13 01:46:00,362][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 01:46:00,864][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 01:46:01,363][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 01:46:01,867][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 01:46:02,367][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 01:46:02,870][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 01:46:03,371][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 01:46:03,875][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 01:46:04,379][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 01:46:04,884][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 01:46:05,386][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 01:46:05,888][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 01:46:06,390][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 01:46:06,887][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 01:46:07,386][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 01:46:07,885][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 01:46:08,383][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 01:46:08,882][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 01:46:09,379][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 01:46:09,876][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 01:46:10,374][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10879 tokens. [2025-11-13 01:46:11,036][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.19%, Current % of VRAM taken: 59.65%, Block Peak % of device VRAM: 62.32%, ΔTime: 00:00:32 [2025-11-13 01:46:11,832][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 01:46:11,834][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 01:46:11,836][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 01:46:13,588][__main__][INFO] - Iteration 251 took 50s (27.35% Gen, 69.20% Train). Generation: 13s, Training: 35s. Estimated remaining time: 38h 45m 50s. Estimated total time: 42h 22m 22s. Time estimates for 10 more iterations: 8m 28s, 100 more iterations: 1h 24m 44s, 500 more iterations: 7h 3m 43s. [2025-11-13 01:46:13,590][__main__][INFO] - Starting iteration 251. [2025-11-13 01:46:14,071][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 25 and human policies 1. [2025-11-13 01:46:14,072][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 01:46:28,582][__main__][INFO] - Number of regex retries in iteration 251: 0 [2025-11-13 01:46:28,583][__main__][INFO] - agents played in iteration 251 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 01:46:29,372][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:46:29,395][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:46:29,418][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:46:29,440][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:46:29,441][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 01:46:29,441][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 01:46:30,119][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 01:46:30,577][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 01:46:31,087][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 01:46:31,592][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 01:46:32,092][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 01:46:32,595][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 01:46:33,096][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 01:46:33,597][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 01:46:34,099][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 01:46:34,601][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 01:46:35,102][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 01:46:46,670][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 01:46:47,169][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 01:46:47,668][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 01:46:48,170][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 01:46:48,671][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 01:46:49,170][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 01:46:49,674][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 01:46:50,175][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 01:46:50,681][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 01:46:51,181][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 01:46:51,681][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 01:46:52,183][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 01:46:52,682][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 01:46:53,183][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 01:46:53,690][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 01:46:54,192][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 01:46:54,704][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 01:46:55,209][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 01:46:55,711][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 01:46:56,214][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 01:46:56,717][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 01:46:57,221][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 01:46:57,725][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 01:46:58,229][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 01:46:58,747][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 01:46:59,247][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 01:46:59,752][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 01:47:00,256][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 01:47:00,760][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 01:47:01,264][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 01:47:01,767][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 01:47:02,267][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10876 tokens. [2025-11-13 01:47:02,995][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.20%, Current % of VRAM taken: 59.65%, Block Peak % of device VRAM: 62.34%, ΔTime: 00:00:32 [2025-11-13 01:47:03,768][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 01:47:03,770][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 01:47:03,771][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 01:47:04,769][__main__][INFO] - Iteration 252 took 50s (28.62% Gen, 69.41% Train). Generation: 14s, Training: 35s. Estimated remaining time: 38h 37m 31s. Estimated total time: 42h 14m 54s. Time estimates for 10 more iterations: 8m 26s, 100 more iterations: 1h 24m 29s, 500 more iterations: 7h 2m 29s. [2025-11-13 01:47:04,771][__main__][INFO] - Starting iteration 252. [2025-11-13 01:47:05,280][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 25 and human policies 1. [2025-11-13 01:47:05,281][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 01:47:20,608][__main__][INFO] - Number of regex retries in iteration 252: 0 [2025-11-13 01:47:20,609][__main__][INFO] - agents played in iteration 252 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 01:47:21,436][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:47:21,464][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:47:21,491][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:47:21,514][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:47:21,515][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 01:47:21,516][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 01:47:22,177][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 01:47:22,637][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 01:47:23,147][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 01:47:23,649][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 01:47:24,151][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 01:47:24,654][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 01:47:25,157][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 01:47:25,657][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 01:47:26,159][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 01:47:26,661][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 01:47:27,162][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 01:47:27,669][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 01:47:28,169][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 01:47:28,669][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 01:47:29,173][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 01:47:29,674][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 01:47:30,176][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 01:47:30,680][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 01:47:31,185][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 01:47:31,690][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 01:47:32,194][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 01:47:32,698][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 01:47:33,203][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 01:47:33,705][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 01:47:34,209][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 01:47:34,708][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 01:47:35,209][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 01:47:35,710][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 01:47:36,209][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 01:47:36,708][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 01:47:37,209][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 01:47:37,707][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 01:47:38,207][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 01:47:38,707][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 01:47:39,206][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 01:47:39,707][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 01:47:40,206][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 01:47:40,708][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 01:47:41,211][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 01:47:41,712][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 01:47:42,214][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 01:47:42,714][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 01:47:43,213][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 01:47:43,714][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 01:47:44,213][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 01:47:44,713][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 01:47:45,216][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 01:47:45,716][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 01:47:46,217][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 01:47:46,719][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 01:47:47,218][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 01:47:47,719][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 01:47:48,222][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 01:47:48,727][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 01:47:49,229][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 01:47:49,731][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 01:47:50,245][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 01:47:50,747][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 01:47:51,254][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 01:47:51,748][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 01:47:52,248][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 01:47:52,758][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 01:47:53,258][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 01:47:53,756][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 01:47:54,262][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10879 tokens. [2025-11-13 01:47:55,020][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.17%, Current % of VRAM taken: 59.62%, Block Peak % of device VRAM: 62.49%, ΔTime: 00:00:32 [2025-11-13 01:47:55,806][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 01:47:55,808][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 01:47:55,810][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 01:47:56,692][__main__][INFO] - Iteration 253 took 51s (29.81% Gen, 68.47% Train). Generation: 15s, Training: 35s. Estimated remaining time: 39h 12m 20s. Estimated total time: 42h 50m 35s. Time estimates for 10 more iterations: 8m 34s, 100 more iterations: 1h 25m 41s, 500 more iterations: 7h 8m 25s. [2025-11-13 01:47:56,693][__main__][INFO] - Starting iteration 253. [2025-11-13 01:47:57,162][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 25 and human policies 1. [2025-11-13 01:47:57,162][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 01:48:12,433][__main__][INFO] - Number of regex retries in iteration 253: 0 [2025-11-13 01:48:12,433][__main__][INFO] - agents played in iteration 253 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 01:48:13,256][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:48:13,279][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:48:13,301][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:48:13,324][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:48:13,326][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 01:48:13,326][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 01:48:14,003][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 01:48:14,460][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 01:48:14,966][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 01:48:15,471][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 01:48:15,972][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 01:48:16,474][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 01:48:16,975][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 01:48:17,478][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 01:48:17,989][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 01:48:18,491][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 01:48:18,993][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 01:48:19,494][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 01:48:19,994][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 01:48:20,498][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 01:48:20,997][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 01:48:21,502][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 01:48:22,004][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 01:48:22,506][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 01:48:23,007][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 01:48:23,511][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 01:48:24,014][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 01:48:24,517][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 01:48:25,016][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 01:48:25,517][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 01:48:26,019][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 01:48:26,525][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 01:48:27,031][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 01:48:27,541][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 01:48:28,046][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 01:48:28,564][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 01:48:29,066][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 01:48:29,570][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 01:48:30,073][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 01:48:30,573][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 01:48:31,076][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 01:48:31,577][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 01:48:32,079][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 01:48:32,587][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 01:48:33,088][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 01:48:33,589][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 01:48:34,089][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 01:48:34,598][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 01:48:35,106][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 01:48:35,609][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 01:48:36,115][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 01:48:36,620][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 01:48:37,123][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 01:48:37,627][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 01:48:38,132][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 01:48:38,633][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 01:48:39,143][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 01:48:39,639][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 01:48:40,142][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 01:48:40,641][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 01:48:41,140][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 01:48:41,641][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 01:48:42,151][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 01:48:42,652][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 01:48:43,175][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 01:48:43,677][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 01:48:44,182][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 01:48:44,686][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 01:48:45,190][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 01:48:45,699][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 01:48:46,201][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10876 tokens. [2025-11-13 01:48:46,977][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.22%, Current % of VRAM taken: 59.67%, Block Peak % of device VRAM: 62.58%, ΔTime: 00:00:32 [2025-11-13 01:48:47,732][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 01:48:47,734][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 01:48:47,736][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 01:48:48,720][__main__][INFO] - Iteration 254 took 51s (29.62% Gen, 68.47% Train). Generation: 15s, Training: 35s. Estimated remaining time: 39h 18m 50s. Estimated total time: 42h 57m 57s. Time estimates for 10 more iterations: 8m 35s, 100 more iterations: 1h 25m 55s, 500 more iterations: 7h 9m 39s. [2025-11-13 01:48:48,722][__main__][INFO] - Starting iteration 254. [2025-11-13 01:48:49,190][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 25 and human policies 1. [2025-11-13 01:48:49,190][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 01:49:02,438][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 01:49:04,500][__main__][INFO] - Number of regex retries in iteration 254: 1 [2025-11-13 01:49:04,501][__main__][INFO] - agents played in iteration 254 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 01:49:05,305][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:49:05,332][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:49:05,355][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:49:05,377][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:49:05,378][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 01:49:05,379][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 01:49:06,083][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 01:49:06,543][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 01:49:07,053][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 01:49:07,560][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 01:49:08,063][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 01:49:08,565][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 01:49:09,066][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 01:49:09,584][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 01:49:10,089][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 01:49:10,595][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 01:49:11,099][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 01:49:11,601][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 01:49:12,104][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 01:49:12,606][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 01:49:13,108][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 01:49:13,612][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 01:49:14,114][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 01:49:14,615][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 01:49:15,117][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 01:49:15,617][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 01:49:16,120][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 01:49:16,620][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 01:49:17,121][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 01:49:17,622][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 01:49:18,124][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 01:49:18,628][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 01:49:19,128][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 01:49:19,628][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 01:49:20,131][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 01:49:20,633][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 01:49:21,136][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 01:49:21,640][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 01:49:22,143][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 01:49:22,652][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 01:49:23,158][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 01:49:23,662][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 01:49:24,179][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 01:49:24,681][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 01:49:25,183][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 01:49:25,685][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 01:49:26,184][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 01:49:26,692][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 01:49:27,193][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 01:49:27,696][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 01:49:28,194][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 01:49:28,696][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 01:49:29,198][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 01:49:29,697][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 01:49:30,196][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 01:49:30,700][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 01:49:31,198][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 01:49:31,698][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 01:49:32,199][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 01:49:32,698][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 01:49:33,200][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 01:49:33,699][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 01:49:34,200][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 01:49:34,700][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 01:49:35,200][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 01:49:35,700][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 01:49:36,200][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 01:49:36,703][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 01:49:37,208][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 01:49:37,711][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 01:49:38,215][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10876 tokens. [2025-11-13 01:49:38,975][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.23%, Current % of VRAM taken: 59.68%, Block Peak % of device VRAM: 62.31%, ΔTime: 00:00:32 [2025-11-13 01:49:39,746][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 01:49:39,748][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 01:49:39,750][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 01:49:40,681][__main__][INFO] - Iteration 255 took 51s (29.73% Gen, 68.46% Train). Generation: 15s, Training: 35s. Estimated remaining time: 39h 14m 35s. Estimated total time: 42h 54m 34s. Time estimates for 10 more iterations: 8m 34s, 100 more iterations: 1h 25m 49s, 500 more iterations: 7h 9m 5s. [2025-11-13 01:49:40,683][__main__][INFO] - Starting iteration 255. [2025-11-13 01:49:41,173][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 25 and human policies 1. [2025-11-13 01:49:41,173][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 01:49:45,948][mllm.models.large_language_model_local][WARNING] - Response Proposal: 5 hats, 5 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 01:49:50,277][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 01:49:56,265][__main__][INFO] - Number of regex retries in iteration 255: 2 [2025-11-13 01:49:56,266][__main__][INFO] - agents played in iteration 255 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 01:49:57,076][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:49:57,104][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:49:57,130][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:49:57,153][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:49:57,154][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 01:49:57,154][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 01:49:57,921][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 01:49:58,398][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 01:49:58,907][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 01:49:59,411][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 01:49:59,914][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 01:50:00,418][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 01:50:00,923][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 01:50:01,427][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 01:50:01,929][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 01:50:02,433][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 01:50:02,935][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 01:50:03,438][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 01:50:03,942][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 01:50:04,443][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 01:50:04,945][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 01:50:05,445][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 01:50:05,943][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 01:50:06,448][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 01:50:06,949][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 01:50:07,453][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 01:50:07,952][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 01:50:08,450][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 01:50:08,951][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 01:50:09,451][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 01:50:09,952][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 01:50:10,452][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 01:50:10,954][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 01:50:11,463][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 01:50:11,964][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 01:50:12,475][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 01:50:12,978][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 01:50:13,481][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 01:50:13,982][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 01:50:14,482][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 01:50:14,986][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 01:50:15,489][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 01:50:15,998][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 01:50:16,518][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 01:50:17,021][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 01:50:17,524][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 01:50:18,024][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 01:50:18,527][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 01:50:19,031][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 01:50:19,530][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 01:50:20,029][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 01:50:20,530][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 01:50:21,033][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 01:50:21,531][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 01:50:22,030][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 01:50:22,529][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 01:50:23,035][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 01:50:23,534][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 01:50:24,033][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 01:50:24,531][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 01:50:25,031][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 01:50:25,533][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 01:50:26,033][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 01:50:26,531][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 01:50:27,033][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 01:50:27,531][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 01:50:28,030][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 01:50:28,528][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 01:50:29,028][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 01:50:29,528][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 01:50:30,027][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10879 tokens. [2025-11-13 01:50:30,793][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.20%, Current % of VRAM taken: 59.65%, Block Peak % of device VRAM: 62.31%, ΔTime: 00:00:32 [2025-11-13 01:50:31,610][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 01:50:31,612][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 01:50:31,613][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 01:50:32,548][__main__][INFO] - Iteration 256 took 51s (29.38% Gen, 68.80% Train). Generation: 15s, Training: 35s. Estimated remaining time: 39h 7m 57s. Estimated total time: 42h 48m 48s. Time estimates for 10 more iterations: 8m 33s, 100 more iterations: 1h 25m 37s, 500 more iterations: 7h 8m 8s. [2025-11-13 01:50:32,551][__main__][INFO] - Starting iteration 256. [2025-11-13 01:50:33,062][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 25 and human policies 1. [2025-11-13 01:50:33,063][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 01:50:37,405][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 01:50:38,527][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 01:50:47,272][__main__][INFO] - Number of regex retries in iteration 256: 2 [2025-11-13 01:50:47,272][__main__][INFO] - agents played in iteration 256 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 01:50:48,074][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:50:48,101][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:50:48,128][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:50:48,151][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:50:48,152][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 01:50:48,153][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 01:50:48,836][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 01:50:49,297][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 01:50:49,810][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 01:50:50,314][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 01:50:50,822][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 01:50:51,341][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 01:50:51,849][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 01:50:52,357][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 01:50:52,865][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 01:50:53,373][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 01:50:53,878][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 01:50:54,384][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 01:50:54,905][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 01:50:55,409][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 01:50:55,913][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 01:50:56,416][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 01:50:56,917][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 01:50:57,425][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 01:50:57,925][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 01:50:58,435][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 01:50:58,937][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 01:50:59,439][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 01:50:59,944][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 01:51:00,447][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 01:51:00,948][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 01:51:01,451][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 01:51:01,953][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 01:51:02,455][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 01:51:02,957][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 01:51:03,459][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 01:51:03,964][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 01:51:04,466][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 01:51:04,976][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 01:51:05,479][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 01:51:05,983][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 01:51:06,485][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 01:51:06,995][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 01:51:07,498][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 01:51:08,022][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 01:51:08,526][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 01:51:09,029][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 01:51:09,531][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 01:51:10,030][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 01:51:10,534][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 01:51:11,034][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 01:51:11,533][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 01:51:12,037][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 01:51:12,536][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 01:51:13,037][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 01:51:13,544][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 01:51:14,045][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 01:51:14,549][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 01:51:15,048][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 01:51:15,547][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 01:51:16,048][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 01:51:16,549][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 01:51:17,050][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 01:51:17,549][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 01:51:18,048][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 01:51:18,551][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 01:51:19,051][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 01:51:19,551][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 01:51:20,053][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 01:51:20,552][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 01:51:21,051][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10879 tokens. [2025-11-13 01:51:21,723][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.25%, Current % of VRAM taken: 59.70%, Block Peak % of device VRAM: 62.34%, ΔTime: 00:00:32 [2025-11-13 01:51:22,506][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 01:51:22,508][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 01:51:22,510][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 01:51:23,478][__main__][INFO] - Iteration 257 took 50s (28.18% Gen, 69.89% Train). Generation: 14s, Training: 35s. Estimated remaining time: 38h 19m 9s. Estimated total time: 42h 0m 50s. Time estimates for 10 more iterations: 8m 24s, 100 more iterations: 1h 24m 1s, 500 more iterations: 7h 0m 8s. [2025-11-13 01:51:23,480][__main__][INFO] - Starting iteration 257. [2025-11-13 01:51:23,972][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 25 and human policies 1. [2025-11-13 01:51:23,973][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 01:51:29,466][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 01:51:29,468][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 01:51:40,707][__main__][INFO] - Number of regex retries in iteration 257: 2 [2025-11-13 01:51:40,708][__main__][INFO] - agents played in iteration 257 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 01:51:41,554][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:51:41,579][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:51:41,604][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:51:41,627][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:51:41,627][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 01:51:41,628][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 01:51:42,366][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 01:51:42,827][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 01:51:43,334][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 01:51:43,841][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 01:51:44,345][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 01:51:44,853][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 01:51:45,356][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 01:51:45,860][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 01:51:46,366][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 01:51:46,868][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 01:51:47,373][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 01:51:47,876][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 01:51:48,380][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 01:51:48,886][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 01:51:49,389][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 01:51:49,890][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 01:51:50,402][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 01:51:50,904][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 01:51:51,406][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 01:51:51,905][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 01:51:52,410][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 01:51:52,910][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 01:51:53,411][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 01:51:53,913][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 01:51:54,418][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 01:51:54,918][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 01:51:55,417][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 01:51:55,918][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 01:51:56,418][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 01:51:56,917][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 01:51:57,418][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 01:51:57,915][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 01:51:58,416][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 01:51:58,924][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 01:51:59,440][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 01:51:59,943][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 01:52:00,446][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 01:52:00,953][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 01:52:01,456][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 01:52:01,963][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 01:52:02,468][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 01:52:02,967][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 01:52:03,466][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 01:52:03,966][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 01:52:04,466][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 01:52:04,964][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 01:52:05,464][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 01:52:05,965][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 01:52:06,466][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 01:52:06,965][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 01:52:07,467][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 01:52:07,967][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 01:52:08,466][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 01:52:08,966][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 01:52:09,467][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 01:52:09,967][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 01:52:10,467][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 01:52:10,967][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 01:52:11,467][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 01:52:11,967][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 01:52:12,468][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 01:52:12,969][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 01:52:13,469][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 01:52:13,972][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 01:52:14,471][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10872 tokens. [2025-11-13 01:52:15,165][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.23%, Current % of VRAM taken: 59.68%, Block Peak % of device VRAM: 62.35%, ΔTime: 00:00:32 [2025-11-13 01:52:15,934][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 01:52:15,936][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 01:52:15,938][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 01:52:16,875][__main__][INFO] - Iteration 258 took 52s (31.63% Gen, 66.59% Train). Generation: 16s, Training: 35s. Estimated remaining time: 40h 22m 37s. Estimated total time: 44h 5m 12s. Time estimates for 10 more iterations: 8m 49s, 100 more iterations: 1h 28m 10s, 500 more iterations: 7h 20m 52s. [2025-11-13 01:52:16,877][__main__][INFO] - Starting iteration 258. [2025-11-13 01:52:17,389][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 25 and human policies 1. [2025-11-13 01:52:17,389][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 01:52:29,004][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 01:52:32,686][__main__][INFO] - Number of regex retries in iteration 258: 1 [2025-11-13 01:52:32,687][__main__][INFO] - agents played in iteration 258 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 01:52:33,538][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:52:33,573][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:52:33,603][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:52:33,628][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:52:33,629][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 01:52:33,630][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 01:52:34,277][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 01:52:34,739][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 01:52:35,252][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 01:52:35,758][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 01:52:36,266][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 01:52:36,771][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 01:52:37,277][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 01:52:37,782][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 01:52:38,289][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 01:52:38,818][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 01:52:39,323][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 01:52:39,829][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 01:52:40,332][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 01:52:40,836][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 01:52:41,343][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 01:52:41,847][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 01:52:42,350][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 01:52:42,861][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 01:52:43,380][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 01:52:43,883][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 01:52:44,386][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 01:52:44,890][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 01:52:45,394][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 01:52:45,900][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 01:52:46,403][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 01:52:46,917][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 01:52:47,422][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 01:52:47,936][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 01:52:48,438][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 01:52:48,939][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 01:52:49,443][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 01:52:49,945][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 01:52:50,449][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 01:52:50,950][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 01:52:51,450][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 01:52:51,957][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 01:52:52,462][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 01:52:52,967][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 01:52:53,470][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 01:52:53,971][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 01:52:54,477][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 01:52:54,979][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 01:52:55,480][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 01:52:55,982][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 01:52:56,484][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 01:52:56,987][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 01:52:57,491][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 01:52:57,996][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 01:52:58,497][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 01:52:58,998][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 01:52:59,498][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 01:53:00,000][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 01:53:00,500][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 01:53:01,000][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 01:53:01,507][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 01:53:02,007][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 01:53:02,510][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 01:53:03,008][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 01:53:03,508][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 01:53:04,016][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 01:53:04,516][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 01:53:05,014][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 01:53:05,512][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 01:53:06,010][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 01:53:06,514][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10879 tokens. [2025-11-13 01:53:07,180][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.22%, Current % of VRAM taken: 59.67%, Block Peak % of device VRAM: 62.42%, ΔTime: 00:00:32 [2025-11-13 01:53:08,013][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 01:53:08,015][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 01:53:08,016][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 01:53:08,927][__main__][INFO] - Iteration 259 took 51s (29.68% Gen, 68.55% Train). Generation: 15s, Training: 35s. Estimated remaining time: 39h 13m 30s. Estimated total time: 42h 56m 57s. Time estimates for 10 more iterations: 8m 35s, 100 more iterations: 1h 25m 53s, 500 more iterations: 7h 9m 29s. [2025-11-13 01:53:08,929][__main__][INFO] - Starting iteration 259. [2025-11-13 01:53:09,424][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 25 and human policies 1. [2025-11-13 01:53:09,426][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 01:53:15,577][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 01:53:27,005][__main__][INFO] - Number of regex retries in iteration 259: 1 [2025-11-13 01:53:27,006][__main__][INFO] - agents played in iteration 259 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 01:53:27,819][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:53:27,844][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:53:27,869][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:53:27,892][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:53:27,892][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 01:53:27,893][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 01:53:28,579][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 01:53:29,052][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 01:53:29,560][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 01:53:30,063][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 01:53:30,568][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 01:53:31,071][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 01:53:31,574][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 01:53:32,078][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 01:53:32,584][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 01:53:33,087][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 01:53:33,592][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 01:53:45,128][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 01:53:45,632][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 01:53:46,150][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 01:53:46,651][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 01:53:47,152][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 01:53:47,657][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 01:53:48,159][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 01:53:48,661][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 01:53:49,163][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 01:53:49,664][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 01:53:50,170][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 01:53:50,673][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 01:53:51,181][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 01:53:51,686][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 01:53:52,186][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 01:53:52,687][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 01:53:53,188][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 01:53:53,688][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 01:53:54,190][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 01:53:54,687][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 01:53:55,187][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 01:53:55,689][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 01:53:56,186][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 01:53:56,687][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 01:53:57,187][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 01:53:57,687][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 01:53:58,196][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 01:53:58,698][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 01:53:59,200][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 01:53:59,700][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 01:54:00,204][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 01:54:00,707][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10880 tokens. [2025-11-13 01:54:01,372][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.24%, Current % of VRAM taken: 59.69%, Block Peak % of device VRAM: 62.31%, ΔTime: 00:00:32 [2025-11-13 01:54:02,158][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 01:54:02,160][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 01:54:02,162][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 01:54:03,091][__main__][INFO] - Iteration 260 took 53s (32.76% Gen, 65.51% Train). Generation: 17s, Training: 35s. Estimated remaining time: 40h 59m 2s. Estimated total time: 44h 43m 23s. Time estimates for 10 more iterations: 8m 56s, 100 more iterations: 1h 29m 26s, 500 more iterations: 7h 27m 13s. [2025-11-13 01:54:03,095][__main__][INFO] - Starting iteration 260. [2025-11-13 01:54:03,604][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 25 and human policies 1. [2025-11-13 01:54:03,605][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 01:54:19,472][__main__][INFO] - Number of regex retries in iteration 260: 0 [2025-11-13 01:54:19,473][__main__][INFO] - agents played in iteration 260 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 01:54:20,313][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:54:20,340][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:54:20,366][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:54:20,389][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:54:20,389][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 01:54:20,390][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 01:54:21,067][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 01:54:21,531][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 01:54:22,040][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 01:54:22,545][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 01:54:23,065][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 01:54:23,571][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 01:54:24,075][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 01:54:24,581][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 01:54:25,088][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 01:54:25,596][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 01:54:26,100][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 01:54:37,691][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 01:54:38,193][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 01:54:38,695][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 01:54:39,200][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 01:54:39,704][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 01:54:40,206][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 01:54:40,706][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 01:54:41,209][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 01:54:41,710][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 01:54:42,211][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 01:54:42,711][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 01:54:43,213][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 01:54:43,714][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 01:54:44,218][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 01:54:44,720][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 01:54:45,220][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 01:54:45,720][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 01:54:46,219][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 01:54:46,718][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 01:54:47,217][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 01:54:47,714][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 01:54:48,214][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 01:54:48,712][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 01:54:49,211][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 01:54:49,712][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 01:54:50,211][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 01:54:50,711][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 01:54:51,211][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 01:54:51,713][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 01:54:52,214][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 01:54:52,712][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 01:54:53,211][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10875 tokens. [2025-11-13 01:54:53,877][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.22%, Current % of VRAM taken: 59.68%, Block Peak % of device VRAM: 62.32%, ΔTime: 00:00:32 [2025-11-13 01:54:54,709][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 01:54:54,711][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 01:54:54,713][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 01:54:56,469][__main__][INFO] - Iteration 261 took 52s (30.02% Gen, 66.66% Train). Generation: 15s, Training: 35s. Estimated remaining time: 40h 18m 1s. Estimated total time: 44h 3m 16s. Time estimates for 10 more iterations: 8m 48s, 100 more iterations: 1h 28m 6s, 500 more iterations: 7h 20m 32s. [2025-11-13 01:54:56,471][__main__][INFO] - Starting iteration 261. [2025-11-13 01:54:56,949][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 26 and human policies 1. [2025-11-13 01:54:56,950][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 01:55:13,655][__main__][INFO] - Number of regex retries in iteration 261: 0 [2025-11-13 01:55:13,656][__main__][INFO] - agents played in iteration 261 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 01:55:14,510][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:55:14,537][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:55:14,565][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:55:14,587][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:55:14,588][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 01:55:14,589][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 01:55:15,302][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 01:55:15,766][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 01:55:16,273][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 01:55:16,774][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 01:55:17,278][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 01:55:17,782][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 01:55:18,290][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 01:55:18,794][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 01:55:19,297][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 01:55:19,805][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 01:55:20,310][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 01:55:20,815][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 01:55:21,317][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 01:55:21,821][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 01:55:22,324][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 01:55:22,827][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 01:55:23,330][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 01:55:23,829][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 01:55:24,329][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 01:55:24,828][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 01:55:25,326][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 01:55:25,825][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 01:55:31,858][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 01:55:32,361][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 01:55:32,885][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 01:55:33,385][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 01:55:33,887][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 01:55:34,390][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 01:55:34,891][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 01:55:35,394][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 01:55:35,896][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 01:55:36,397][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 01:55:36,900][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 01:55:37,404][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 01:55:37,912][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 01:55:38,412][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 01:55:38,915][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 01:55:39,419][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 01:55:39,923][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 01:55:40,424][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 01:55:40,928][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 01:55:41,429][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 01:55:41,930][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 01:55:42,432][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 01:55:42,931][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 01:55:43,435][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 01:55:43,938][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 01:55:44,436][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 01:55:44,938][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 01:55:45,437][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 01:55:45,934][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 01:55:46,432][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 01:55:46,930][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 01:55:47,427][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10864 tokens. [2025-11-13 01:55:48,125][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.12%, Current % of VRAM taken: 59.57%, Block Peak % of device VRAM: 62.30%, ΔTime: 00:00:32 [2025-11-13 01:55:48,925][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 01:55:48,927][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 01:55:48,929][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 01:55:50,080][__main__][INFO] - Iteration 262 took 53s (31.44% Gen, 66.39% Train). Generation: 16s, Training: 35s. Estimated remaining time: 40h 30m 25s. Estimated total time: 44h 16m 33s. Time estimates for 10 more iterations: 8m 51s, 100 more iterations: 1h 28m 33s, 500 more iterations: 7h 22m 45s. [2025-11-13 01:55:50,082][__main__][INFO] - Starting iteration 262. [2025-11-13 01:55:50,571][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 26 and human policies 1. [2025-11-13 01:55:50,572][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 01:56:06,665][__main__][INFO] - Number of regex retries in iteration 262: 0 [2025-11-13 01:56:06,666][__main__][INFO] - agents played in iteration 262 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 01:56:07,492][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.54%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:56:07,520][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.54%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:56:07,546][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.54%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:56:07,569][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.54%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:56:07,569][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 01:56:07,570][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 01:56:08,270][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 01:56:08,728][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 01:56:09,234][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 01:56:09,735][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 01:56:10,236][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 01:56:10,741][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 01:56:11,244][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 01:56:11,749][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 01:56:12,253][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 01:56:12,761][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 01:56:13,264][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 01:56:24,856][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 01:56:25,357][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 01:56:25,859][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 01:56:26,360][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 01:56:26,860][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 01:56:27,362][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 01:56:27,862][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 01:56:28,363][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 01:56:28,860][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 01:56:29,362][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 01:56:29,877][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 01:56:30,377][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 01:56:30,875][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 01:56:31,374][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 01:56:31,874][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 01:56:32,392][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 01:56:32,896][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 01:56:33,399][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 01:56:33,911][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 01:56:34,415][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 01:56:34,914][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 01:56:35,414][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 01:56:35,914][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 01:56:36,417][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 01:56:36,915][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 01:56:37,416][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 01:56:37,914][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 01:56:38,416][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 01:56:38,914][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 01:56:39,412][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 01:56:39,913][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 01:56:40,412][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10868 tokens. [2025-11-13 01:56:41,103][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.25%, Current % of VRAM taken: 59.70%, Block Peak % of device VRAM: 62.42%, ΔTime: 00:00:32 [2025-11-13 01:56:41,870][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 01:56:41,871][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 01:56:41,873][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 01:56:42,787][__main__][INFO] - Iteration 263 took 52s (30.82% Gen, 67.42% Train). Generation: 16s, Training: 35s. Estimated remaining time: 39h 43m 48s. Estimated total time: 43h 30m 49s. Time estimates for 10 more iterations: 8m 42s, 100 more iterations: 1h 27m 1s, 500 more iterations: 7h 15m 8s. [2025-11-13 01:56:42,789][__main__][INFO] - Starting iteration 263. [2025-11-13 01:56:43,263][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 26 and human policies 1. [2025-11-13 01:56:43,263][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 01:56:51,508][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 01:56:58,342][__main__][INFO] - Number of regex retries in iteration 263: 1 [2025-11-13 01:56:58,343][__main__][INFO] - agents played in iteration 263 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 01:56:59,140][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:56:59,171][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:56:59,197][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:56:59,221][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:56:59,221][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 01:56:59,222][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 01:56:59,894][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 01:57:00,353][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 01:57:00,862][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 01:57:01,379][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 01:57:01,886][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 01:57:02,393][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 01:57:02,895][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 01:57:03,401][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 01:57:03,910][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 01:57:04,411][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 01:57:04,920][mllm.training.trainer_common][INFO] - 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Processing mini-batch 21 of 64 [2025-11-13 01:57:10,979][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 01:57:11,484][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 01:57:12,002][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 01:57:12,505][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 01:57:13,008][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 01:57:13,509][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 01:57:14,011][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 01:57:14,515][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 01:57:15,016][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 01:57:15,519][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 01:57:16,031][mllm.training.trainer_common][INFO] - 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Processing mini-batch 43 of 64 [2025-11-13 01:57:22,045][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 01:57:22,550][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 01:57:23,051][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 01:57:23,550][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 01:57:24,052][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 01:57:24,556][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 01:57:25,062][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 01:57:25,580][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 01:57:26,083][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 01:57:26,601][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 01:57:27,101][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 01:57:27,601][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 01:57:28,102][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 01:57:28,603][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 01:57:29,105][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 01:57:29,605][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 01:57:30,105][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 01:57:30,606][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 01:57:31,107][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 01:57:31,607][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 01:57:32,108][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10872 tokens. [2025-11-13 01:57:32,798][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.22%, Current % of VRAM taken: 59.68%, Block Peak % of device VRAM: 62.29%, ΔTime: 00:00:32 [2025-11-13 01:57:33,575][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 01:57:33,577][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 01:57:33,578][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 01:57:34,632][__main__][INFO] - Iteration 264 took 51s (29.35% Gen, 68.59% Train). Generation: 15s, Training: 35s. Estimated remaining time: 39h 0m 37s. Estimated total time: 42h 48m 30s. Time estimates for 10 more iterations: 8m 33s, 100 more iterations: 1h 25m 37s, 500 more iterations: 7h 8m 5s. [2025-11-13 01:57:34,634][__main__][INFO] - Starting iteration 264. [2025-11-13 01:57:35,100][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 26 and human policies 1. [2025-11-13 01:57:35,101][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 01:57:39,528][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 01:57:49,560][__main__][INFO] - Number of regex retries in iteration 264: 1 [2025-11-13 01:57:49,561][__main__][INFO] - agents played in iteration 264 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 01:57:50,355][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:57:50,378][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:57:50,401][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:57:50,423][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:57:50,424][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 01:57:50,425][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 01:57:51,151][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 01:57:51,613][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 01:57:52,118][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 01:57:52,621][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 01:57:53,127][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 01:57:53,631][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 01:57:54,135][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 01:57:54,637][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 01:57:55,140][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 01:57:55,645][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 01:57:56,152][mllm.training.trainer_common][INFO] - 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Processing mini-batch 43 of 64 [2025-11-13 01:58:13,275][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 01:58:13,776][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 01:58:14,300][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 01:58:14,801][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 01:58:15,303][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 01:58:15,805][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 01:58:16,305][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 01:58:16,809][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 01:58:17,308][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 01:58:17,812][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 01:58:18,315][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 01:58:18,819][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 01:58:19,320][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 01:58:19,817][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 01:58:20,316][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 01:58:20,820][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 01:58:21,319][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 01:58:21,822][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 01:58:22,323][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 01:58:22,820][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 01:58:23,318][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10878 tokens. [2025-11-13 01:58:24,005][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.21%, Current % of VRAM taken: 59.66%, Block Peak % of device VRAM: 62.34%, ΔTime: 00:00:32 [2025-11-13 01:58:24,806][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 01:58:24,807][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 01:58:24,809][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 01:58:25,724][__main__][INFO] - Iteration 265 took 50s (28.56% Gen, 69.63% Train). Generation: 14s, Training: 35s. Estimated remaining time: 38h 22m 30s. Estimated total time: 42h 11m 14s. Time estimates for 10 more iterations: 8m 26s, 100 more iterations: 1h 24m 22s, 500 more iterations: 7h 1m 52s. [2025-11-13 01:58:25,727][__main__][INFO] - Starting iteration 265. [2025-11-13 01:58:26,200][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 26 and human policies 1. [2025-11-13 01:58:26,200][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 01:58:33,348][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 01:58:33,818][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 01:58:42,753][__main__][INFO] - Number of regex retries in iteration 265: 2 [2025-11-13 01:58:42,754][__main__][INFO] - agents played in iteration 265 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 01:58:43,608][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:58:43,636][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:58:43,663][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:58:43,686][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:58:43,687][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 01:58:43,688][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 01:58:44,390][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 01:58:44,849][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 01:58:45,358][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 01:58:45,867][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 01:58:46,374][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 01:58:46,881][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 01:58:47,394][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 01:58:47,896][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 01:58:48,401][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 01:58:48,911][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 01:58:49,420][mllm.training.trainer_common][INFO] - 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Processing mini-batch 21 of 64 [2025-11-13 01:58:55,510][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 01:58:56,012][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 01:58:56,516][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 01:58:57,018][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 01:58:57,522][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 01:58:58,025][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 01:58:58,529][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 01:58:59,034][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 01:58:59,536][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 01:59:00,040][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 01:59:00,542][mllm.training.trainer_common][INFO] - 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Processing mini-batch 43 of 64 [2025-11-13 01:59:06,562][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 01:59:07,064][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 01:59:07,563][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 01:59:08,064][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 01:59:08,565][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 01:59:09,067][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 01:59:09,570][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 01:59:10,072][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 01:59:10,572][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 01:59:11,075][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 01:59:11,575][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 01:59:12,078][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 01:59:12,580][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 01:59:13,084][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 01:59:13,590][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 01:59:14,091][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 01:59:14,592][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 01:59:15,093][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 01:59:15,596][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 01:59:16,113][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 01:59:16,613][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10876 tokens. [2025-11-13 01:59:17,308][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.23%, Current % of VRAM taken: 59.68%, Block Peak % of device VRAM: 62.31%, ΔTime: 00:00:32 [2025-11-13 01:59:18,091][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 01:59:18,093][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 01:59:18,094][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 01:59:19,081][__main__][INFO] - Iteration 266 took 52s (31.30% Gen, 66.83% Train). Generation: 16s, Training: 35s. Estimated remaining time: 40h 14m 28s. Estimated total time: 44h 4m 5s. Time estimates for 10 more iterations: 8m 48s, 100 more iterations: 1h 28m 8s, 500 more iterations: 7h 20m 40s. [2025-11-13 01:59:19,083][__main__][INFO] - Starting iteration 266. [2025-11-13 01:59:19,553][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 26 and human policies 1. [2025-11-13 01:59:19,554][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 01:59:24,306][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 01:59:32,254][__main__][INFO] - Number of regex retries in iteration 266: 1 [2025-11-13 01:59:32,254][__main__][INFO] - agents played in iteration 266 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 01:59:33,120][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:59:33,149][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:59:33,175][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:59:33,198][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 01:59:33,199][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 01:59:33,199][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 01:59:33,837][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 01:59:34,307][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 01:59:34,814][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 01:59:35,318][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 01:59:35,830][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 01:59:36,333][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 01:59:36,835][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 01:59:37,340][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 01:59:37,847][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 01:59:38,357][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 01:59:38,859][mllm.training.trainer_common][INFO] - 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Processing mini-batch 43 of 64 [2025-11-13 01:59:55,988][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 01:59:56,489][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 01:59:56,992][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 01:59:57,492][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 01:59:57,995][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 01:59:58,496][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 01:59:58,999][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 01:59:59,514][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 02:00:00,013][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 02:00:00,513][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 02:00:01,028][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 02:00:01,528][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 02:00:02,029][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 02:00:02,532][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 02:00:03,034][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 02:00:03,537][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 02:00:04,039][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 02:00:04,546][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 02:00:05,049][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 02:00:05,550][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 02:00:06,053][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10870 tokens. [2025-11-13 02:00:06,765][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.25%, Current % of VRAM taken: 59.71%, Block Peak % of device VRAM: 62.32%, ΔTime: 00:00:32 [2025-11-13 02:00:07,555][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 02:00:07,556][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 02:00:07,558][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 02:00:08,555][__main__][INFO] - Iteration 267 took 49s (25.92% Gen, 72.05% Train). Generation: 12s, Training: 35s. Estimated remaining time: 36h 59m 38s. Estimated total time: 40h 50m 5s. Time estimates for 10 more iterations: 8m 10s, 100 more iterations: 1h 21m 40s, 500 more iterations: 6h 48m 20s. [2025-11-13 02:00:08,557][__main__][INFO] - Starting iteration 267. [2025-11-13 02:00:09,033][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 26 and human policies 1. [2025-11-13 02:00:09,033][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 02:00:16,530][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 02:00:25,041][__main__][INFO] - Number of regex retries in iteration 267: 1 [2025-11-13 02:00:25,041][__main__][INFO] - agents played in iteration 267 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 02:00:25,841][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:00:25,863][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:00:25,886][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:00:25,909][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:00:25,910][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 02:00:25,911][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 02:00:26,569][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 02:00:27,024][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 02:00:27,529][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 02:00:28,029][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 02:00:28,537][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 02:00:29,037][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 02:00:29,534][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 02:00:30,047][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 02:00:30,547][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 02:00:31,050][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 02:00:31,553][mllm.training.trainer_common][INFO] - 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Processing mini-batch 43 of 64 [2025-11-13 02:00:48,683][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 02:00:49,186][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 02:00:49,688][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 02:00:50,194][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 02:00:50,699][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 02:00:51,215][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 02:00:51,718][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 02:00:52,230][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 02:00:52,732][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 02:00:53,233][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 02:00:53,736][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 02:00:54,237][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 02:00:54,740][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 02:00:55,246][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 02:00:55,764][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 02:00:56,265][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 02:00:56,767][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 02:00:57,270][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 02:00:57,772][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 02:00:58,273][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 02:00:58,776][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10853 tokens. [2025-11-13 02:00:59,479][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.23%, Current % of VRAM taken: 59.68%, Block Peak % of device VRAM: 62.18%, ΔTime: 00:00:32 [2025-11-13 02:01:00,262][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 02:01:00,263][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 02:01:00,265][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 02:01:01,323][__main__][INFO] - Iteration 268 took 52s (30.61% Gen, 67.36% Train). Generation: 16s, Training: 35s. Estimated remaining time: 39h 43m 13s. Estimated total time: 43h 34m 32s. Time estimates for 10 more iterations: 8m 42s, 100 more iterations: 1h 27m 9s, 500 more iterations: 7h 15m 45s. [2025-11-13 02:01:01,325][__main__][INFO] - Starting iteration 268. [2025-11-13 02:01:01,807][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 26 and human policies 1. [2025-11-13 02:01:01,808][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 02:01:10,667][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 02:01:18,783][__main__][INFO] - Number of regex retries in iteration 268: 1 [2025-11-13 02:01:18,784][__main__][INFO] - agents played in iteration 268 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 02:01:19,609][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:01:19,631][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:01:19,654][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:01:19,677][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:01:19,678][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 02:01:19,679][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 02:01:20,353][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 02:01:20,810][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 02:01:21,328][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 02:01:21,830][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 02:01:22,334][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 02:01:22,835][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 02:01:23,335][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 02:01:23,842][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 02:01:24,346][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 02:01:24,854][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 02:01:25,363][mllm.training.trainer_common][INFO] - 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Processing mini-batch 43 of 64 [2025-11-13 02:01:42,510][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 02:01:43,012][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 02:01:43,522][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 02:01:44,025][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 02:01:44,532][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 02:01:45,036][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 02:01:45,537][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 02:01:46,037][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 02:01:46,538][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 02:01:47,037][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 02:01:47,552][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 02:01:48,052][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 02:01:48,552][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 02:01:49,053][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 02:01:49,553][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 02:01:50,053][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 02:01:50,555][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 02:01:51,057][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 02:01:51,567][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 02:01:52,069][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 02:01:52,571][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10879 tokens. [2025-11-13 02:01:53,284][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.23%, Current % of VRAM taken: 59.68%, Block Peak % of device VRAM: 62.31%, ΔTime: 00:00:32 [2025-11-13 02:01:54,077][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 02:01:54,078][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 02:01:54,080][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 02:01:54,985][__main__][INFO] - Iteration 269 took 53s (31.92% Gen, 66.37% Train). Generation: 16s, Training: 35s. Estimated remaining time: 40h 26m 42s. Estimated total time: 44h 18m 55s. Time estimates for 10 more iterations: 8m 51s, 100 more iterations: 1h 28m 37s, 500 more iterations: 7h 23m 9s. [2025-11-13 02:01:54,987][__main__][INFO] - Starting iteration 269. [2025-11-13 02:01:55,466][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 26 and human policies 1. [2025-11-13 02:01:55,467][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 02:02:00,286][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 02:02:12,087][__main__][INFO] - Number of regex retries in iteration 269: 1 [2025-11-13 02:02:12,087][__main__][INFO] - agents played in iteration 269 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 02:02:12,923][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:02:12,946][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:02:12,968][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:02:12,990][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:02:12,990][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 02:02:12,991][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 02:02:13,673][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 02:02:14,144][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 02:02:14,650][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 02:02:15,164][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 02:02:15,668][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 02:02:16,169][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 02:02:16,679][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 02:02:17,180][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 02:02:17,687][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 02:02:18,191][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 02:02:18,694][mllm.training.trainer_common][INFO] - 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Processing mini-batch 21 of 64 [2025-11-13 02:02:24,733][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 02:02:25,234][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 02:02:25,749][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 02:02:26,256][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 02:02:26,759][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 02:02:27,266][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 02:02:27,770][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 02:02:28,288][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 02:02:28,796][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 02:02:29,305][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 02:02:29,813][mllm.training.trainer_common][INFO] - 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Processing mini-batch 43 of 64 [2025-11-13 02:02:35,891][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 02:02:36,400][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 02:02:36,909][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 02:02:37,415][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 02:02:37,921][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 02:02:38,426][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 02:02:38,931][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 02:02:39,433][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 02:02:39,933][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 02:02:40,449][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 02:02:40,951][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 02:02:41,460][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 02:02:41,961][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 02:02:42,473][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 02:02:42,979][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 02:02:43,484][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 02:02:43,989][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 02:02:44,494][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 02:02:44,999][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 02:02:45,502][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 02:02:46,006][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10877 tokens. [2025-11-13 02:02:46,699][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.31%, Current % of VRAM taken: 59.76%, Block Peak % of device VRAM: 62.52%, ΔTime: 00:00:33 [2025-11-13 02:02:47,473][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 02:02:47,475][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 02:02:47,476][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 02:02:48,512][__main__][INFO] - Iteration 270 took 53s (31.33% Gen, 66.71% Train). Generation: 16s, Training: 35s. Estimated remaining time: 40h 19m 12s. Estimated total time: 44h 12m 18s. Time estimates for 10 more iterations: 8m 50s, 100 more iterations: 1h 28m 24s, 500 more iterations: 7h 22m 3s. [2025-11-13 02:02:48,514][__main__][INFO] - Starting iteration 270. [2025-11-13 02:02:48,983][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 26 and human policies 1. [2025-11-13 02:02:48,984][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 02:02:56,654][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 02:02:59,806][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 02:03:04,583][__main__][INFO] - Number of regex retries in iteration 270: 2 [2025-11-13 02:03:04,584][__main__][INFO] - agents played in iteration 270 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 02:03:05,381][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.62%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:03:05,418][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.62%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:03:05,441][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.62%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:03:05,464][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.62%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:03:05,464][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 02:03:05,465][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 02:03:06,146][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 02:03:06,606][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 02:03:07,112][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 02:03:07,615][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 02:03:08,118][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 02:03:08,622][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 02:03:09,127][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 02:03:09,629][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 02:03:10,130][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 02:03:10,631][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 02:03:11,135][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 02:03:22,741][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 02:03:23,244][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 02:03:23,749][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 02:03:24,262][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 02:03:24,765][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 02:03:25,270][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 02:03:25,777][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 02:03:26,281][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 02:03:26,797][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 02:03:27,301][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 02:03:27,804][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 02:03:28,310][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 02:03:28,814][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 02:03:29,318][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 02:03:29,823][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 02:03:30,325][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 02:03:30,831][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 02:03:31,337][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 02:03:31,842][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 02:03:32,350][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 02:03:32,856][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 02:03:33,356][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 02:03:33,857][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 02:03:34,358][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 02:03:34,866][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 02:03:35,363][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 02:03:35,865][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 02:03:36,378][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 02:03:36,877][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 02:03:37,400][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 02:03:37,898][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 02:03:38,399][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10873 tokens. [2025-11-13 02:03:39,085][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.22%, Current % of VRAM taken: 59.67%, Block Peak % of device VRAM: 62.35%, ΔTime: 00:00:32 [2025-11-13 02:03:39,866][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 02:03:39,868][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 02:03:39,870][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 02:03:41,617][__main__][INFO] - Iteration 271 took 52s (29.64% Gen, 67.04% Train). Generation: 15s, Training: 35s. Estimated remaining time: 39h 57m 42s. Estimated total time: 43h 51m 42s. Time estimates for 10 more iterations: 8m 46s, 100 more iterations: 1h 27m 43s, 500 more iterations: 7h 18m 37s. [2025-11-13 02:03:41,619][__main__][INFO] - Starting iteration 271. [2025-11-13 02:03:42,097][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 27 and human policies 1. [2025-11-13 02:03:42,098][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 02:03:50,375][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 02:03:56,041][__main__][INFO] - Number of regex retries in iteration 271: 1 [2025-11-13 02:03:56,042][__main__][INFO] - agents played in iteration 271 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 02:03:56,906][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:03:56,943][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:03:56,969][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:03:56,994][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:03:56,995][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 02:03:56,995][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 02:03:57,692][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 02:03:58,151][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 02:03:58,663][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 02:03:59,168][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 02:03:59,672][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 02:04:00,177][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 02:04:00,678][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 02:04:01,182][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 02:04:01,683][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 02:04:02,183][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 02:04:02,691][mllm.training.trainer_common][INFO] - 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Processing mini-batch 21 of 64 [2025-11-13 02:04:08,742][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 02:04:09,245][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 02:04:09,747][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 02:04:10,252][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 02:04:10,754][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 02:04:11,257][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 02:04:11,760][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 02:04:12,264][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 02:04:12,766][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 02:04:13,268][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 02:04:13,771][mllm.training.trainer_common][INFO] - 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Processing mini-batch 43 of 64 [2025-11-13 02:04:19,805][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 02:04:20,306][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 02:04:20,818][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 02:04:21,322][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 02:04:21,823][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 02:04:22,326][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 02:04:22,829][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 02:04:23,334][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 02:04:23,838][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 02:04:24,341][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 02:04:24,843][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 02:04:25,345][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 02:04:25,850][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 02:04:26,353][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 02:04:26,855][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 02:04:27,360][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 02:04:27,861][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 02:04:28,362][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 02:04:28,861][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 02:04:29,362][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 02:04:29,864][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10873 tokens. [2025-11-13 02:04:30,546][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.20%, Current % of VRAM taken: 59.65%, Block Peak % of device VRAM: 62.24%, ΔTime: 00:00:32 [2025-11-13 02:04:31,335][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 02:04:31,337][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 02:04:31,339][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 02:04:32,285][__main__][INFO] - Iteration 272 took 50s (27.78% Gen, 70.33% Train). Generation: 13s, Training: 35s. Estimated remaining time: 37h 54m 34s. Estimated total time: 41h 49m 25s. Time estimates for 10 more iterations: 8m 21s, 100 more iterations: 1h 23m 38s, 500 more iterations: 6h 58m 14s. [2025-11-13 02:04:32,287][__main__][INFO] - Starting iteration 272. [2025-11-13 02:04:32,770][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 27 and human policies 1. [2025-11-13 02:04:32,770][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 02:04:50,184][__main__][INFO] - Number of regex retries in iteration 272: 0 [2025-11-13 02:04:50,185][__main__][INFO] - agents played in iteration 272 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 02:04:51,033][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:04:51,056][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:04:51,079][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:04:51,101][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:04:51,102][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 02:04:51,102][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 02:04:51,781][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 02:04:52,246][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 02:04:52,755][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 02:04:53,259][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 02:04:53,761][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 02:04:54,268][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 02:04:54,771][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 02:04:55,277][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 02:04:55,779][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 02:04:56,287][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 02:04:56,788][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 02:05:08,341][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 02:05:08,843][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 02:05:09,346][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 02:05:09,846][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 02:05:10,346][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 02:05:10,843][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 02:05:11,342][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 02:05:11,846][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 02:05:12,345][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 02:05:12,846][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 02:05:13,348][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 02:05:13,847][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 02:05:14,348][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 02:05:14,850][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 02:05:15,354][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 02:05:15,859][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 02:05:16,365][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 02:05:16,871][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 02:05:17,376][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 02:05:17,879][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 02:05:18,382][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 02:05:18,888][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 02:05:19,393][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 02:05:19,912][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 02:05:20,416][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 02:05:20,919][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 02:05:21,422][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 02:05:21,923][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 02:05:22,431][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 02:05:22,933][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 02:05:23,435][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 02:05:23,940][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10875 tokens. [2025-11-13 02:05:24,608][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.26%, Current % of VRAM taken: 59.71%, Block Peak % of device VRAM: 62.32%, ΔTime: 00:00:32 [2025-11-13 02:05:25,411][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 02:05:25,413][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 02:05:25,414][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 02:05:26,430][__main__][INFO] - Iteration 273 took 53s (32.45% Gen, 65.65% Train). Generation: 17s, Training: 35s. Estimated remaining time: 40h 47m 16s. Estimated total time: 44h 43m 1s. Time estimates for 10 more iterations: 8m 56s, 100 more iterations: 1h 29m 26s, 500 more iterations: 7h 27m 10s. [2025-11-13 02:05:26,432][__main__][INFO] - Starting iteration 273. [2025-11-13 02:05:26,899][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 27 and human policies 1. [2025-11-13 02:05:26,900][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 02:05:41,298][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 02:05:42,137][__main__][INFO] - Number of regex retries in iteration 273: 1 [2025-11-13 02:05:42,137][__main__][INFO] - agents played in iteration 273 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 02:05:42,981][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:05:43,004][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:05:43,026][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:05:43,049][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:05:43,049][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 02:05:43,051][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 02:05:43,711][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 02:05:44,170][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 02:05:44,677][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 02:05:45,182][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 02:05:45,688][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 02:05:46,193][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 02:05:46,695][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 02:05:47,198][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 02:05:47,700][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 02:05:48,205][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 02:05:48,731][mllm.training.trainer_common][INFO] - 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Processing mini-batch 54 of 64 [2025-11-13 02:06:11,380][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 02:06:11,883][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 02:06:12,387][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 02:06:12,891][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 02:06:13,395][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 02:06:13,909][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 02:06:14,413][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 02:06:14,927][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 02:06:15,430][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 02:06:15,932][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10880 tokens. [2025-11-13 02:06:16,633][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.24%, Current % of VRAM taken: 59.69%, Block Peak % of device VRAM: 62.35%, ΔTime: 00:00:32 [2025-11-13 02:06:17,380][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 02:06:17,382][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 02:06:17,384][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 02:06:18,306][__main__][INFO] - Iteration 274 took 51s (29.64% Gen, 68.56% Train). Generation: 15s, Training: 35s. Estimated remaining time: 38h 53m 46s. Estimated total time: 42h 50m 23s. Time estimates for 10 more iterations: 8m 34s, 100 more iterations: 1h 25m 40s, 500 more iterations: 7h 8m 23s. [2025-11-13 02:06:18,309][__main__][INFO] - Starting iteration 274. [2025-11-13 02:06:18,783][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 27 and human policies 1. [2025-11-13 02:06:18,784][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 02:06:24,222][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 02:06:32,888][__main__][INFO] - Number of regex retries in iteration 274: 1 [2025-11-13 02:06:32,888][__main__][INFO] - agents played in iteration 274 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 02:06:33,668][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:06:33,691][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:06:33,713][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:06:33,734][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:06:33,735][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 02:06:33,736][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 02:06:34,372][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 02:06:34,845][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 02:06:35,350][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 02:06:35,853][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 02:06:36,351][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 02:06:36,855][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 02:06:37,364][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 02:06:37,869][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 02:06:38,372][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 02:06:38,874][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 02:06:39,375][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 02:06:50,974][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 02:06:51,478][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 02:06:51,981][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 02:06:52,483][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 02:06:52,990][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 02:06:53,493][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 02:06:53,994][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 02:06:54,496][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 02:06:54,997][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 02:06:55,501][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 02:06:56,003][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 02:06:56,503][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 02:06:57,007][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 02:06:57,511][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 02:06:58,014][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 02:06:58,530][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 02:06:59,033][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 02:06:59,550][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 02:07:00,052][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 02:07:00,553][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 02:07:01,054][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 02:07:01,556][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 02:07:02,061][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 02:07:02,561][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 02:07:03,063][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 02:07:03,569][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 02:07:04,072][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 02:07:04,577][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 02:07:05,083][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 02:07:05,587][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 02:07:06,092][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 02:07:06,596][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10877 tokens. [2025-11-13 02:07:07,345][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.27%, Current % of VRAM taken: 59.72%, Block Peak % of device VRAM: 62.32%, ΔTime: 00:00:32 [2025-11-13 02:07:08,116][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 02:07:08,117][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 02:07:08,120][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 02:07:09,013][__main__][INFO] - Iteration 275 took 50s (28.08% Gen, 70.14% Train). Generation: 14s, Training: 35s. Estimated remaining time: 37h 54m 3s. Estimated total time: 41h 51m 30s. Time estimates for 10 more iterations: 8m 22s, 100 more iterations: 1h 23m 43s, 500 more iterations: 6h 58m 35s. [2025-11-13 02:07:09,015][__main__][INFO] - Starting iteration 275. [2025-11-13 02:07:09,495][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 27 and human policies 1. [2025-11-13 02:07:09,495][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 02:07:16,687][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 02:07:20,882][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 02:07:24,997][__main__][INFO] - Number of regex retries in iteration 275: 2 [2025-11-13 02:07:24,997][__main__][INFO] - agents played in iteration 275 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 02:07:25,790][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:07:25,812][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:07:25,835][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:07:25,857][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:07:25,858][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 02:07:25,859][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 02:07:26,519][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 02:07:26,977][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 02:07:27,479][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 02:07:27,981][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 02:07:28,483][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 02:07:28,983][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 02:07:29,484][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 02:07:29,984][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 02:07:30,488][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 02:07:30,993][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 02:07:31,495][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 02:07:31,999][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 02:07:32,502][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 02:07:33,002][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 02:07:33,508][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 02:07:34,011][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 02:07:34,512][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 02:07:35,018][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 02:07:35,520][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 02:07:36,021][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 02:07:36,521][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 02:07:37,021][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 02:07:43,092][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 02:07:43,600][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 02:07:44,108][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 02:07:44,609][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 02:07:45,116][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 02:07:45,629][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 02:07:46,131][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 02:07:46,639][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 02:07:47,141][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 02:07:47,643][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 02:07:48,148][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 02:07:48,654][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 02:07:49,158][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 02:07:49,665][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 02:07:50,167][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 02:07:50,684][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 02:07:51,191][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 02:07:51,696][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 02:07:52,198][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 02:07:52,699][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 02:07:53,200][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 02:07:53,704][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 02:07:54,209][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 02:07:54,711][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 02:07:55,214][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 02:07:55,718][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 02:07:56,219][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 02:07:56,724][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 02:07:57,238][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 02:07:57,738][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 02:07:58,252][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 02:07:58,756][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10875 tokens. [2025-11-13 02:07:59,510][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.19%, Current % of VRAM taken: 59.65%, Block Peak % of device VRAM: 62.49%, ΔTime: 00:00:32 [2025-11-13 02:08:00,288][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 02:08:00,290][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 02:08:00,291][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 02:08:01,223][__main__][INFO] - Iteration 276 took 51s (29.97% Gen, 68.23% Train). Generation: 15s, Training: 35s. Estimated remaining time: 39h 8m 5s. Estimated total time: 43h 6m 25s. Time estimates for 10 more iterations: 8m 37s, 100 more iterations: 1h 26m 12s, 500 more iterations: 7h 11m 4s. [2025-11-13 02:08:01,225][__main__][INFO] - Starting iteration 276. [2025-11-13 02:08:01,697][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 27 and human policies 1. [2025-11-13 02:08:01,698][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 02:08:05,994][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 02:08:05,999][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 2 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 02:08:15,344][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 02:08:16,228][__main__][INFO] - Number of regex retries in iteration 276: 3 [2025-11-13 02:08:16,229][__main__][INFO] - agents played in iteration 276 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 02:08:17,004][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:08:17,034][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:08:17,061][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:08:17,084][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:08:17,085][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 02:08:17,085][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 02:08:17,775][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 02:08:18,235][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 02:08:18,744][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 02:08:19,270][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 02:08:19,771][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 02:08:20,273][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 02:08:20,775][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 02:08:21,275][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 02:08:21,778][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 02:08:22,285][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 02:08:22,787][mllm.training.trainer_common][INFO] - 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Processing mini-batch 21 of 64 [2025-11-13 02:08:28,850][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 02:08:29,353][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 02:08:29,858][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 02:08:30,360][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 02:08:30,862][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 02:08:31,364][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 02:08:31,870][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 02:08:32,382][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 02:08:32,884][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 02:08:33,388][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 02:08:33,891][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 02:08:34,393][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 02:08:34,895][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 02:08:35,396][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 02:08:35,897][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 02:08:36,404][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 02:08:36,908][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 02:08:37,410][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 02:08:37,913][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 02:08:38,418][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 02:08:38,928][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 02:08:39,432][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 02:08:39,935][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 02:08:40,452][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 02:08:40,955][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 02:08:41,458][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 02:08:41,962][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 02:08:42,468][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 02:08:42,974][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 02:08:43,476][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 02:08:43,982][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 02:08:44,484][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 02:08:44,985][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 02:08:45,495][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 02:08:45,999][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 02:08:46,501][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 02:08:47,004][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 02:08:47,508][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 02:08:48,011][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 02:08:48,514][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 02:08:49,015][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 02:08:49,519][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 02:08:50,022][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10877 tokens. [2025-11-13 02:08:50,716][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.30%, Current % of VRAM taken: 59.75%, Block Peak % of device VRAM: 62.41%, ΔTime: 00:00:32 [2025-11-13 02:08:51,481][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 02:08:51,482][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 02:08:51,484][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 02:08:52,425][__main__][INFO] - Iteration 277 took 50s (28.65% Gen, 69.50% Train). Generation: 14s, Training: 35s. Estimated remaining time: 38h 17m 14s. Estimated total time: 42h 16m 25s. Time estimates for 10 more iterations: 8m 27s, 100 more iterations: 1h 24m 32s, 500 more iterations: 7h 2m 44s. [2025-11-13 02:08:52,427][__main__][INFO] - Starting iteration 277. [2025-11-13 02:08:52,917][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 27 and human policies 1. [2025-11-13 02:08:52,917][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 02:08:57,907][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 02:09:07,624][__main__][INFO] - Number of regex retries in iteration 277: 1 [2025-11-13 02:09:07,625][__main__][INFO] - agents played in iteration 277 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 02:09:08,403][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:09:08,430][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:09:08,456][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:09:08,479][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:09:08,480][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 02:09:08,480][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 02:09:09,144][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 02:09:09,601][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 02:09:10,108][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 02:09:10,616][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 02:09:11,117][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 02:09:11,618][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 02:09:12,118][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 02:09:12,620][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 02:09:13,122][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 02:09:13,627][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 02:09:14,131][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 02:09:25,728][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 02:09:26,227][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 02:09:26,729][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 02:09:27,230][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 02:09:27,732][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 02:09:28,233][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 02:09:28,733][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 02:09:29,234][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 02:09:29,734][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 02:09:30,238][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 02:09:30,741][mllm.training.trainer_common][INFO] - 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Processing mini-batch 54 of 64 [2025-11-13 02:09:36,817][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 02:09:37,320][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 02:09:37,822][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 02:09:38,328][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 02:09:38,831][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 02:09:39,333][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 02:09:39,835][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 02:09:40,340][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 02:09:40,842][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 02:09:41,346][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10877 tokens. [2025-11-13 02:09:42,036][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.20%, Current % of VRAM taken: 59.65%, Block Peak % of device VRAM: 62.28%, ΔTime: 00:00:32 [2025-11-13 02:09:42,804][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 02:09:42,806][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 02:09:42,808][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 02:09:43,724][__main__][INFO] - Iteration 278 took 50s (28.95% Gen, 69.25% Train). Generation: 14s, Training: 35s. Estimated remaining time: 38h 20m 22s. Estimated total time: 42h 20m 24s. Time estimates for 10 more iterations: 8m 28s, 100 more iterations: 1h 24m 40s, 500 more iterations: 7h 3m 24s. [2025-11-13 02:09:43,727][__main__][INFO] - Starting iteration 278. [2025-11-13 02:09:44,207][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 27 and human policies 1. [2025-11-13 02:09:44,207][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 02:09:56,896][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 02:09:59,151][__main__][INFO] - Number of regex retries in iteration 278: 1 [2025-11-13 02:09:59,152][__main__][INFO] - agents played in iteration 278 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 02:09:59,975][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:10:00,002][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:10:00,028][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:10:00,051][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:10:00,051][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 02:10:00,053][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 02:10:00,713][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 02:10:01,169][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 02:10:01,687][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 02:10:02,189][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 02:10:02,690][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 02:10:03,192][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 02:10:03,694][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 02:10:04,199][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 02:10:04,699][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 02:10:05,202][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 02:10:05,707][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 02:10:17,289][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 02:10:17,791][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 02:10:18,294][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 02:10:18,796][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 02:10:19,296][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 02:10:19,794][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 02:10:20,292][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 02:10:20,792][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 02:10:21,300][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 02:10:21,800][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 02:10:22,303][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 02:10:22,804][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 02:10:23,304][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 02:10:23,804][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 02:10:24,304][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 02:10:24,804][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 02:10:25,307][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 02:10:25,808][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 02:10:26,308][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 02:10:26,812][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 02:10:27,315][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 02:10:27,819][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 02:10:28,321][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 02:10:28,825][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 02:10:29,329][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 02:10:29,834][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 02:10:30,335][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 02:10:30,836][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 02:10:31,340][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 02:10:31,845][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 02:10:32,349][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 02:10:32,852][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10875 tokens. [2025-11-13 02:10:33,591][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.21%, Current % of VRAM taken: 59.66%, Block Peak % of device VRAM: 62.41%, ΔTime: 00:00:32 [2025-11-13 02:10:34,374][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 02:10:34,376][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 02:10:34,377][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 02:10:35,375][__main__][INFO] - Iteration 279 took 51s (29.21% Gen, 68.84% Train). Generation: 14s, Training: 35s. Estimated remaining time: 38h 37m 31s. Estimated total time: 42h 38m 25s. Time estimates for 10 more iterations: 8m 31s, 100 more iterations: 1h 25m 16s, 500 more iterations: 7h 6m 24s. [2025-11-13 02:10:35,377][__main__][INFO] - Starting iteration 279. [2025-11-13 02:10:35,846][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 27 and human policies 1. [2025-11-13 02:10:35,846][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 02:10:39,960][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 02:10:49,461][__main__][INFO] - Number of regex retries in iteration 279: 1 [2025-11-13 02:10:49,462][__main__][INFO] - agents played in iteration 279 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 02:10:50,240][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:10:50,268][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:10:50,294][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:10:50,317][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:10:50,317][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 02:10:50,318][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 02:10:50,967][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 02:10:51,424][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 02:10:51,932][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 02:10:52,434][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 02:10:52,938][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 02:10:53,438][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 02:10:53,941][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 02:10:54,442][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 02:10:54,944][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 02:10:55,447][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 02:10:55,947][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 02:11:07,501][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 02:11:08,003][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 02:11:08,511][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 02:11:09,015][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 02:11:09,518][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 02:11:10,020][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 02:11:10,521][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 02:11:11,026][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 02:11:11,527][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 02:11:12,028][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 02:11:12,537][mllm.training.trainer_common][INFO] - 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Processing mini-batch 54 of 64 [2025-11-13 02:11:18,546][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 02:11:19,048][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 02:11:19,549][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 02:11:20,051][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 02:11:20,557][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 02:11:21,061][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 02:11:21,567][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 02:11:22,071][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 02:11:22,575][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 02:11:23,098][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10879 tokens. [2025-11-13 02:11:23,804][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.21%, Current % of VRAM taken: 59.66%, Block Peak % of device VRAM: 62.24%, ΔTime: 00:00:32 [2025-11-13 02:11:24,580][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 02:11:24,582][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 02:11:24,585][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 02:11:25,534][__main__][INFO] - Iteration 280 took 49s (27.40% Gen, 70.69% Train). Generation: 13s, Training: 35s. Estimated remaining time: 37h 22m 42s. Estimated total time: 41h 24m 26s. Time estimates for 10 more iterations: 8m 16s, 100 more iterations: 1h 22m 48s, 500 more iterations: 6h 54m 4s. [2025-11-13 02:11:25,536][__main__][INFO] - Starting iteration 280. [2025-11-13 02:11:26,013][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 27 and human policies 1. [2025-11-13 02:11:26,014][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 02:11:31,927][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 02:11:40,376][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 02:11:42,792][__main__][INFO] - Number of regex retries in iteration 280: 2 [2025-11-13 02:11:42,793][__main__][INFO] - agents played in iteration 280 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 02:11:43,636][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:11:43,663][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:11:43,690][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:11:43,713][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:11:43,713][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 02:11:43,714][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 02:11:44,382][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 02:11:44,839][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 02:11:45,345][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 02:11:45,848][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 02:11:46,352][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 02:11:46,856][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 02:11:47,356][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 02:11:47,856][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 02:11:48,367][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 02:11:48,870][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 02:11:49,378][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 02:12:00,964][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 02:12:01,469][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 02:12:01,974][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 02:12:02,479][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 02:12:02,983][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 02:12:03,486][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 02:12:03,990][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 02:12:04,494][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 02:12:04,996][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 02:12:05,502][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 02:12:06,003][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 02:12:06,520][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 02:12:07,019][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 02:12:07,520][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 02:12:08,018][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 02:12:08,518][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 02:12:09,019][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 02:12:09,518][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 02:12:10,015][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 02:12:10,513][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 02:12:11,012][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 02:12:11,511][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 02:12:12,010][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 02:12:12,509][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 02:12:13,026][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 02:12:13,525][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 02:12:14,025][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 02:12:14,525][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 02:12:15,024][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 02:12:15,527][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 02:12:16,031][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 02:12:16,534][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10873 tokens. [2025-11-13 02:12:17,269][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.23%, Current % of VRAM taken: 59.68%, Block Peak % of device VRAM: 62.31%, ΔTime: 00:00:32 [2025-11-13 02:12:18,055][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 02:12:18,056][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 02:12:18,058][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 02:12:19,801][__main__][INFO] - Iteration 281 took 53s (31.20% Gen, 65.56% Train). Generation: 16s, Training: 35s. Estimated remaining time: 40h 46m 45s. Estimated total time: 44h 49m 23s. Time estimates for 10 more iterations: 8m 57s, 100 more iterations: 1h 29m 38s, 500 more iterations: 7h 28m 13s. [2025-11-13 02:12:19,802][__main__][INFO] - Starting iteration 281. [2025-11-13 02:12:20,276][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 28 and human policies 1. [2025-11-13 02:12:20,276][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 02:12:34,180][__main__][INFO] - Number of regex retries in iteration 281: 0 [2025-11-13 02:12:34,180][__main__][INFO] - agents played in iteration 281 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 02:12:35,103][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:12:35,126][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:12:35,149][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:12:35,172][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:12:35,173][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 02:12:35,174][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 02:12:35,861][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 02:12:36,319][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 02:12:36,831][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 02:12:37,338][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 02:12:37,842][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 02:12:38,344][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 02:12:38,844][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 02:12:39,346][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 02:12:39,844][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 02:12:40,348][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 02:12:40,855][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 02:12:52,445][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 02:12:52,944][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 02:12:53,446][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 02:12:53,949][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 02:12:54,450][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 02:12:54,955][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 02:12:55,457][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 02:12:55,958][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 02:12:56,461][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 02:12:56,962][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 02:12:57,465][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 02:12:57,969][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 02:12:58,472][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 02:12:58,979][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 02:12:59,483][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 02:12:59,983][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 02:13:00,486][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 02:13:00,987][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 02:13:01,496][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 02:13:01,997][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 02:13:02,499][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 02:13:03,010][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 02:13:03,512][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 02:13:04,023][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 02:13:04,523][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 02:13:05,025][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 02:13:05,528][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 02:13:06,029][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 02:13:06,534][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 02:13:07,032][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 02:13:07,533][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 02:13:08,041][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10875 tokens. [2025-11-13 02:13:08,727][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.21%, Current % of VRAM taken: 59.66%, Block Peak % of device VRAM: 62.34%, ΔTime: 00:00:32 [2025-11-13 02:13:09,503][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 02:13:09,505][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 02:13:09,507][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 02:13:10,545][__main__][INFO] - Iteration 282 took 50s (27.66% Gen, 70.27% Train). Generation: 13s, Training: 35s. Estimated remaining time: 37h 50m 1s. Estimated total time: 41h 53m 30s. Time estimates for 10 more iterations: 8m 22s, 100 more iterations: 1h 23m 47s, 500 more iterations: 6h 58m 55s. [2025-11-13 02:13:10,547][__main__][INFO] - Starting iteration 282. [2025-11-13 02:13:11,021][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 28 and human policies 1. [2025-11-13 02:13:11,022][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 02:13:16,269][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 02:13:18,398][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 02:13:27,382][__main__][INFO] - Number of regex retries in iteration 282: 2 [2025-11-13 02:13:27,383][__main__][INFO] - agents played in iteration 282 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 02:13:28,184][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:13:28,206][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:13:28,229][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:13:28,251][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:13:28,251][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 02:13:28,252][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 02:13:28,974][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 02:13:29,433][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 02:13:29,940][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 02:13:30,450][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 02:13:30,957][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 02:13:31,460][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 02:13:31,963][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 02:13:32,466][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 02:13:32,969][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 02:13:33,469][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 02:13:33,971][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 02:13:34,474][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 02:13:34,975][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 02:13:35,477][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 02:13:35,978][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 02:13:36,479][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 02:13:36,980][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 02:13:37,481][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 02:13:37,982][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 02:13:38,484][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 02:13:38,985][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 02:13:39,485][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 02:13:39,987][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 02:13:40,487][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 02:13:40,990][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 02:13:41,489][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 02:13:41,992][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 02:13:42,495][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 02:13:42,997][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 02:13:43,500][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 02:13:44,000][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 02:13:44,501][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 02:13:45,001][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 02:13:45,502][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 02:13:46,006][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 02:13:46,504][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 02:13:47,004][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 02:13:47,506][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 02:13:48,005][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 02:13:48,506][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 02:13:49,009][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 02:13:49,515][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 02:13:50,019][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 02:13:50,523][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 02:13:51,023][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 02:13:51,525][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 02:13:52,027][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 02:13:52,529][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 02:13:53,046][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 02:13:53,553][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 02:13:54,056][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 02:13:54,560][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 02:13:55,063][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 02:13:55,567][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 02:13:56,067][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 02:13:56,565][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 02:13:57,067][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 02:13:57,566][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 02:13:58,064][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 02:13:58,564][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 02:13:59,065][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 02:13:59,569][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 02:14:00,071][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 02:14:00,572][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 02:14:01,073][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10878 tokens. [2025-11-13 02:14:01,750][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.19%, Current % of VRAM taken: 59.64%, Block Peak % of device VRAM: 62.28%, ΔTime: 00:00:32 [2025-11-13 02:14:02,517][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 02:14:02,518][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 02:14:02,520][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 02:14:03,482][__main__][INFO] - Iteration 283 took 52s (31.19% Gen, 66.98% Train). Generation: 16s, Training: 35s. Estimated remaining time: 39h 38m 44s. Estimated total time: 43h 43m 5s. Time estimates for 10 more iterations: 8m 44s, 100 more iterations: 1h 27m 26s, 500 more iterations: 7h 17m 10s. [2025-11-13 02:14:03,484][__main__][INFO] - Starting iteration 283. [2025-11-13 02:14:03,952][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 28 and human policies 1. [2025-11-13 02:14:03,953][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 02:14:08,009][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 02:14:17,382][__main__][INFO] - Number of regex retries in iteration 283: 1 [2025-11-13 02:14:17,383][__main__][INFO] - agents played in iteration 283 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 02:14:18,166][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:14:18,189][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:14:18,212][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:14:18,234][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:14:18,235][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 02:14:18,236][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 02:14:18,932][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 02:14:19,396][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 02:14:19,907][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 02:14:20,413][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 02:14:20,918][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 02:14:21,422][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 02:14:21,927][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 02:14:22,431][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 02:14:22,936][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 02:14:23,458][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 02:14:23,962][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 02:14:24,465][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 02:14:24,971][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 02:14:25,474][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 02:14:25,981][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 02:14:26,485][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 02:14:26,989][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 02:14:27,494][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 02:14:27,996][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 02:14:28,499][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 02:14:28,999][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 02:14:29,500][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 02:14:35,518][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 02:14:36,018][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 02:14:36,520][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 02:14:37,022][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 02:14:37,523][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 02:14:38,033][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 02:14:38,534][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 02:14:39,036][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 02:14:39,536][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 02:14:40,039][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 02:14:40,564][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 02:14:41,063][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 02:14:41,562][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 02:14:42,061][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 02:14:42,559][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 02:14:43,067][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 02:14:43,571][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 02:14:44,074][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 02:14:44,582][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 02:14:45,091][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 02:14:45,598][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 02:14:46,103][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 02:14:46,603][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 02:14:47,107][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 02:14:47,613][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 02:14:48,116][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 02:14:48,622][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 02:14:49,126][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 02:14:49,629][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 02:14:50,142][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 02:14:50,643][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 02:14:51,149][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10875 tokens. [2025-11-13 02:14:51,824][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.26%, Current % of VRAM taken: 59.71%, Block Peak % of device VRAM: 62.31%, ΔTime: 00:00:32 [2025-11-13 02:14:52,589][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 02:14:52,590][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 02:14:52,592][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 02:14:53,499][__main__][INFO] - Iteration 284 took 49s (27.10% Gen, 71.06% Train). Generation: 13s, Training: 35s. Estimated remaining time: 37h 12m 10s. Estimated total time: 41h 17m 21s. Time estimates for 10 more iterations: 8m 15s, 100 more iterations: 1h 22m 34s, 500 more iterations: 6h 52m 53s. [2025-11-13 02:14:53,501][__main__][INFO] - Starting iteration 284. [2025-11-13 02:14:53,970][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 28 and human policies 1. [2025-11-13 02:14:53,970][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 02:15:08,293][__main__][INFO] - Number of regex retries in iteration 284: 0 [2025-11-13 02:15:08,294][__main__][INFO] - agents played in iteration 284 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 02:15:09,067][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:15:09,094][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:15:09,120][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:15:09,143][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:15:09,143][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 02:15:09,144][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 02:15:09,801][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 02:15:10,258][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 02:15:10,762][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 02:15:11,263][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 02:15:11,763][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 02:15:12,279][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 02:15:12,782][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 02:15:13,287][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 02:15:13,792][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 02:15:14,303][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 02:15:14,810][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 02:15:15,314][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 02:15:15,818][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 02:15:16,321][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 02:15:16,826][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 02:15:17,330][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 02:15:17,835][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 02:15:18,338][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 02:15:18,841][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 02:15:19,348][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 02:15:19,851][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 02:15:20,358][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 02:15:20,860][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 02:15:21,377][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 02:15:21,878][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 02:15:22,385][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 02:15:22,893][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 02:15:23,395][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 02:15:23,900][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 02:15:24,401][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 02:15:24,902][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 02:15:25,409][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 02:15:25,911][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 02:15:26,412][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 02:15:26,914][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 02:15:27,416][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 02:15:27,918][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 02:15:28,421][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 02:15:28,927][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 02:15:29,432][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 02:15:29,933][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 02:15:30,431][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 02:15:30,930][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 02:15:31,428][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 02:15:31,930][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 02:15:32,428][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 02:15:32,925][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 02:15:33,421][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 02:15:33,919][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 02:15:34,419][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 02:15:34,919][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 02:15:35,420][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 02:15:35,922][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 02:15:36,419][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 02:15:36,919][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 02:15:37,418][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 02:15:37,919][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 02:15:38,427][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 02:15:38,929][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 02:15:39,432][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 02:15:39,937][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 02:15:40,438][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 02:15:40,941][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 02:15:41,447][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 02:15:41,949][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10877 tokens. [2025-11-13 02:15:42,678][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.23%, Current % of VRAM taken: 59.68%, Block Peak % of device VRAM: 62.31%, ΔTime: 00:00:32 [2025-11-13 02:15:43,428][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 02:15:43,430][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 02:15:43,432][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 02:15:44,331][__main__][INFO] - Iteration 285 took 50s (28.44% Gen, 69.77% Train). Generation: 14s, Training: 35s. Estimated remaining time: 37h 52m 1s. Estimated total time: 41h 58m 4s. Time estimates for 10 more iterations: 8m 23s, 100 more iterations: 1h 23m 56s, 500 more iterations: 6h 59m 40s. [2025-11-13 02:15:44,333][__main__][INFO] - Starting iteration 285. [2025-11-13 02:15:44,827][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 28 and human policies 1. [2025-11-13 02:15:44,828][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 02:15:49,559][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 02:15:51,421][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 02:15:59,996][__main__][INFO] - Number of regex retries in iteration 285: 2 [2025-11-13 02:15:59,997][__main__][INFO] - agents played in iteration 285 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 02:16:00,813][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:16:00,840][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:16:00,866][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:16:00,889][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:16:00,890][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 02:16:00,891][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 02:16:01,539][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 02:16:01,997][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 02:16:02,504][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 02:16:03,004][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 02:16:03,506][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 02:16:04,005][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 02:16:04,508][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 02:16:05,008][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 02:16:05,508][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 02:16:06,012][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 02:16:06,512][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 02:16:18,084][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 02:16:18,584][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 02:16:19,086][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 02:16:19,592][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 02:16:20,095][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 02:16:20,607][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 02:16:21,107][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 02:16:21,610][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 02:16:22,108][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 02:16:22,612][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 02:16:23,114][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 02:16:23,615][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 02:16:24,115][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 02:16:24,617][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 02:16:25,117][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 02:16:25,620][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 02:16:26,122][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 02:16:26,623][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 02:16:27,126][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 02:16:27,625][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 02:16:28,131][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 02:16:28,633][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 02:16:29,136][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 02:16:29,641][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 02:16:30,141][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 02:16:30,639][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 02:16:31,139][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 02:16:31,638][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 02:16:32,135][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 02:16:32,639][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 02:16:33,144][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 02:16:33,651][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10877 tokens. [2025-11-13 02:16:34,378][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.22%, Current % of VRAM taken: 59.68%, Block Peak % of device VRAM: 62.42%, ΔTime: 00:00:32 [2025-11-13 02:16:35,164][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 02:16:35,166][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 02:16:35,168][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 02:16:36,042][__main__][INFO] - Iteration 286 took 51s (29.62% Gen, 68.67% Train). Generation: 15s, Training: 35s. Estimated remaining time: 38h 33m 51s. Estimated total time: 42h 40m 45s. Time estimates for 10 more iterations: 8m 32s, 100 more iterations: 1h 25m 21s, 500 more iterations: 7h 6m 47s. [2025-11-13 02:16:36,044][__main__][INFO] - Starting iteration 286. [2025-11-13 02:16:36,512][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 28 and human policies 1. [2025-11-13 02:16:36,513][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 02:16:43,904][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 02:16:46,256][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 02:16:51,694][__main__][INFO] - Number of regex retries in iteration 286: 2 [2025-11-13 02:16:51,695][__main__][INFO] - agents played in iteration 286 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 02:16:52,516][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:16:52,539][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:16:52,561][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:16:52,583][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:16:52,584][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 02:16:52,585][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 02:16:53,273][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 02:16:53,731][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 02:16:54,234][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 02:16:54,735][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 02:16:55,236][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 02:16:55,736][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 02:16:56,237][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 02:16:56,736][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 02:16:57,235][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 02:16:57,741][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 02:16:58,242][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 02:16:58,744][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 02:16:59,244][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 02:16:59,744][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 02:17:00,245][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 02:17:00,746][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 02:17:01,250][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 02:17:01,754][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 02:17:02,257][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 02:17:02,762][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 02:17:03,266][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 02:17:03,776][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 02:17:09,838][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 02:17:10,341][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 02:17:10,845][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 02:17:11,349][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 02:17:11,850][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 02:17:12,351][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 02:17:12,853][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 02:17:13,356][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 02:17:13,857][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 02:17:14,364][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 02:17:14,865][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 02:17:15,367][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 02:17:15,869][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 02:17:16,371][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 02:17:16,881][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 02:17:17,382][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 02:17:17,881][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 02:17:18,393][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 02:17:18,893][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 02:17:19,406][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 02:17:19,906][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 02:17:20,406][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 02:17:20,913][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 02:17:21,415][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 02:17:21,915][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 02:17:22,419][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 02:17:22,921][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 02:17:23,429][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 02:17:23,933][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 02:17:24,435][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 02:17:24,934][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 02:17:25,435][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10878 tokens. [2025-11-13 02:17:26,161][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.21%, Current % of VRAM taken: 59.66%, Block Peak % of device VRAM: 62.30%, ΔTime: 00:00:32 [2025-11-13 02:17:26,909][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 02:17:26,911][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 02:17:26,913][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 02:17:28,002][__main__][INFO] - Iteration 287 took 51s (29.48% Gen, 68.40% Train). Generation: 15s, Training: 35s. Estimated remaining time: 38h 46m 45s. Estimated total time: 42h 54m 31s. Time estimates for 10 more iterations: 8m 34s, 100 more iterations: 1h 25m 49s, 500 more iterations: 7h 9m 5s. [2025-11-13 02:17:28,004][__main__][INFO] - Starting iteration 287. [2025-11-13 02:17:28,479][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 28 and human policies 1. [2025-11-13 02:17:28,480][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 02:17:33,177][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 02:17:42,890][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 02:17:43,781][__main__][INFO] - Number of regex retries in iteration 287: 2 [2025-11-13 02:17:43,782][__main__][INFO] - agents played in iteration 287 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 02:17:44,571][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:17:44,597][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:17:44,624][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:17:44,646][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:17:44,647][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 02:17:44,648][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 02:17:45,360][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 02:17:45,823][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 02:17:46,332][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 02:17:46,838][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 02:17:47,347][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 02:17:47,848][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 02:17:48,357][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 02:17:48,863][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 02:17:49,367][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 02:17:49,883][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 02:17:50,386][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 02:18:02,009][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 02:18:02,512][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 02:18:03,014][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 02:18:03,517][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 02:18:04,024][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 02:18:04,523][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 02:18:05,026][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 02:18:05,524][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 02:18:06,049][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 02:18:06,563][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 02:18:07,066][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 02:18:07,574][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 02:18:08,078][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 02:18:08,581][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 02:18:09,084][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 02:18:09,588][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 02:18:10,087][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 02:18:10,601][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 02:18:11,099][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 02:18:11,600][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 02:18:12,103][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 02:18:12,605][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 02:18:13,113][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 02:18:13,613][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 02:18:14,114][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 02:18:14,624][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 02:18:15,124][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 02:18:15,627][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 02:18:16,125][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 02:18:16,624][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 02:18:17,128][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 02:18:17,627][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10874 tokens. [2025-11-13 02:18:18,390][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.23%, Current % of VRAM taken: 59.68%, Block Peak % of device VRAM: 62.60%, ΔTime: 00:00:33 [2025-11-13 02:18:19,144][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 02:18:19,145][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 02:18:19,147][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 02:18:20,156][__main__][INFO] - Iteration 288 took 51s (29.61% Gen, 68.43% Train). Generation: 15s, Training: 35s. Estimated remaining time: 38h 55m 15s. Estimated total time: 43h 3m 53s. Time estimates for 10 more iterations: 8m 36s, 100 more iterations: 1h 26m 7s, 500 more iterations: 7h 10m 38s. [2025-11-13 02:18:20,158][__main__][INFO] - Starting iteration 288. [2025-11-13 02:18:20,655][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 28 and human policies 1. [2025-11-13 02:18:20,655][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 02:18:35,961][__main__][INFO] - Number of regex retries in iteration 288: 0 [2025-11-13 02:18:35,962][__main__][INFO] - agents played in iteration 288 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 02:18:36,746][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:18:36,774][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:18:36,800][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:18:36,826][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:18:36,826][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 02:18:36,827][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 02:18:37,493][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 02:18:37,948][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 02:18:38,459][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 02:18:38,962][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 02:18:39,463][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 02:18:39,964][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 02:18:40,468][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 02:18:40,973][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 02:18:41,477][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 02:18:41,982][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 02:18:42,486][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 02:18:54,101][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 02:18:54,604][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 02:18:55,109][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 02:18:55,613][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 02:18:56,117][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 02:18:56,622][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 02:18:57,126][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 02:18:57,630][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 02:18:58,130][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 02:18:58,633][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 02:18:59,141][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 02:18:59,643][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 02:19:00,144][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 02:19:00,641][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 02:19:01,141][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 02:19:01,648][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 02:19:02,151][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 02:19:02,657][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 02:19:03,162][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 02:19:03,663][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 02:19:04,164][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 02:19:04,664][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 02:19:05,164][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 02:19:05,668][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 02:19:06,168][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 02:19:06,669][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 02:19:07,173][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 02:19:07,675][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 02:19:08,176][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 02:19:08,676][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 02:19:09,179][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 02:19:09,686][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10880 tokens. [2025-11-13 02:19:10,381][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.20%, Current % of VRAM taken: 59.65%, Block Peak % of device VRAM: 62.29%, ΔTime: 00:00:32 [2025-11-13 02:19:11,147][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 02:19:11,148][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 02:19:11,150][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 02:19:12,147][__main__][INFO] - Iteration 289 took 51s (29.73% Gen, 68.34% Train). Generation: 15s, Training: 35s. Estimated remaining time: 38h 45m 7s. Estimated total time: 42h 54m 37s. Time estimates for 10 more iterations: 8m 34s, 100 more iterations: 1h 25m 49s, 500 more iterations: 7h 9m 6s. [2025-11-13 02:19:12,149][__main__][INFO] - Starting iteration 289. [2025-11-13 02:19:12,634][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 28 and human policies 1. [2025-11-13 02:19:12,635][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 02:19:27,819][__main__][INFO] - Number of regex retries in iteration 289: 0 [2025-11-13 02:19:27,820][__main__][INFO] - agents played in iteration 289 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 02:19:28,663][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:19:28,691][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:19:28,717][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:19:28,741][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:19:28,742][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 02:19:28,742][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 02:19:29,419][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 02:19:29,877][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 02:19:30,383][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 02:19:30,888][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 02:19:31,394][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 02:19:31,896][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 02:19:32,398][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 02:19:32,901][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 02:19:33,403][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 02:19:33,906][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 02:19:34,409][mllm.training.trainer_common][INFO] - 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Processing mini-batch 43 of 64 [2025-11-13 02:19:51,611][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 02:19:52,115][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 02:19:52,620][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 02:19:53,126][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 02:19:53,628][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 02:19:54,132][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 02:19:54,638][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 02:19:55,141][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 02:19:55,641][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 02:19:56,142][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 02:19:56,642][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 02:19:57,155][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 02:19:57,658][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 02:19:58,163][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 02:19:58,664][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 02:19:59,169][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 02:19:59,673][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 02:20:00,176][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 02:20:00,678][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 02:20:01,178][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 02:20:01,678][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10875 tokens. [2025-11-13 02:20:02,378][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.18%, Current % of VRAM taken: 59.64%, Block Peak % of device VRAM: 62.31%, ΔTime: 00:00:32 [2025-11-13 02:20:03,120][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 02:20:03,122][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 02:20:03,123][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 02:20:04,137][__main__][INFO] - Iteration 290 took 51s (29.48% Gen, 68.55% Train). Generation: 15s, Training: 35s. Estimated remaining time: 38h 44m 47s. Estimated total time: 42h 55m 10s. Time estimates for 10 more iterations: 8m 35s, 100 more iterations: 1h 25m 50s, 500 more iterations: 7h 9m 11s. [2025-11-13 02:20:04,139][__main__][INFO] - Starting iteration 290. [2025-11-13 02:20:04,631][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 28 and human policies 1. [2025-11-13 02:20:04,632][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 02:20:08,356][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 02:20:18,147][__main__][INFO] - Number of regex retries in iteration 290: 1 [2025-11-13 02:20:18,148][__main__][INFO] - agents played in iteration 290 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 02:20:18,929][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:20:18,957][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:20:18,983][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:20:19,006][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:20:19,007][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 02:20:19,007][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 02:20:19,673][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 02:20:20,131][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 02:20:20,649][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 02:20:21,151][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 02:20:21,657][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 02:20:22,160][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 02:20:22,663][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 02:20:23,165][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 02:20:23,666][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 02:20:24,167][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 02:20:24,672][mllm.training.trainer_common][INFO] - 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Processing mini-batch 43 of 64 [2025-11-13 02:20:41,779][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 02:20:42,282][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 02:20:42,789][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 02:20:43,295][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 02:20:43,798][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 02:20:44,301][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 02:20:44,803][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 02:20:45,306][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 02:20:45,809][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 02:20:46,313][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 02:20:46,815][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 02:20:47,317][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 02:20:47,818][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 02:20:48,331][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 02:20:48,832][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 02:20:49,334][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 02:20:49,841][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 02:20:50,343][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 02:20:50,855][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 02:20:51,357][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 02:20:51,858][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10879 tokens. [2025-11-13 02:20:52,572][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.24%, Current % of VRAM taken: 59.69%, Block Peak % of device VRAM: 62.28%, ΔTime: 00:00:32 [2025-11-13 02:20:53,340][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 02:20:53,342][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 02:20:53,343][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 02:20:55,262][__main__][INFO] - Iteration 291 took 50s (26.70% Gen, 69.51% Train). Generation: 13s, Training: 35s. Estimated remaining time: 38h 0m 20s. Estimated total time: 42h 11m 34s. Time estimates for 10 more iterations: 8m 26s, 100 more iterations: 1h 24m 23s, 500 more iterations: 7h 1m 55s. [2025-11-13 02:20:55,264][__main__][INFO] - Starting iteration 291. [2025-11-13 02:20:55,904][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 29 and human policies 1. [2025-11-13 02:20:55,906][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 02:21:09,416][__main__][INFO] - Number of regex retries in iteration 291: 0 [2025-11-13 02:21:09,416][__main__][INFO] - agents played in iteration 291 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 02:21:10,207][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:21:10,231][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:21:10,253][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:21:10,275][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:21:10,276][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 02:21:10,276][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 02:21:10,973][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 02:21:11,433][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 02:21:11,955][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 02:21:12,453][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 02:21:12,958][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 02:21:13,459][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 02:21:13,960][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 02:21:14,463][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 02:21:14,963][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 02:21:15,462][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 02:21:15,967][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 02:21:27,522][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 02:21:28,027][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 02:21:28,534][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 02:21:29,040][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 02:21:29,540][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 02:21:30,042][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 02:21:30,546][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 02:21:31,049][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 02:21:31,552][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 02:21:32,055][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 02:21:32,557][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 02:21:33,062][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 02:21:33,564][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 02:21:34,067][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 02:21:34,570][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 02:21:35,077][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 02:21:35,583][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 02:21:36,091][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 02:21:36,596][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 02:21:37,104][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 02:21:37,607][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 02:21:38,114][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 02:21:38,620][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 02:21:39,123][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 02:21:39,643][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 02:21:40,166][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 02:21:40,677][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 02:21:41,184][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 02:21:41,689][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 02:21:42,199][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 02:21:42,706][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 02:21:43,212][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10877 tokens. [2025-11-13 02:21:43,927][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.32%, Current % of VRAM taken: 59.77%, Block Peak % of device VRAM: 62.52%, ΔTime: 00:00:32 [2025-11-13 02:21:44,715][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 02:21:44,717][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 02:21:44,718][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 02:21:45,665][__main__][INFO] - Iteration 292 took 49s (27.15% Gen, 70.94% Train). Generation: 13s, Training: 35s. Estimated remaining time: 37h 16m 0s. Estimated total time: 41h 28m 4s. Time estimates for 10 more iterations: 8m 17s, 100 more iterations: 1h 22m 56s, 500 more iterations: 6h 54m 40s. [2025-11-13 02:21:45,668][__main__][INFO] - Starting iteration 292. [2025-11-13 02:21:46,149][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 29 and human policies 1. [2025-11-13 02:21:46,150][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 02:21:55,195][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 02:22:03,344][__main__][INFO] - Number of regex retries in iteration 292: 1 [2025-11-13 02:22:03,345][__main__][INFO] - agents played in iteration 292 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 02:22:04,151][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.62%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:22:04,178][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.62%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:22:04,200][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.62%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:22:04,223][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.62%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:22:04,223][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 02:22:04,224][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 02:22:04,918][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 02:22:05,379][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 02:22:05,886][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 02:22:06,389][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 02:22:06,890][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 02:22:07,393][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 02:22:07,895][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 02:22:08,398][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 02:22:08,900][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 02:22:09,404][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 02:22:09,906][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 02:22:21,506][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 02:22:22,010][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 02:22:22,514][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 02:22:23,016][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 02:22:23,519][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 02:22:24,024][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 02:22:24,526][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 02:22:25,029][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 02:22:25,535][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 02:22:26,039][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 02:22:26,549][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 02:22:27,053][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 02:22:27,557][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 02:22:28,066][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 02:22:28,570][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 02:22:29,082][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 02:22:29,587][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 02:22:30,094][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 02:22:30,602][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 02:22:31,108][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 02:22:31,612][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 02:22:32,116][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 02:22:32,619][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 02:22:33,125][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 02:22:33,628][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 02:22:34,130][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 02:22:34,635][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 02:22:35,139][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 02:22:35,642][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 02:22:36,144][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 02:22:36,646][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 02:22:37,149][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10876 tokens. [2025-11-13 02:22:37,854][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.23%, Current % of VRAM taken: 59.68%, Block Peak % of device VRAM: 62.31%, ΔTime: 00:00:32 [2025-11-13 02:22:38,619][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 02:22:38,620][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 02:22:38,622][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 02:22:39,604][__main__][INFO] - Iteration 293 took 53s (32.17% Gen, 65.99% Train). Generation: 17s, Training: 35s. Estimated remaining time: 40h 19m 50s. Estimated total time: 44h 32m 47s. Time estimates for 10 more iterations: 8m 54s, 100 more iterations: 1h 29m 5s, 500 more iterations: 7h 25m 27s. [2025-11-13 02:22:39,606][__main__][INFO] - Starting iteration 293. [2025-11-13 02:22:40,102][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 29 and human policies 1. [2025-11-13 02:22:40,102][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 02:22:48,593][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 02:22:56,777][__main__][INFO] - Number of regex retries in iteration 293: 1 [2025-11-13 02:22:56,778][__main__][INFO] - agents played in iteration 293 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 02:22:57,627][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:22:57,650][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:22:57,673][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:22:57,694][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:22:57,695][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 02:22:57,696][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 02:22:58,381][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 02:22:58,839][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 02:22:59,370][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 02:22:59,872][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 02:23:00,375][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 02:23:00,877][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 02:23:01,378][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 02:23:01,896][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 02:23:02,396][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 02:23:02,895][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 02:23:03,397][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 02:23:03,896][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 02:23:04,401][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 02:23:04,900][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 02:23:05,400][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 02:23:05,900][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 02:23:06,400][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 02:23:06,900][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 02:23:07,400][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 02:23:07,900][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 02:23:08,410][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 02:23:08,911][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 02:23:14,932][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 02:23:15,434][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 02:23:15,935][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 02:23:16,440][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 02:23:16,942][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 02:23:17,441][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 02:23:17,939][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 02:23:18,437][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 02:23:18,941][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 02:23:19,443][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 02:23:19,946][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 02:23:20,449][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 02:23:20,950][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 02:23:21,453][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 02:23:21,955][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 02:23:22,458][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 02:23:22,966][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 02:23:23,469][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 02:23:23,972][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 02:23:24,475][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 02:23:24,979][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 02:23:25,497][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 02:23:25,998][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 02:23:26,500][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 02:23:27,003][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 02:23:27,506][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 02:23:28,014][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 02:23:28,516][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 02:23:29,018][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 02:23:29,523][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 02:23:30,025][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 02:23:30,529][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10876 tokens. [2025-11-13 02:23:31,254][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.21%, Current % of VRAM taken: 59.66%, Block Peak % of device VRAM: 62.31%, ΔTime: 00:00:32 [2025-11-13 02:23:32,123][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 02:23:32,125][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 02:23:32,127][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 02:23:33,132][__main__][INFO] - Iteration 294 took 53s (31.45% Gen, 66.66% Train). Generation: 16s, Training: 35s. Estimated remaining time: 39h 57m 42s. Estimated total time: 44h 11m 33s. Time estimates for 10 more iterations: 8m 50s, 100 more iterations: 1h 28m 23s, 500 more iterations: 7h 21m 55s. [2025-11-13 02:23:33,135][__main__][INFO] - Starting iteration 294. [2025-11-13 02:23:33,619][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 29 and human policies 1. [2025-11-13 02:23:33,619][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 02:23:38,325][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 02:23:47,727][__main__][INFO] - Number of regex retries in iteration 294: 1 [2025-11-13 02:23:47,728][__main__][INFO] - agents played in iteration 294 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 02:23:48,526][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:23:48,551][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:23:48,576][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:23:48,598][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:23:48,599][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 02:23:48,600][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 02:23:49,307][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 02:23:49,766][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 02:23:50,276][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 02:23:50,780][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 02:23:51,284][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 02:23:51,800][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 02:23:52,302][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 02:23:52,805][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 02:23:53,310][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 02:23:53,813][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 02:23:54,322][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 02:24:05,883][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 02:24:06,398][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 02:24:06,899][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 02:24:07,399][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 02:24:07,900][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 02:24:08,400][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 02:24:08,915][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 02:24:09,416][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 02:24:09,923][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 02:24:10,425][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 02:24:10,927][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 02:24:11,431][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 02:24:11,935][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 02:24:12,438][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 02:24:12,944][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 02:24:13,449][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 02:24:13,953][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 02:24:14,457][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 02:24:14,960][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 02:24:15,463][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 02:24:15,963][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 02:24:16,465][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 02:24:16,966][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 02:24:17,467][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 02:24:17,974][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 02:24:18,478][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 02:24:18,982][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 02:24:19,499][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 02:24:20,001][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 02:24:20,514][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 02:24:21,016][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 02:24:21,521][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10879 tokens. [2025-11-13 02:24:22,272][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.23%, Current % of VRAM taken: 59.68%, Block Peak % of device VRAM: 62.34%, ΔTime: 00:00:32 [2025-11-13 02:24:23,018][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 02:24:23,020][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 02:24:23,023][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 02:24:23,905][__main__][INFO] - Iteration 295 took 50s (28.06% Gen, 70.19% Train). Generation: 14s, Training: 35s. Estimated remaining time: 37h 39m 38s. Estimated total time: 41h 54m 20s. Time estimates for 10 more iterations: 8m 22s, 100 more iterations: 1h 23m 48s, 500 more iterations: 6h 59m 3s. [2025-11-13 02:24:23,907][__main__][INFO] - Starting iteration 295. [2025-11-13 02:24:24,396][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 29 and human policies 1. [2025-11-13 02:24:24,396][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 02:24:34,290][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 1, 9 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 02:24:38,625][__main__][INFO] - Number of regex retries in iteration 295: 1 [2025-11-13 02:24:38,626][__main__][INFO] - agents played in iteration 295 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 02:24:39,395][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:24:39,423][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:24:39,450][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:24:39,473][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:24:39,474][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 02:24:39,475][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 02:24:40,132][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 02:24:40,588][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 02:24:41,095][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 02:24:41,607][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 02:24:42,110][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 02:24:42,621][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 02:24:43,120][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 02:24:43,619][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 02:24:44,120][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 02:24:44,621][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 02:24:45,126][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 02:24:45,632][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 02:24:46,136][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 02:24:46,642][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 02:24:47,148][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 02:24:47,648][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 02:24:48,154][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 02:24:48,659][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 02:24:49,167][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 02:24:49,671][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 02:24:50,175][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 02:24:50,679][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 02:24:51,183][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 02:24:51,692][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 02:24:52,198][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 02:24:52,703][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 02:24:53,222][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 02:24:53,723][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 02:24:54,224][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 02:24:54,724][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 02:24:55,224][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 02:24:55,739][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 02:24:56,239][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 02:24:56,742][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 02:24:57,243][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 02:24:57,744][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 02:24:58,256][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 02:24:58,759][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 02:24:59,262][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 02:24:59,766][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 02:25:00,268][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 02:25:00,770][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 02:25:01,269][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 02:25:01,770][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 02:25:02,273][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 02:25:02,774][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 02:25:03,273][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 02:25:03,775][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 02:25:04,287][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 02:25:04,790][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 02:25:05,297][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 02:25:05,801][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 02:25:06,315][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 02:25:06,820][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 02:25:07,334][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 02:25:07,839][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 02:25:08,346][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 02:25:08,853][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 02:25:09,361][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 02:25:09,866][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 02:25:10,369][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 02:25:10,874][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 02:25:11,382][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 02:25:11,885][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 02:25:12,388][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10876 tokens. [2025-11-13 02:25:13,106][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.23%, Current % of VRAM taken: 59.68%, Block Peak % of device VRAM: 62.31%, ΔTime: 00:00:32 [2025-11-13 02:25:13,866][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 02:25:13,868][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 02:25:13,871][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 02:25:14,957][__main__][INFO] - Iteration 296 took 50s (28.14% Gen, 69.71% Train). Generation: 14s, Training: 35s. Estimated remaining time: 37h 52m 35s. Estimated total time: 42h 8m 8s. Time estimates for 10 more iterations: 8m 25s, 100 more iterations: 1h 24m 16s, 500 more iterations: 7h 1m 21s. [2025-11-13 02:25:14,960][__main__][INFO] - Starting iteration 296. [2025-11-13 02:25:15,437][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 29 and human policies 1. [2025-11-13 02:25:15,438][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 02:25:30,276][__main__][INFO] - Number of regex retries in iteration 296: 0 [2025-11-13 02:25:30,276][__main__][INFO] - agents played in iteration 296 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 02:25:31,130][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:25:31,155][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:25:31,180][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:25:31,203][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:25:31,203][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 02:25:31,205][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 02:25:31,870][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 02:25:32,338][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 02:25:32,848][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 02:25:33,350][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 02:25:33,858][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 02:25:34,365][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 02:25:34,868][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 02:25:35,369][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 02:25:35,869][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 02:25:36,378][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 02:25:36,879][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 02:25:48,462][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 02:25:48,963][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 02:25:49,464][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 02:25:49,965][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 02:25:50,467][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 02:25:50,969][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 02:25:51,471][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 02:25:51,972][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 02:25:52,473][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 02:25:52,974][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 02:25:53,475][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 02:25:53,977][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 02:25:54,474][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 02:25:54,977][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 02:25:55,478][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 02:25:55,978][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 02:25:56,478][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 02:25:56,978][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 02:25:57,481][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 02:25:57,982][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 02:25:58,482][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 02:25:58,982][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 02:25:59,482][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 02:25:59,987][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 02:26:00,487][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 02:26:00,992][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 02:26:01,496][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 02:26:01,996][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 02:26:02,500][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 02:26:03,020][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 02:26:03,524][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 02:26:04,043][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10875 tokens. [2025-11-13 02:26:04,761][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.24%, Current % of VRAM taken: 59.69%, Block Peak % of device VRAM: 62.31%, ΔTime: 00:00:32 [2025-11-13 02:26:05,525][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 02:26:05,556][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 02:26:05,561][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 02:26:06,446][__main__][INFO] - Iteration 297 took 51s (29.09% Gen, 69.17% Train). Generation: 14s, Training: 35s. Estimated remaining time: 38h 14m 2s. Estimated total time: 42h 30m 26s. Time estimates for 10 more iterations: 8m 30s, 100 more iterations: 1h 25m 0s, 500 more iterations: 7h 5m 4s. [2025-11-13 02:26:06,448][__main__][INFO] - Starting iteration 297. [2025-11-13 02:26:06,955][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 29 and human policies 1. [2025-11-13 02:26:06,955][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 02:26:14,660][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 02:26:21,936][__main__][INFO] - Number of regex retries in iteration 297: 1 [2025-11-13 02:26:21,936][__main__][INFO] - agents played in iteration 297 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 02:26:22,753][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:26:22,784][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:26:22,811][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:26:22,834][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:26:22,835][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 02:26:22,836][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 02:26:23,517][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 02:26:23,984][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 02:26:24,490][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 02:26:24,995][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 02:26:25,500][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 02:26:26,007][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 02:26:26,509][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 02:26:27,014][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 02:26:27,518][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 02:26:28,040][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 02:26:28,542][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 02:26:29,052][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 02:26:29,552][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 02:26:30,053][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 02:26:30,564][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 02:26:31,070][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 02:26:31,571][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 02:26:32,076][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 02:26:32,578][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 02:26:33,082][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 02:26:33,582][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 02:26:34,083][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 02:26:34,588][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 02:26:35,089][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 02:26:35,588][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 02:26:36,091][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 02:26:36,595][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 02:26:37,098][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 02:26:37,601][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 02:26:38,104][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 02:26:38,606][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 02:26:39,108][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 02:26:39,610][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 02:26:40,121][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 02:26:40,624][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 02:26:41,151][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 02:26:41,656][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 02:26:42,157][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 02:26:42,666][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 02:26:43,167][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 02:26:43,670][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 02:26:44,173][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 02:26:44,674][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 02:26:45,176][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 02:26:45,678][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 02:26:46,179][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 02:26:46,681][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 02:26:47,181][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 02:26:47,683][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 02:26:48,183][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 02:26:48,684][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 02:26:49,185][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 02:26:49,688][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 02:26:50,189][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 02:26:50,690][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 02:26:51,189][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 02:26:51,692][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 02:26:52,194][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 02:26:52,695][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 02:26:53,197][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 02:26:53,698][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 02:26:54,199][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 02:26:54,699][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 02:26:55,200][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 02:26:55,704][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10878 tokens. [2025-11-13 02:26:56,407][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.19%, Current % of VRAM taken: 59.64%, Block Peak % of device VRAM: 62.25%, ΔTime: 00:00:32 [2025-11-13 02:26:57,186][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 02:26:57,188][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 02:26:57,190][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 02:26:58,138][__main__][INFO] - Iteration 298 took 51s (29.27% Gen, 68.88% Train). Generation: 14s, Training: 35s. Estimated remaining time: 38h 21m 57s. Estimated total time: 42h 39m 13s. Time estimates for 10 more iterations: 8m 31s, 100 more iterations: 1h 25m 18s, 500 more iterations: 7h 6m 32s. [2025-11-13 02:26:58,141][__main__][INFO] - Starting iteration 298. [2025-11-13 02:26:58,618][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 29 and human policies 1. [2025-11-13 02:26:58,619][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 02:27:13,079][__main__][INFO] - Number of regex retries in iteration 298: 0 [2025-11-13 02:27:13,079][__main__][INFO] - agents played in iteration 298 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 02:27:13,862][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:27:13,889][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:27:13,915][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:27:13,938][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:27:13,938][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 02:27:13,939][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 02:27:14,608][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 02:27:15,068][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 02:27:15,577][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 02:27:16,079][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 02:27:16,585][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 02:27:17,087][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 02:27:17,589][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 02:27:18,098][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 02:27:18,604][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 02:27:19,108][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 02:27:19,614][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 02:27:20,116][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 02:27:20,619][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 02:27:21,121][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 02:27:21,625][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 02:27:22,126][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 02:27:22,626][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 02:27:23,137][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 02:27:23,636][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 02:27:24,135][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 02:27:24,640][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 02:27:25,138][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 02:27:25,648][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 02:27:26,146][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 02:27:26,645][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 02:27:27,146][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 02:27:27,646][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 02:27:28,147][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 02:27:28,647][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 02:27:29,149][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 02:27:29,661][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 02:27:30,162][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 02:27:30,662][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 02:27:31,170][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 02:27:31,676][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 02:27:32,183][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 02:27:32,687][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 02:27:33,195][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 02:27:33,699][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 02:27:34,204][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 02:27:34,710][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 02:27:35,213][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 02:27:35,716][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 02:27:36,219][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 02:27:36,721][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 02:27:37,226][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 02:27:37,755][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 02:27:38,257][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 02:27:38,756][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 02:27:39,258][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 02:27:39,759][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 02:27:40,266][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 02:27:40,767][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 02:27:41,269][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 02:27:41,773][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 02:27:42,273][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 02:27:42,776][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 02:27:43,276][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 02:27:43,777][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 02:27:44,280][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 02:27:44,778][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 02:27:45,279][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 02:27:45,777][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 02:27:46,279][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 02:27:46,783][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10878 tokens. [2025-11-13 02:27:47,466][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.19%, Current % of VRAM taken: 59.64%, Block Peak % of device VRAM: 62.41%, ΔTime: 00:00:32 [2025-11-13 02:27:48,254][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 02:27:48,256][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 02:27:48,258][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 02:27:49,433][__main__][INFO] - Iteration 299 took 50s (28.46% Gen, 69.23% Train). Generation: 14s, Training: 35s. Estimated remaining time: 38h 2m 41s. Estimated total time: 42h 20m 48s. Time estimates for 10 more iterations: 8m 28s, 100 more iterations: 1h 24m 41s, 500 more iterations: 7h 3m 28s. [2025-11-13 02:27:49,435][__main__][INFO] - Starting iteration 299. [2025-11-13 02:27:49,915][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 29 and human policies 1. [2025-11-13 02:27:49,915][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 02:27:56,186][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 02:28:04,931][mllm.models.large_language_model_local][WARNING] - Response 提案：10 帽子, 10 书籍, 10 球 did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 02:28:07,196][__main__][INFO] - Number of regex retries in iteration 299: 2 [2025-11-13 02:28:07,197][__main__][INFO] - agents played in iteration 299 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 02:28:07,981][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:28:08,008][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:28:08,035][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:28:08,058][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:28:08,059][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 02:28:08,059][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 02:28:08,733][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 02:28:09,196][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 02:28:09,703][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 02:28:10,206][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 02:28:10,708][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 02:28:11,213][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 02:28:11,718][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 02:28:12,222][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 02:28:12,725][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 02:28:13,230][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 02:28:13,736][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 02:28:14,242][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 02:28:14,745][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 02:28:15,249][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 02:28:15,750][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 02:28:16,251][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 02:28:16,767][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 02:28:17,267][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 02:28:17,768][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 02:28:18,268][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 02:28:18,772][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 02:28:19,277][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 02:28:19,777][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 02:28:20,279][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 02:28:20,783][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 02:28:21,283][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 02:28:21,784][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 02:28:22,284][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 02:28:22,785][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 02:28:23,287][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 02:28:23,787][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 02:28:24,291][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 02:28:24,796][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 02:28:25,297][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 02:28:25,798][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 02:28:26,299][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 02:28:26,799][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 02:28:27,304][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 02:28:27,807][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 02:28:28,309][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 02:28:28,814][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 02:28:29,316][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 02:28:29,821][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 02:28:30,323][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 02:28:30,825][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 02:28:31,328][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 02:28:31,830][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 02:28:32,332][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 02:28:32,833][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 02:28:33,338][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 02:28:33,865][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 02:28:34,367][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 02:28:34,868][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 02:28:35,371][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 02:28:35,871][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 02:28:36,380][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 02:28:36,882][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 02:28:37,382][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 02:28:37,894][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 02:28:38,395][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 02:28:38,897][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 02:28:39,398][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 02:28:39,899][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 02:28:40,403][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 02:28:40,904][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10878 tokens. [2025-11-13 02:28:41,598][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.25%, Current % of VRAM taken: 59.70%, Block Peak % of device VRAM: 62.35%, ΔTime: 00:00:32 [2025-11-13 02:28:42,349][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 02:28:42,351][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 02:28:42,353][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 02:28:43,301][__main__][INFO] - Iteration 300 took 53s (32.37% Gen, 65.85% Train). Generation: 17s, Training: 35s. Estimated remaining time: 40h 10m 18s. Estimated total time: 44h 29m 20s. Time estimates for 10 more iterations: 8m 53s, 100 more iterations: 1h 28m 58s, 500 more iterations: 7h 24m 53s. [2025-11-13 02:28:43,304][__main__][INFO] - Starting iteration 300. [2025-11-13 02:28:43,792][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 29 and human policies 1. [2025-11-13 02:28:43,793][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 02:28:59,871][__main__][INFO] - Number of regex retries in iteration 300: 0 [2025-11-13 02:28:59,872][__main__][INFO] - agents played in iteration 300 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 02:29:00,669][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:29:00,703][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:29:00,731][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:29:00,756][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:29:00,757][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 02:29:00,758][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 02:29:01,452][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 02:29:01,907][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 02:29:02,410][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 02:29:02,913][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 02:29:03,414][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 02:29:03,912][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 02:29:04,411][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 02:29:04,909][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 02:29:05,413][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 02:29:05,919][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 02:29:06,422][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 02:29:06,928][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 02:29:07,433][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 02:29:07,934][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 02:29:08,437][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 02:29:08,938][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 02:29:09,448][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 02:29:09,951][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 02:29:10,459][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 02:29:10,969][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 02:29:11,470][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 02:29:11,978][mllm.training.trainer_common][INFO] - 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Processing mini-batch 43 of 64 [2025-11-13 02:29:23,589][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 02:29:24,095][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 02:29:24,598][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 02:29:25,103][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 02:29:25,606][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 02:29:26,114][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 02:29:26,623][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 02:29:27,128][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 02:29:27,629][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 02:29:28,132][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 02:29:28,635][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 02:29:29,140][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 02:29:29,642][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 02:29:30,143][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 02:29:30,652][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 02:29:31,154][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 02:29:31,658][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 02:29:32,158][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 02:29:32,658][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 02:29:33,160][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 02:29:33,659][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10876 tokens. [2025-11-13 02:29:34,355][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.23%, Current % of VRAM taken: 59.68%, Block Peak % of device VRAM: 62.51%, ΔTime: 00:00:32 [2025-11-13 02:29:35,107][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 02:29:35,108][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 02:29:35,110][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 02:29:36,916][__main__][INFO] - Iteration 301 took 53s (30.27% Gen, 66.33% Train). Generation: 16s, Training: 35s. Estimated remaining time: 39h 56m 20s. Estimated total time: 44h 16m 15s. Time estimates for 10 more iterations: 8m 51s, 100 more iterations: 1h 28m 32s, 500 more iterations: 7h 22m 42s. [2025-11-13 02:29:36,918][__main__][INFO] - Starting iteration 301. [2025-11-13 02:29:37,420][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 30 and human policies 1. [2025-11-13 02:29:37,421][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 02:29:53,437][__main__][INFO] - Number of regex retries in iteration 301: 0 [2025-11-13 02:29:53,437][__main__][INFO] - agents played in iteration 301 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 02:29:54,265][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:29:54,294][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:29:54,320][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:29:54,342][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:29:54,343][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 02:29:54,344][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 02:29:55,024][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 02:29:55,482][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 02:29:56,125][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 02:29:56,630][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 02:29:57,132][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 02:29:57,635][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 02:29:58,139][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 02:29:58,641][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 02:29:59,156][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 02:29:59,657][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 02:30:00,162][mllm.training.trainer_common][INFO] - 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Processing mini-batch 43 of 64 [2025-11-13 02:30:17,305][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 02:30:17,813][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 02:30:18,318][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 02:30:18,826][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 02:30:19,333][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 02:30:19,836][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 02:30:20,341][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 02:30:20,844][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 02:30:21,347][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 02:30:21,850][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 02:30:22,351][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 02:30:22,864][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 02:30:23,364][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 02:30:23,865][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 02:30:24,366][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 02:30:24,867][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 02:30:25,384][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 02:30:25,885][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 02:30:26,387][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 02:30:26,889][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 02:30:27,392][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10877 tokens. [2025-11-13 02:30:28,088][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.26%, Current % of VRAM taken: 59.72%, Block Peak % of device VRAM: 62.53%, ΔTime: 00:00:33 [2025-11-13 02:30:28,849][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 02:30:28,851][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 02:30:28,852][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 02:30:29,869][__main__][INFO] - Iteration 302 took 52s (30.54% Gen, 67.52% Train). Generation: 16s, Training: 35s. Estimated remaining time: 39h 21m 39s. Estimated total time: 43h 42m 27s. Time estimates for 10 more iterations: 8m 44s, 100 more iterations: 1h 27m 24s, 500 more iterations: 7h 17m 4s. [2025-11-13 02:30:29,871][__main__][INFO] - Starting iteration 302. [2025-11-13 02:30:30,350][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 30 and human policies 1. [2025-11-13 02:30:30,350][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 02:30:43,261][__main__][INFO] - Number of regex retries in iteration 302: 0 [2025-11-13 02:30:43,262][__main__][INFO] - agents played in iteration 302 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 02:30:44,198][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:30:44,221][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:30:44,244][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:30:44,266][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:30:44,266][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 02:30:44,267][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 02:30:44,973][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 02:30:45,431][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 02:30:45,942][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 02:30:46,446][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 02:30:46,950][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 02:30:47,454][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 02:30:47,957][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 02:30:48,460][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 02:30:48,964][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 02:30:49,465][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 02:30:49,965][mllm.training.trainer_common][INFO] - 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Processing mini-batch 54 of 64 [2025-11-13 02:31:12,628][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 02:31:13,131][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 02:31:13,635][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 02:31:14,138][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 02:31:14,642][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 02:31:15,149][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 02:31:15,650][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 02:31:16,151][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 02:31:16,661][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 02:31:17,160][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10877 tokens. [2025-11-13 02:31:17,849][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.14%, Current % of VRAM taken: 59.59%, Block Peak % of device VRAM: 62.31%, ΔTime: 00:00:32 [2025-11-13 02:31:18,605][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 02:31:18,608][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 02:31:18,610][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 02:31:19,630][__main__][INFO] - Iteration 303 took 49s (26.20% Gen, 71.73% Train). Generation: 12s, Training: 35s. Estimated remaining time: 36h 42m 24s. Estimated total time: 41h 4m 2s. Time estimates for 10 more iterations: 8m 12s, 100 more iterations: 1h 22m 8s, 500 more iterations: 6h 50m 40s. [2025-11-13 02:31:19,632][__main__][INFO] - Starting iteration 303. [2025-11-13 02:31:20,109][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 30 and human policies 1. [2025-11-13 02:31:20,110][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 02:31:34,701][__main__][INFO] - Number of regex retries in iteration 303: 0 [2025-11-13 02:31:34,702][__main__][INFO] - agents played in iteration 303 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 02:31:35,539][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:31:35,565][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:31:35,591][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:31:35,613][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:31:35,614][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 02:31:35,615][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 02:31:36,302][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 02:31:36,761][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 02:31:37,274][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 02:31:37,778][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 02:31:38,282][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 02:31:38,802][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 02:31:39,307][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 02:31:39,812][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 02:31:40,315][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 02:31:40,820][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 02:31:41,327][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 02:31:52,942][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 02:31:53,446][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 02:31:53,949][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 02:31:54,454][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 02:31:54,958][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 02:31:55,460][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 02:31:55,963][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 02:31:56,466][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 02:31:56,968][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 02:31:57,469][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 02:31:57,971][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 02:31:58,493][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 02:31:58,995][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 02:31:59,500][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 02:32:00,000][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 02:32:00,503][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 02:32:01,010][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 02:32:01,514][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 02:32:02,019][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 02:32:02,530][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 02:32:03,034][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 02:32:03,541][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 02:32:04,044][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 02:32:04,545][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 02:32:05,050][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 02:32:05,551][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 02:32:06,053][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 02:32:06,562][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 02:32:07,065][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 02:32:07,573][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 02:32:08,075][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 02:32:08,578][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10876 tokens. [2025-11-13 02:32:09,290][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.23%, Current % of VRAM taken: 59.68%, Block Peak % of device VRAM: 62.35%, ΔTime: 00:00:32 [2025-11-13 02:32:10,059][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 02:32:10,060][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 02:32:10,062][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 02:32:11,026][__main__][INFO] - Iteration 304 took 50s (28.66% Gen, 69.44% Train). Generation: 14s, Training: 35s. Estimated remaining time: 38h 3m 27s. Estimated total time: 42h 25m 57s. Time estimates for 10 more iterations: 8m 29s, 100 more iterations: 1h 24m 51s, 500 more iterations: 7h 4m 19s. [2025-11-13 02:32:11,029][__main__][INFO] - Starting iteration 304. [2025-11-13 02:32:11,517][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 30 and human policies 1. [2025-11-13 02:32:11,517][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 02:32:25,637][__main__][INFO] - Number of regex retries in iteration 304: 0 [2025-11-13 02:32:25,638][__main__][INFO] - agents played in iteration 304 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 02:32:26,419][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:32:26,446][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:32:26,472][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:32:26,494][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:32:26,495][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 02:32:26,496][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 02:32:27,199][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 02:32:27,668][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 02:32:28,175][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 02:32:28,686][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 02:32:29,189][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 02:32:29,690][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 02:32:30,207][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 02:32:30,713][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 02:32:31,218][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 02:32:31,721][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 02:32:32,225][mllm.training.trainer_common][INFO] - 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Processing mini-batch 43 of 64 [2025-11-13 02:32:49,369][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 02:32:49,869][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 02:32:50,372][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 02:32:50,872][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 02:32:51,385][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 02:32:51,884][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 02:32:52,385][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 02:32:52,886][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 02:32:53,388][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 02:32:53,888][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 02:32:54,389][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 02:32:54,893][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 02:32:55,411][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 02:32:55,915][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 02:32:56,420][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 02:32:56,922][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 02:32:57,424][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 02:32:57,932][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 02:32:58,438][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 02:32:58,942][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 02:32:59,445][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10880 tokens. [2025-11-13 02:33:00,163][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.24%, Current % of VRAM taken: 59.69%, Block Peak % of device VRAM: 62.31%, ΔTime: 00:00:32 [2025-11-13 02:33:00,919][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 02:33:00,925][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 02:33:00,927][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 02:33:01,891][__main__][INFO] - Iteration 305 took 50s (28.03% Gen, 70.05% Train). Generation: 14s, Training: 35s. Estimated remaining time: 37h 35m 23s. Estimated total time: 41h 58m 44s. Time estimates for 10 more iterations: 8m 23s, 100 more iterations: 1h 23m 57s, 500 more iterations: 6h 59m 47s. [2025-11-13 02:33:01,893][__main__][INFO] - Starting iteration 305. [2025-11-13 02:33:02,370][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 30 and human policies 1. [2025-11-13 02:33:02,371][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 02:33:16,989][__main__][INFO] - Number of regex retries in iteration 305: 0 [2025-11-13 02:33:16,990][__main__][INFO] - agents played in iteration 305 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 02:33:17,803][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:33:17,831][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:33:17,858][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:33:17,882][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:33:17,882][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 02:33:17,883][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 02:33:18,595][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 02:33:19,061][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 02:33:19,565][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 02:33:20,065][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 02:33:20,565][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 02:33:21,067][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 02:33:21,573][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 02:33:22,081][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 02:33:22,584][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 02:33:23,099][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 02:33:23,603][mllm.training.trainer_common][INFO] - 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Processing mini-batch 43 of 64 [2025-11-13 02:33:40,765][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 02:33:41,269][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 02:33:41,771][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 02:33:42,274][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 02:33:42,778][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 02:33:43,283][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 02:33:43,789][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 02:33:44,290][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 02:33:44,793][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 02:33:45,295][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 02:33:45,797][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 02:33:46,315][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 02:33:46,817][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 02:33:47,318][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 02:33:47,819][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 02:33:48,320][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 02:33:48,828][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 02:33:49,326][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 02:33:49,827][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 02:33:50,331][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 02:33:50,833][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10878 tokens. [2025-11-13 02:33:51,579][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.19%, Current % of VRAM taken: 59.64%, Block Peak % of device VRAM: 62.35%, ΔTime: 00:00:32 [2025-11-13 02:33:52,341][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 02:33:52,343][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 02:33:52,344][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 02:33:53,301][__main__][INFO] - Iteration 306 took 50s (28.70% Gen, 69.42% Train). Generation: 14s, Training: 35s. Estimated remaining time: 38h 2m 23s. Estimated total time: 42h 26m 34s. Time estimates for 10 more iterations: 8m 29s, 100 more iterations: 1h 24m 53s, 500 more iterations: 7h 4m 25s. [2025-11-13 02:33:53,304][__main__][INFO] - Starting iteration 306. [2025-11-13 02:33:53,792][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 30 and human policies 1. [2025-11-13 02:33:53,792][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 02:34:08,872][__main__][INFO] - Number of regex retries in iteration 306: 0 [2025-11-13 02:34:08,873][__main__][INFO] - agents played in iteration 306 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 02:34:09,696][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:34:09,719][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:34:09,754][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:34:09,777][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:34:09,777][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 02:34:09,778][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 02:34:10,465][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 02:34:10,923][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 02:34:11,427][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 02:34:11,926][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 02:34:12,432][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 02:34:12,933][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 02:34:13,435][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 02:34:13,943][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 02:34:14,444][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 02:34:14,947][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 02:34:15,450][mllm.training.trainer_common][INFO] - 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Processing mini-batch 54 of 64 [2025-11-13 02:34:38,164][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 02:34:38,669][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 02:34:39,172][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 02:34:39,676][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 02:34:40,179][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 02:34:40,680][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 02:34:41,181][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 02:34:41,682][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 02:34:42,183][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 02:34:42,685][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10877 tokens. [2025-11-13 02:34:43,367][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.24%, Current % of VRAM taken: 59.69%, Block Peak % of device VRAM: 62.41%, ΔTime: 00:00:32 [2025-11-13 02:34:44,134][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 02:34:44,136][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 02:34:44,138][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 02:34:45,069][__main__][INFO] - Iteration 307 took 51s (29.41% Gen, 68.77% Train). Generation: 15s, Training: 35s. Estimated remaining time: 38h 18m 49s. Estimated total time: 42h 43m 52s. Time estimates for 10 more iterations: 8m 32s, 100 more iterations: 1h 25m 27s, 500 more iterations: 7h 7m 18s. [2025-11-13 02:34:45,071][__main__][INFO] - Starting iteration 307. [2025-11-13 02:34:45,547][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 30 and human policies 1. [2025-11-13 02:34:45,548][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 02:34:50,490][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 02:35:00,721][__main__][INFO] - Number of regex retries in iteration 307: 1 [2025-11-13 02:35:00,722][__main__][INFO] - agents played in iteration 307 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 02:35:01,497][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:35:01,521][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:35:01,546][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:35:01,568][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:35:01,569][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 02:35:01,570][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 02:35:02,249][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 02:35:02,705][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 02:35:03,209][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 02:35:03,714][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 02:35:04,220][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 02:35:04,722][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 02:35:05,226][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 02:35:05,730][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 02:35:06,245][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 02:35:06,751][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 02:35:07,256][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 02:35:18,775][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 02:35:19,281][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 02:35:19,783][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 02:35:20,285][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 02:35:20,785][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 02:35:21,287][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 02:35:21,793][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 02:35:22,299][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 02:35:22,808][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 02:35:23,348][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 02:35:23,854][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 02:35:24,364][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 02:35:24,873][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 02:35:25,381][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 02:35:25,887][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 02:35:26,391][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 02:35:26,895][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 02:35:27,401][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 02:35:27,904][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 02:35:28,434][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 02:35:28,937][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 02:35:29,439][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 02:35:29,939][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 02:35:30,440][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 02:35:30,950][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 02:35:31,451][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 02:35:31,951][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 02:35:32,452][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 02:35:32,952][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 02:35:33,455][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 02:35:33,956][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 02:35:34,455][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10876 tokens. [2025-11-13 02:35:35,160][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.17%, Current % of VRAM taken: 59.63%, Block Peak % of device VRAM: 62.52%, ΔTime: 00:00:32 [2025-11-13 02:35:35,914][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 02:35:35,915][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 02:35:35,917][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 02:35:36,901][__main__][INFO] - Iteration 308 took 51s (29.55% Gen, 68.54% Train). Generation: 15s, Training: 35s. Estimated remaining time: 38h 21m 46s. Estimated total time: 42h 47m 41s. Time estimates for 10 more iterations: 8m 33s, 100 more iterations: 1h 25m 35s, 500 more iterations: 7h 7m 56s. [2025-11-13 02:35:36,903][__main__][INFO] - Starting iteration 308. [2025-11-13 02:35:37,377][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 30 and human policies 1. [2025-11-13 02:35:37,379][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 02:35:51,474][__main__][INFO] - Number of regex retries in iteration 308: 0 [2025-11-13 02:35:51,475][__main__][INFO] - agents played in iteration 308 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 02:35:52,254][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:35:52,279][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:35:52,303][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:35:52,325][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:35:52,326][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 02:35:52,326][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 02:35:52,995][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 02:35:53,449][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 02:35:53,958][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 02:35:54,461][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 02:35:54,963][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 02:35:55,463][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 02:35:55,963][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 02:35:56,461][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 02:35:56,964][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 02:35:57,471][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 02:35:57,978][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 02:35:58,485][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 02:35:58,985][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 02:35:59,486][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 02:35:59,990][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 02:36:00,494][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 02:36:01,001][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 02:36:01,510][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 02:36:02,012][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 02:36:02,513][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 02:36:03,015][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 02:36:03,517][mllm.training.trainer_common][INFO] - 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Processing mini-batch 43 of 64 [2025-11-13 02:36:15,151][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 02:36:15,664][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 02:36:16,166][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 02:36:16,670][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 02:36:17,175][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 02:36:17,678][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 02:36:18,188][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 02:36:18,692][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 02:36:19,195][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 02:36:19,703][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 02:36:20,207][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 02:36:20,710][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 02:36:21,213][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 02:36:21,716][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 02:36:22,233][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 02:36:22,735][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 02:36:23,238][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 02:36:23,739][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 02:36:24,240][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 02:36:24,758][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 02:36:25,260][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10880 tokens. [2025-11-13 02:36:25,980][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.23%, Current % of VRAM taken: 59.68%, Block Peak % of device VRAM: 62.38%, ΔTime: 00:00:32 [2025-11-13 02:36:26,736][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 02:36:26,738][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 02:36:26,739][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 02:36:27,732][__main__][INFO] - Iteration 309 took 50s (27.99% Gen, 70.03% Train). Generation: 14s, Training: 35s. Estimated remaining time: 37h 31m 1s. Estimated total time: 41h 57m 48s. Time estimates for 10 more iterations: 8m 23s, 100 more iterations: 1h 23m 55s, 500 more iterations: 6h 59m 38s. [2025-11-13 02:36:27,734][__main__][INFO] - Starting iteration 309. [2025-11-13 02:36:28,222][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 30 and human policies 1. [2025-11-13 02:36:28,223][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 02:36:43,901][__main__][INFO] - Number of regex retries in iteration 309: 0 [2025-11-13 02:36:43,901][__main__][INFO] - agents played in iteration 309 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 02:36:44,684][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:36:44,712][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:36:44,738][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:36:44,762][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:36:44,762][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 02:36:44,763][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 02:36:45,433][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 02:36:45,894][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 02:36:46,403][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 02:36:46,904][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 02:36:47,407][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 02:36:47,909][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 02:36:48,412][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 02:36:48,911][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 02:36:49,412][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 02:36:49,922][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 02:36:50,425][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 02:37:01,986][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 02:37:02,488][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 02:37:02,990][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 02:37:03,494][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 02:37:03,996][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 02:37:04,497][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 02:37:05,001][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 02:37:05,509][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 02:37:06,011][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 02:37:06,513][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 02:37:07,013][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 02:37:07,516][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 02:37:08,028][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 02:37:08,530][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 02:37:09,031][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 02:37:09,538][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 02:37:10,041][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 02:37:10,544][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 02:37:11,048][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 02:37:11,552][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 02:37:12,063][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 02:37:12,567][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 02:37:13,068][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 02:37:13,569][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 02:37:14,071][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 02:37:14,576][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 02:37:15,081][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 02:37:15,584][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 02:37:16,089][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 02:37:16,596][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 02:37:17,100][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 02:37:17,606][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10878 tokens. [2025-11-13 02:37:18,302][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.25%, Current % of VRAM taken: 59.71%, Block Peak % of device VRAM: 62.28%, ΔTime: 00:00:32 [2025-11-13 02:37:19,070][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 02:37:19,072][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 02:37:19,073][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 02:37:20,236][__main__][INFO] - Iteration 310 took 52s (30.14% Gen, 67.62% Train). Generation: 15s, Training: 35s. Estimated remaining time: 38h 53m 5s. Estimated total time: 43h 20m 44s. Time estimates for 10 more iterations: 8m 40s, 100 more iterations: 1h 26m 41s, 500 more iterations: 7h 13m 27s. [2025-11-13 02:37:20,238][__main__][INFO] - Starting iteration 310. [2025-11-13 02:37:20,725][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 30 and human policies 1. [2025-11-13 02:37:20,725][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 02:37:27,254][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 02:37:33,327][__main__][INFO] - Number of regex retries in iteration 310: 1 [2025-11-13 02:37:33,327][__main__][INFO] - agents played in iteration 310 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 02:37:34,185][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:37:34,208][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:37:34,232][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:37:34,254][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:37:34,255][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 02:37:34,256][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 02:37:34,924][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 02:37:35,524][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 02:37:36,136][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 02:37:36,639][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 02:37:37,162][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 02:37:37,663][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 02:37:38,163][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 02:37:38,666][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 02:37:39,166][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 02:37:39,669][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 02:37:40,168][mllm.training.trainer_common][INFO] - 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Processing mini-batch 54 of 64 [2025-11-13 02:38:02,750][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 02:38:03,266][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 02:38:03,768][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 02:38:04,275][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 02:38:04,781][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 02:38:05,284][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 02:38:05,787][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 02:38:06,290][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 02:38:06,792][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 02:38:07,302][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10877 tokens. [2025-11-13 02:38:08,044][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.20%, Current % of VRAM taken: 59.66%, Block Peak % of device VRAM: 62.32%, ΔTime: 00:00:33 [2025-11-13 02:38:08,796][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 02:38:08,797][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 02:38:08,800][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 02:38:10,495][__main__][INFO] - Iteration 311 took 49s (25.32% Gen, 71.27% Train). Generation: 12s, Training: 35s. Estimated remaining time: 37h 0m 5s. Estimated total time: 41h 28m 34s. Time estimates for 10 more iterations: 8m 17s, 100 more iterations: 1h 22m 57s, 500 more iterations: 6h 54m 45s. [2025-11-13 02:38:10,498][__main__][INFO] - Starting iteration 311. [2025-11-13 02:38:10,975][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 31 and human policies 1. [2025-11-13 02:38:10,975][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 02:38:15,713][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 02:38:19,201][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 02:38:25,801][__main__][INFO] - Number of regex retries in iteration 311: 2 [2025-11-13 02:38:25,802][__main__][INFO] - agents played in iteration 311 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 02:38:26,597][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:38:26,620][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:38:26,643][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:38:26,668][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:38:26,669][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 02:38:26,670][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 02:38:27,375][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 02:38:27,833][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 02:38:28,342][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 02:38:28,845][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 02:38:29,347][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 02:38:29,846][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 02:38:30,348][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 02:38:30,860][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 02:38:31,361][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 02:38:31,864][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 02:38:32,374][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 02:38:32,876][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 02:38:33,388][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 02:38:33,893][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 02:38:34,394][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 02:38:34,903][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 02:38:35,404][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 02:38:35,907][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 02:38:36,408][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 02:38:36,909][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 02:38:37,411][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 02:38:37,912][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 02:38:43,941][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 02:38:44,440][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 02:38:44,941][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 02:38:45,443][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 02:38:45,942][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 02:38:46,442][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 02:38:46,942][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 02:38:47,442][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 02:38:47,942][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 02:38:48,442][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 02:38:48,943][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 02:38:49,447][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 02:38:49,948][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 02:38:50,448][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 02:38:50,946][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 02:38:51,446][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 02:38:51,951][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 02:38:52,450][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 02:38:52,950][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 02:38:53,450][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 02:38:53,949][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 02:38:54,448][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 02:38:54,951][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 02:38:55,454][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 02:38:55,961][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 02:38:56,463][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 02:38:56,966][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 02:38:57,469][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 02:38:57,972][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 02:38:58,492][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 02:38:58,996][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 02:38:59,503][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10878 tokens. [2025-11-13 02:39:00,268][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.27%, Current % of VRAM taken: 59.72%, Block Peak % of device VRAM: 62.31%, ΔTime: 00:00:32 [2025-11-13 02:39:01,053][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 02:39:01,054][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 02:39:01,056][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 02:39:01,998][__main__][INFO] - Iteration 312 took 51s (29.05% Gen, 69.09% Train). Generation: 14s, Training: 35s. Estimated remaining time: 38h 1m 52s. Estimated total time: 42h 31m 13s. Time estimates for 10 more iterations: 8m 30s, 100 more iterations: 1h 25m 2s, 500 more iterations: 7h 5m 12s. [2025-11-13 02:39:02,001][__main__][INFO] - Starting iteration 312. [2025-11-13 02:39:02,476][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 31 and human policies 1. [2025-11-13 02:39:02,477][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 02:39:06,654][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 02:39:06,737][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 02:39:06,816][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 02:39:16,993][__main__][INFO] - Number of regex retries in iteration 312: 3 [2025-11-13 02:39:16,994][__main__][INFO] - agents played in iteration 312 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 02:39:17,791][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:39:17,813][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:39:17,836][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:39:17,860][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:39:17,861][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 02:39:17,861][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 02:39:18,595][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 02:39:19,054][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 02:39:19,567][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 02:39:20,070][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 02:39:20,575][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 02:39:21,084][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 02:39:21,588][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 02:39:22,091][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 02:39:22,595][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 02:39:23,096][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 02:39:23,605][mllm.training.trainer_common][INFO] - 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Processing mini-batch 21 of 64 [2025-11-13 02:39:29,620][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 02:39:30,120][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 02:39:30,617][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 02:39:31,118][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 02:39:31,621][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 02:39:32,122][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 02:39:32,623][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 02:39:33,124][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 02:39:33,627][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 02:39:34,129][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 02:39:34,629][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 02:39:35,127][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 02:39:35,630][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 02:39:36,129][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 02:39:36,634][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 02:39:37,136][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 02:39:37,637][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 02:39:38,138][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 02:39:38,638][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 02:39:39,139][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 02:39:39,639][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 02:39:40,140][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 02:39:40,641][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 02:39:41,141][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 02:39:41,642][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 02:39:42,142][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 02:39:42,643][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 02:39:43,142][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 02:39:43,643][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 02:39:44,142][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 02:39:44,643][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 02:39:45,145][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 02:39:45,645][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 02:39:46,145][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 02:39:46,647][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 02:39:47,157][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 02:39:47,661][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 02:39:48,167][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 02:39:48,673][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 02:39:49,177][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 02:39:49,692][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 02:39:50,196][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 02:39:50,699][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10864 tokens. [2025-11-13 02:39:51,440][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.26%, Current % of VRAM taken: 59.71%, Block Peak % of device VRAM: 62.32%, ΔTime: 00:00:32 [2025-11-13 02:39:52,212][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 02:39:52,213][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 02:39:52,215][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 02:39:53,081][__main__][INFO] - Iteration 313 took 50s (28.69% Gen, 69.60% Train). Generation: 14s, Training: 35s. Estimated remaining time: 37h 40m 4s. Estimated total time: 42h 10m 16s. Time estimates for 10 more iterations: 8m 26s, 100 more iterations: 1h 24m 20s, 500 more iterations: 7h 1m 42s. [2025-11-13 02:39:53,083][__main__][INFO] - Starting iteration 313. [2025-11-13 02:39:53,555][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 31 and human policies 1. [2025-11-13 02:39:53,556][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 02:39:59,654][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 02:40:00,375][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 02:40:08,176][__main__][INFO] - Number of regex retries in iteration 313: 2 [2025-11-13 02:40:08,177][__main__][INFO] - agents played in iteration 313 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 02:40:08,962][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:40:08,986][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:40:09,010][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:40:09,032][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:40:09,033][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 02:40:09,034][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 02:40:09,738][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 02:40:10,196][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 02:40:10,711][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 02:40:11,214][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 02:40:11,715][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 02:40:12,217][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 02:40:12,725][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 02:40:13,230][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 02:40:13,733][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 02:40:14,239][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 02:40:14,745][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 02:40:26,334][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 02:40:26,834][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 02:40:27,337][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 02:40:27,840][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 02:40:28,343][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 02:40:28,847][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 02:40:29,353][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 02:40:29,856][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 02:40:30,359][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 02:40:30,862][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 02:40:31,363][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 02:40:31,867][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 02:40:32,369][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 02:40:32,873][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 02:40:33,389][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 02:40:33,889][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 02:40:34,401][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 02:40:34,903][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 02:40:35,404][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 02:40:35,914][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 02:40:36,414][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 02:40:36,915][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 02:40:37,420][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 02:40:37,921][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 02:40:38,425][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 02:40:38,924][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 02:40:39,425][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 02:40:39,931][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 02:40:40,437][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 02:40:40,942][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 02:40:41,445][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 02:40:41,946][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10878 tokens. [2025-11-13 02:40:42,692][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.21%, Current % of VRAM taken: 59.66%, Block Peak % of device VRAM: 62.24%, ΔTime: 00:00:32 [2025-11-13 02:40:43,450][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 02:40:43,452][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 02:40:43,454][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 02:40:44,529][__main__][INFO] - Iteration 314 took 50s (28.68% Gen, 69.21% Train). Generation: 14s, Training: 35s. Estimated remaining time: 37h 57m 41s. Estimated total time: 42h 28m 44s. Time estimates for 10 more iterations: 8m 29s, 100 more iterations: 1h 24m 57s, 500 more iterations: 7h 4m 47s. [2025-11-13 02:40:44,531][__main__][INFO] - Starting iteration 314. [2025-11-13 02:40:45,007][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 31 and human policies 1. [2025-11-13 02:40:45,007][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 02:41:00,779][__main__][INFO] - Number of regex retries in iteration 314: 0 [2025-11-13 02:41:00,780][__main__][INFO] - agents played in iteration 314 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 02:41:01,594][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:41:01,624][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:41:01,651][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:41:01,675][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:41:01,675][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 02:41:01,676][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 02:41:02,363][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 02:41:02,818][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 02:41:03,324][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 02:41:03,824][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 02:41:04,323][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 02:41:04,829][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 02:41:05,334][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 02:41:05,836][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 02:41:06,342][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 02:41:06,847][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 02:41:07,349][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 02:41:07,852][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 02:41:08,357][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 02:41:08,862][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 02:41:09,367][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 02:41:09,870][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 02:41:10,386][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 02:41:10,892][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 02:41:11,406][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 02:41:11,909][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 02:41:12,412][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 02:41:12,922][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 02:41:13,424][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 02:41:13,926][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 02:41:14,425][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 02:41:14,925][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 02:41:15,426][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 02:41:15,927][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 02:41:16,428][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 02:41:16,929][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 02:41:17,430][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 02:41:17,931][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 02:41:18,432][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 02:41:18,936][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 02:41:19,439][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 02:41:19,940][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 02:41:20,443][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 02:41:20,944][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 02:41:21,445][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 02:41:21,947][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 02:41:22,448][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 02:41:22,970][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 02:41:23,489][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 02:41:23,990][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 02:41:24,502][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 02:41:25,006][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 02:41:25,509][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 02:41:26,024][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 02:41:26,526][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 02:41:27,027][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 02:41:27,527][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 02:41:28,031][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 02:41:28,537][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 02:41:29,041][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 02:41:29,542][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 02:41:30,043][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 02:41:30,543][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 02:41:31,045][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 02:41:31,546][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 02:41:32,046][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 02:41:32,546][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 02:41:33,045][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 02:41:33,544][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 02:41:34,046][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 02:41:34,548][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10877 tokens. [2025-11-13 02:41:35,281][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.26%, Current % of VRAM taken: 59.71%, Block Peak % of device VRAM: 62.34%, ΔTime: 00:00:32 [2025-11-13 02:41:36,042][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 02:41:36,043][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 02:41:36,045][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 02:41:36,987][__main__][INFO] - Iteration 315 took 51s (30.34% Gen, 67.84% Train). Generation: 15s, Training: 35s. Estimated remaining time: 38h 47m 7s. Estimated total time: 43h 19m 2s. Time estimates for 10 more iterations: 8m 39s, 100 more iterations: 1h 26m 38s, 500 more iterations: 7h 13m 10s. [2025-11-13 02:41:36,989][__main__][INFO] - Starting iteration 315. [2025-11-13 02:41:37,468][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 31 and human policies 1. [2025-11-13 02:41:37,469][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 02:41:48,541][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 02:41:49,386][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 02:41:54,587][__main__][INFO] - Number of regex retries in iteration 315: 2 [2025-11-13 02:41:54,588][__main__][INFO] - agents played in iteration 315 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 02:41:55,476][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:41:55,504][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:41:55,530][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:41:55,553][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:41:55,554][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 02:41:55,555][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 02:41:56,280][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 02:41:56,742][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 02:41:57,251][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 02:41:57,756][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 02:41:58,258][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 02:41:58,762][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 02:41:59,264][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 02:41:59,766][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 02:42:00,272][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 02:42:00,775][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 02:42:01,284][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 02:42:12,882][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 02:42:13,386][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 02:42:13,890][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 02:42:14,392][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 02:42:14,894][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 02:42:15,397][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 02:42:15,901][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 02:42:16,404][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 02:42:16,905][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 02:42:17,408][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 02:42:17,910][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 02:42:18,425][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 02:42:18,927][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 02:42:19,428][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 02:42:19,932][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 02:42:20,433][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 02:42:20,943][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 02:42:21,444][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 02:42:21,946][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 02:42:22,454][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 02:42:22,953][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 02:42:23,456][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 02:42:23,963][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 02:42:24,468][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 02:42:24,971][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 02:42:25,471][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 02:42:25,970][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 02:42:26,471][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 02:42:26,970][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 02:42:27,469][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 02:42:27,968][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 02:42:28,468][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10879 tokens. [2025-11-13 02:42:29,163][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.21%, Current % of VRAM taken: 59.67%, Block Peak % of device VRAM: 62.35%, ΔTime: 00:00:32 [2025-11-13 02:42:29,932][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 02:42:29,933][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 02:42:29,935][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 02:42:30,889][__main__][INFO] - Iteration 316 took 53s (32.04% Gen, 66.17% Train). Generation: 17s, Training: 35s. Estimated remaining time: 39h 58m 15s. Estimated total time: 44h 31m 4s. Time estimates for 10 more iterations: 8m 54s, 100 more iterations: 1h 29m 2s, 500 more iterations: 7h 25m 10s. [2025-11-13 02:42:30,891][__main__][INFO] - Starting iteration 316. [2025-11-13 02:42:31,385][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 31 and human policies 1. [2025-11-13 02:42:31,385][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 02:42:45,340][__main__][INFO] - Number of regex retries in iteration 316: 0 [2025-11-13 02:42:45,340][__main__][INFO] - agents played in iteration 316 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 02:42:46,306][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:42:46,329][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:42:46,352][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:42:46,374][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:42:46,374][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 02:42:46,375][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 02:42:47,081][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 02:42:47,539][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 02:42:48,044][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 02:42:48,551][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 02:42:49,053][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 02:42:49,554][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 02:42:50,058][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 02:42:50,560][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 02:42:51,061][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 02:42:51,558][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 02:42:52,062][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 02:42:52,564][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 02:42:53,064][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 02:42:53,565][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 02:42:54,065][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 02:42:54,564][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 02:42:55,068][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 02:42:55,571][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 02:42:56,072][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 02:42:56,578][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 02:42:57,083][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 02:42:57,585][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 02:43:03,648][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 02:43:04,151][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 02:43:04,653][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 02:43:05,155][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 02:43:05,663][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 02:43:06,163][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 02:43:06,664][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 02:43:07,166][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 02:43:07,665][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 02:43:08,165][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 02:43:08,665][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 02:43:09,165][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 02:43:09,668][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 02:43:10,168][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 02:43:10,669][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 02:43:11,169][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 02:43:11,670][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 02:43:12,178][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 02:43:12,681][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 02:43:13,183][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 02:43:13,688][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 02:43:14,190][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 02:43:14,690][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 02:43:15,192][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 02:43:15,692][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 02:43:16,194][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 02:43:16,694][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 02:43:17,193][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 02:43:17,694][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 02:43:18,196][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 02:43:18,701][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 02:43:19,208][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10879 tokens. [2025-11-13 02:43:19,923][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.17%, Current % of VRAM taken: 59.62%, Block Peak % of device VRAM: 62.31%, ΔTime: 00:00:32 [2025-11-13 02:43:20,702][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 02:43:20,704][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 02:43:20,705][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 02:43:21,672][__main__][INFO] - Iteration 317 took 50s (27.75% Gen, 70.32% Train). Generation: 13s, Training: 35s. Estimated remaining time: 37h 20m 44s. Estimated total time: 41h 54m 24s. Time estimates for 10 more iterations: 8m 22s, 100 more iterations: 1h 23m 48s, 500 more iterations: 6h 59m 4s. [2025-11-13 02:43:21,674][__main__][INFO] - Starting iteration 317. [2025-11-13 02:43:22,163][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 31 and human policies 1. [2025-11-13 02:43:22,163][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 02:43:27,066][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 02:43:35,470][mllm.models.large_language_model_local][WARNING] - Response 提案：10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 02:43:37,694][__main__][INFO] - Number of regex retries in iteration 317: 2 [2025-11-13 02:43:37,695][__main__][INFO] - agents played in iteration 317 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 02:43:38,534][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.54%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:43:38,556][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.54%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:43:38,593][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.54%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:43:38,616][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.54%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:43:38,617][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 02:43:38,617][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 02:43:39,313][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 02:43:39,773][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 02:43:40,289][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 02:43:40,791][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 02:43:41,296][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 02:43:41,798][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 02:43:42,303][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 02:43:42,805][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 02:43:43,312][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 02:43:43,825][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 02:43:44,333][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 02:43:44,835][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 02:43:45,354][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 02:43:45,855][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 02:43:46,361][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 02:43:46,861][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 02:43:47,364][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 02:43:47,867][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 02:43:48,365][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 02:43:48,865][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 02:43:49,366][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 02:43:49,866][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 02:43:50,367][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 02:43:50,865][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 02:43:51,368][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 02:43:51,875][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 02:43:52,376][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 02:43:52,881][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 02:43:53,384][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 02:43:53,887][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 02:43:54,395][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 02:43:54,902][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 02:43:55,407][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 02:43:55,919][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 02:43:56,418][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 02:43:56,924][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 02:43:57,431][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 02:43:57,936][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 02:43:58,450][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 02:43:58,953][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 02:43:59,453][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 02:43:59,955][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 02:44:00,455][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 02:44:00,963][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 02:44:01,468][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 02:44:01,968][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 02:44:02,473][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 02:44:02,977][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 02:44:03,478][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 02:44:03,980][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 02:44:04,482][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 02:44:04,984][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 02:44:05,486][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 02:44:05,989][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 02:44:06,489][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 02:44:06,992][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 02:44:07,497][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 02:44:07,999][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 02:44:08,501][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 02:44:09,003][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 02:44:09,505][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 02:44:10,006][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 02:44:10,507][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 02:44:11,012][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 02:44:11,516][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10878 tokens. [2025-11-13 02:44:12,236][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.19%, Current % of VRAM taken: 59.64%, Block Peak % of device VRAM: 62.28%, ΔTime: 00:00:32 [2025-11-13 02:44:13,026][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 02:44:13,028][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 02:44:13,030][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 02:44:14,271][__main__][INFO] - Iteration 318 took 52s (29.81% Gen, 67.81% Train). Generation: 15s, Training: 35s. Estimated remaining time: 38h 50m 55s. Estimated total time: 43h 25m 28s. Time estimates for 10 more iterations: 8m 41s, 100 more iterations: 1h 26m 50s, 500 more iterations: 7h 14m 14s. [2025-11-13 02:44:14,273][__main__][INFO] - Starting iteration 318. [2025-11-13 02:44:14,761][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 31 and human policies 1. [2025-11-13 02:44:14,762][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 02:44:30,720][__main__][INFO] - Number of regex retries in iteration 318: 0 [2025-11-13 02:44:30,720][__main__][INFO] - agents played in iteration 318 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 02:44:31,526][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:44:31,554][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:44:31,580][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:44:31,603][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:44:31,604][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 02:44:31,605][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 02:44:32,325][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 02:44:32,782][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 02:44:33,291][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 02:44:33,793][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 02:44:34,293][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 02:44:34,795][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 02:44:35,301][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 02:44:35,805][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 02:44:36,314][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 02:44:36,823][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 02:44:37,328][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 02:44:37,845][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 02:44:38,350][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 02:44:38,861][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 02:44:39,362][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 02:44:39,864][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 02:44:40,367][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 02:44:40,867][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 02:44:41,367][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 02:44:41,865][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 02:44:42,366][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 02:44:42,879][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 02:44:43,379][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 02:44:43,879][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 02:44:44,383][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 02:44:44,884][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 02:44:45,385][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 02:44:45,884][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 02:44:46,388][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 02:44:46,891][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 02:44:47,394][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 02:44:47,896][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 02:44:48,403][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 02:44:48,909][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 02:44:49,418][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 02:44:49,941][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 02:44:50,447][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 02:44:50,952][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 02:44:51,461][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 02:44:51,968][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 02:44:52,470][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 02:44:52,970][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 02:44:53,484][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 02:44:53,984][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 02:44:54,486][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 02:44:54,987][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 02:44:55,487][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 02:44:55,989][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 02:44:56,492][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 02:44:56,994][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 02:44:57,497][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 02:44:57,996][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 02:44:58,498][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 02:44:59,000][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 02:44:59,502][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 02:45:00,001][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 02:45:00,502][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 02:45:01,003][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 02:45:01,507][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 02:45:02,011][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 02:45:02,513][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 02:45:03,014][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 02:45:03,516][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 02:45:04,018][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 02:45:04,519][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10879 tokens. [2025-11-13 02:45:05,276][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.28%, Current % of VRAM taken: 59.73%, Block Peak % of device VRAM: 62.51%, ΔTime: 00:00:32 [2025-11-13 02:45:06,046][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 02:45:06,048][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 02:45:06,050][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 02:45:07,124][__main__][INFO] - Iteration 319 took 52s (30.48% Gen, 67.47% Train). Generation: 15s, Training: 35s. Estimated remaining time: 39h 2m 45s. Estimated total time: 43h 38m 11s. Time estimates for 10 more iterations: 8m 43s, 100 more iterations: 1h 27m 16s, 500 more iterations: 7h 16m 21s. [2025-11-13 02:45:07,127][__main__][INFO] - Starting iteration 319. [2025-11-13 02:45:07,611][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 31 and human policies 1. [2025-11-13 02:45:07,612][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 02:45:24,201][__main__][INFO] - Number of regex retries in iteration 319: 0 [2025-11-13 02:45:24,202][__main__][INFO] - agents played in iteration 319 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 02:45:25,068][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.62%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:45:25,097][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.62%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:45:25,124][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.62%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:45:25,148][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.62%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:45:25,150][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 02:45:25,150][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 02:45:25,897][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 02:45:26,367][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 02:45:26,882][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 02:45:27,389][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 02:45:27,897][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 02:45:28,404][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 02:45:28,911][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 02:45:29,416][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 02:45:29,920][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 02:45:30,425][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 02:45:30,932][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 02:45:42,505][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 02:45:43,008][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 02:45:43,514][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 02:45:44,018][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 02:45:44,522][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 02:45:45,027][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 02:45:45,530][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 02:45:46,037][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 02:45:46,543][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 02:45:47,047][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 02:45:47,557][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 02:45:48,057][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 02:45:48,567][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 02:45:49,067][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 02:45:49,568][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 02:45:50,069][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 02:45:50,570][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 02:45:51,071][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 02:45:51,572][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 02:45:52,074][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 02:45:52,590][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 02:45:53,091][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 02:45:53,592][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 02:45:54,094][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 02:45:54,597][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 02:45:55,101][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 02:45:55,603][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 02:45:56,105][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 02:45:56,610][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 02:45:57,111][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 02:45:57,613][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 02:45:58,116][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10879 tokens. [2025-11-13 02:45:58,855][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.27%, Current % of VRAM taken: 59.73%, Block Peak % of device VRAM: 62.41%, ΔTime: 00:00:32 [2025-11-13 02:45:59,630][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 02:45:59,631][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 02:45:59,633][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 02:46:00,577][__main__][INFO] - Iteration 320 took 52s (31.32% Gen, 66.89% Train). Generation: 16s, Training: 35s. Estimated remaining time: 39h 32m 2s. Estimated total time: 44h 8m 21s. Time estimates for 10 more iterations: 8m 49s, 100 more iterations: 1h 28m 16s, 500 more iterations: 7h 21m 23s. [2025-11-13 02:46:00,579][__main__][INFO] - Starting iteration 320. [2025-11-13 02:46:01,068][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 31 and human policies 1. [2025-11-13 02:46:01,069][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 02:46:16,246][__main__][INFO] - Number of regex retries in iteration 320: 0 [2025-11-13 02:46:16,246][__main__][INFO] - agents played in iteration 320 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 02:46:17,068][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:46:17,098][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:46:17,124][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:46:17,148][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:46:17,149][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 02:46:17,149][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 02:46:17,817][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 02:46:18,274][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 02:46:18,791][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 02:46:19,293][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 02:46:19,799][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 02:46:20,308][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 02:46:20,811][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 02:46:21,316][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 02:46:21,819][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 02:46:22,323][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 02:46:22,826][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 02:46:34,417][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 02:46:34,923][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 02:46:35,426][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 02:46:35,929][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 02:46:36,433][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 02:46:36,936][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 02:46:37,439][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 02:46:37,943][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 02:46:38,447][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 02:46:38,952][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 02:46:39,454][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 02:46:39,958][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 02:46:40,461][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 02:46:40,963][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 02:46:41,469][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 02:46:41,966][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 02:46:42,467][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 02:46:42,980][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 02:46:43,480][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 02:46:43,991][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 02:46:44,491][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 02:46:44,991][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 02:46:45,509][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 02:46:46,010][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 02:46:46,516][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 02:46:47,016][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 02:46:47,517][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 02:46:48,022][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 02:46:48,522][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 02:46:49,023][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 02:46:49,528][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 02:46:50,030][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10878 tokens. [2025-11-13 02:46:50,726][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.24%, Current % of VRAM taken: 59.69%, Block Peak % of device VRAM: 62.28%, ΔTime: 00:00:32 [2025-11-13 02:46:51,495][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 02:46:51,497][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 02:46:51,499][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 02:46:53,303][__main__][INFO] - Iteration 321 took 52s (29.06% Gen, 67.49% Train). Generation: 15s, Training: 35s. Estimated remaining time: 38h 54m 36s. Estimated total time: 43h 31m 48s. Time estimates for 10 more iterations: 8m 42s, 100 more iterations: 1h 27m 3s, 500 more iterations: 7h 15m 18s. [2025-11-13 02:46:53,306][__main__][INFO] - Starting iteration 321. [2025-11-13 02:46:53,773][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 32 and human policies 1. [2025-11-13 02:46:53,774][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 02:47:09,451][__main__][INFO] - Number of regex retries in iteration 321: 0 [2025-11-13 02:47:09,452][__main__][INFO] - agents played in iteration 321 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 02:47:10,309][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:47:10,336][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:47:10,362][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:47:10,385][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:47:10,385][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 02:47:10,386][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 02:47:11,051][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 02:47:11,513][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 02:47:12,020][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 02:47:12,523][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 02:47:13,027][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 02:47:13,531][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 02:47:14,036][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 02:47:14,540][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 02:47:15,044][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 02:47:15,552][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 02:47:16,054][mllm.training.trainer_common][INFO] - 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Processing mini-batch 43 of 64 [2025-11-13 02:47:33,192][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 02:47:33,694][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 02:47:34,197][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 02:47:34,699][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 02:47:35,211][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 02:47:35,713][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 02:47:36,214][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 02:47:36,718][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 02:47:37,222][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 02:47:37,723][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 02:47:38,223][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 02:47:38,723][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 02:47:39,227][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 02:47:39,727][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 02:47:40,227][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 02:47:40,732][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 02:47:41,232][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 02:47:41,731][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 02:47:42,230][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 02:47:42,730][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 02:47:43,231][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10878 tokens. [2025-11-13 02:47:43,919][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.19%, Current % of VRAM taken: 59.65%, Block Peak % of device VRAM: 62.34%, ΔTime: 00:00:32 [2025-11-13 02:47:44,706][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 02:47:44,708][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 02:47:44,710][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 02:47:45,636][__main__][INFO] - Iteration 322 took 51s (30.23% Gen, 67.98% Train). Generation: 15s, Training: 35s. Estimated remaining time: 38h 35m 6s. Estimated total time: 43h 13m 10s. Time estimates for 10 more iterations: 8m 38s, 100 more iterations: 1h 26m 26s, 500 more iterations: 7h 12m 11s. [2025-11-13 02:47:45,638][__main__][INFO] - Starting iteration 322. [2025-11-13 02:47:46,132][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 32 and human policies 1. [2025-11-13 02:47:46,133][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 02:47:52,616][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 02:47:56,601][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 02:48:00,820][__main__][INFO] - Number of regex retries in iteration 322: 2 [2025-11-13 02:48:00,821][__main__][INFO] - agents played in iteration 322 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 02:48:01,596][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:48:01,621][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:48:01,646][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:48:01,668][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:48:01,669][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 02:48:01,670][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 02:48:02,329][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 02:48:02,785][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 02:48:03,303][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 02:48:03,806][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 02:48:04,326][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 02:48:04,827][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 02:48:05,328][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 02:48:05,830][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 02:48:06,331][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 02:48:06,832][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 02:48:07,336][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 02:48:18,930][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 02:48:19,431][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 02:48:19,937][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 02:48:20,437][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 02:48:20,939][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 02:48:21,444][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 02:48:21,945][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 02:48:22,447][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 02:48:22,951][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 02:48:23,454][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 02:48:23,956][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 02:48:24,461][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 02:48:24,965][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 02:48:25,469][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 02:48:25,972][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 02:48:26,474][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 02:48:26,978][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 02:48:27,481][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 02:48:27,994][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 02:48:28,495][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 02:48:28,999][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 02:48:29,507][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 02:48:30,010][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 02:48:30,520][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 02:48:31,023][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 02:48:31,526][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 02:48:32,033][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 02:48:32,537][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 02:48:33,038][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 02:48:33,539][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 02:48:34,042][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 02:48:34,547][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10877 tokens. [2025-11-13 02:48:35,258][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.21%, Current % of VRAM taken: 59.67%, Block Peak % of device VRAM: 62.31%, ΔTime: 00:00:32 [2025-11-13 02:48:36,052][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 02:48:36,053][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 02:48:36,055][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 02:48:37,001][__main__][INFO] - Iteration 323 took 50s (28.87% Gen, 69.26% Train). Generation: 14s, Training: 35s. Estimated remaining time: 37h 44m 33s. Estimated total time: 42h 23m 28s. Time estimates for 10 more iterations: 8m 28s, 100 more iterations: 1h 24m 46s, 500 more iterations: 7h 3m 54s. [2025-11-13 02:48:37,003][__main__][INFO] - Starting iteration 323. [2025-11-13 02:48:37,469][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 32 and human policies 1. [2025-11-13 02:48:37,470][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 02:48:41,721][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 02:48:51,927][__main__][INFO] - Number of regex retries in iteration 323: 1 [2025-11-13 02:48:51,927][__main__][INFO] - agents played in iteration 323 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 02:48:52,730][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:48:52,767][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:48:52,792][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:48:52,814][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:48:52,815][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 02:48:52,815][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 02:48:53,499][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 02:48:53,955][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 02:48:54,463][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 02:48:54,963][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 02:48:55,464][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 02:48:55,966][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 02:48:56,465][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 02:48:56,968][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 02:48:57,469][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 02:48:57,974][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 02:48:58,475][mllm.training.trainer_common][INFO] - 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Processing mini-batch 43 of 64 [2025-11-13 02:49:15,635][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 02:49:16,134][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 02:49:16,643][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 02:49:17,148][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 02:49:17,652][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 02:49:18,154][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 02:49:18,658][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 02:49:19,165][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 02:49:19,668][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 02:49:20,170][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 02:49:20,673][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 02:49:21,175][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 02:49:21,679][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 02:49:22,182][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 02:49:22,687][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 02:49:23,191][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 02:49:23,693][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 02:49:24,202][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 02:49:24,705][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 02:49:25,209][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 02:49:25,714][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10875 tokens. [2025-11-13 02:49:26,442][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.23%, Current % of VRAM taken: 59.68%, Block Peak % of device VRAM: 62.23%, ΔTime: 00:00:32 [2025-11-13 02:49:27,265][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 02:49:27,267][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 02:49:27,269][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 02:49:28,404][__main__][INFO] - Iteration 324 took 50s (28.38% Gen, 69.38% Train). Generation: 14s, Training: 35s. Estimated remaining time: 37h 47m 0s. Estimated total time: 42h 26m 47s. Time estimates for 10 more iterations: 8m 29s, 100 more iterations: 1h 24m 53s, 500 more iterations: 7h 4m 27s. [2025-11-13 02:49:28,406][__main__][INFO] - Starting iteration 324. [2025-11-13 02:49:28,909][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 32 and human policies 1. [2025-11-13 02:49:28,909][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 02:49:33,633][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 02:49:43,337][__main__][INFO] - Number of regex retries in iteration 324: 1 [2025-11-13 02:49:43,337][__main__][INFO] - agents played in iteration 324 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 02:49:44,241][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:49:44,267][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:49:44,291][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:49:44,313][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:49:44,314][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 02:49:44,314][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 02:49:45,010][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 02:49:45,470][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 02:49:45,978][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 02:49:46,481][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 02:49:46,983][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 02:49:47,487][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 02:49:47,990][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 02:49:48,492][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 02:49:48,993][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 02:49:49,493][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 02:49:49,994][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 02:50:01,579][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 02:50:02,082][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 02:50:02,588][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 02:50:03,091][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 02:50:03,596][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 02:50:04,100][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 02:50:04,603][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 02:50:05,120][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 02:50:05,625][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 02:50:06,138][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 02:50:06,641][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 02:50:07,146][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 02:50:07,651][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 02:50:08,154][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 02:50:08,657][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 02:50:09,161][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 02:50:09,666][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 02:50:10,170][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 02:50:10,675][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 02:50:11,181][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 02:50:11,687][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 02:50:12,192][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 02:50:12,697][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 02:50:13,201][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 02:50:13,708][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 02:50:14,212][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 02:50:14,718][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 02:50:15,224][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 02:50:15,730][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 02:50:16,234][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 02:50:16,744][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 02:50:17,248][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10857 tokens. [2025-11-13 02:50:17,987][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.26%, Current % of VRAM taken: 59.71%, Block Peak % of device VRAM: 62.35%, ΔTime: 00:00:32 [2025-11-13 02:50:18,754][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 02:50:18,755][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 02:50:18,757][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 02:50:19,685][__main__][INFO] - Iteration 325 took 50s (28.41% Gen, 69.76% Train). Generation: 14s, Training: 35s. Estimated remaining time: 37h 38m 10s. Estimated total time: 42h 18m 48s. Time estimates for 10 more iterations: 8m 27s, 100 more iterations: 1h 24m 37s, 500 more iterations: 7h 3m 8s. [2025-11-13 02:50:19,687][__main__][INFO] - Starting iteration 325. [2025-11-13 02:50:20,176][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 32 and human policies 1. [2025-11-13 02:50:20,177][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 02:50:35,846][__main__][INFO] - Number of regex retries in iteration 325: 0 [2025-11-13 02:50:35,846][__main__][INFO] - agents played in iteration 325 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 02:50:36,648][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:50:36,670][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:50:36,693][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:50:36,715][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:50:36,716][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 02:50:36,717][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 02:50:37,401][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 02:50:37,864][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 02:50:38,369][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 02:50:38,871][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 02:50:39,373][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 02:50:39,875][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 02:50:40,380][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 02:50:40,882][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 02:50:41,384][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 02:50:41,894][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 02:50:42,395][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 02:50:53,986][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 02:50:54,488][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 02:50:54,994][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 02:50:55,499][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 02:50:56,005][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 02:50:56,509][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 02:50:57,017][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 02:50:57,540][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 02:50:58,042][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 02:50:58,547][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 02:50:59,050][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 02:50:59,552][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 02:51:00,064][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 02:51:00,568][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 02:51:01,071][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 02:51:01,572][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 02:51:02,073][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 02:51:02,579][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 02:51:03,081][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 02:51:03,584][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 02:51:04,084][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 02:51:04,584][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 02:51:05,089][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 02:51:05,592][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 02:51:06,093][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 02:51:06,597][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 02:51:07,101][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 02:51:07,603][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 02:51:08,109][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 02:51:08,612][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 02:51:09,122][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 02:51:09,625][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10877 tokens. [2025-11-13 02:51:10,378][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.23%, Current % of VRAM taken: 59.68%, Block Peak % of device VRAM: 62.49%, ΔTime: 00:00:32 [2025-11-13 02:51:11,131][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 02:51:11,133][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 02:51:11,134][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 02:51:12,026][__main__][INFO] - Iteration 326 took 51s (30.22% Gen, 68.06% Train). Generation: 15s, Training: 35s. Estimated remaining time: 38h 31m 0s. Estimated total time: 43h 12m 30s. Time estimates for 10 more iterations: 8m 38s, 100 more iterations: 1h 26m 25s, 500 more iterations: 7h 12m 5s. [2025-11-13 02:51:12,028][__main__][INFO] - Starting iteration 326. [2025-11-13 02:51:12,499][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 32 and human policies 1. [2025-11-13 02:51:12,500][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 02:51:22,248][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 02:51:22,995][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 02:51:27,261][__main__][INFO] - Number of regex retries in iteration 326: 2 [2025-11-13 02:51:27,262][__main__][INFO] - agents played in iteration 326 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 02:51:28,067][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:51:28,089][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:51:28,112][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:51:28,134][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:51:28,134][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 02:51:28,136][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 02:51:28,836][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 02:51:29,296][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 02:51:29,804][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 02:51:30,308][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 02:51:30,811][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 02:51:31,314][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 02:51:31,817][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 02:51:32,319][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 02:51:32,829][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 02:51:33,330][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 02:51:33,832][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 02:51:34,333][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 02:51:34,833][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 02:51:35,348][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 02:51:35,849][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 02:51:36,350][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 02:51:36,865][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 02:51:37,366][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 02:51:37,868][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 02:51:38,367][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 02:51:38,868][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 02:51:39,371][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 02:51:39,871][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 02:51:40,372][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 02:51:40,874][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 02:51:41,378][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 02:51:41,884][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 02:51:42,387][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 02:51:42,889][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 02:51:43,393][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 02:51:43,896][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 02:51:44,398][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 02:51:44,900][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 02:51:45,399][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 02:51:45,902][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 02:51:46,406][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 02:51:46,911][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 02:51:47,414][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 02:51:47,913][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 02:51:48,413][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 02:51:48,913][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 02:51:49,411][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 02:51:49,918][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 02:51:50,422][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 02:51:50,925][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 02:51:51,441][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 02:51:51,944][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 02:51:52,463][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 02:51:52,966][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 02:51:53,474][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 02:51:53,978][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 02:51:54,482][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 02:51:54,985][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 02:51:55,488][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 02:51:55,991][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 02:51:56,494][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 02:51:56,997][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 02:51:57,498][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 02:51:57,997][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 02:51:58,497][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 02:51:58,998][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 02:51:59,501][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 02:52:00,005][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 02:52:00,508][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 02:52:01,011][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10877 tokens. [2025-11-13 02:52:01,738][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.16%, Current % of VRAM taken: 59.62%, Block Peak % of device VRAM: 62.28%, ΔTime: 00:00:32 [2025-11-13 02:52:02,484][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 02:52:02,487][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 02:52:02,490][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 02:52:03,386][__main__][INFO] - Iteration 327 took 50s (29.01% Gen, 69.23% Train). Generation: 14s, Training: 35s. Estimated remaining time: 37h 42m 1s. Estimated total time: 42h 24m 23s. Time estimates for 10 more iterations: 8m 28s, 100 more iterations: 1h 24m 48s, 500 more iterations: 7h 4m 3s. [2025-11-13 02:52:03,388][__main__][INFO] - Starting iteration 327. [2025-11-13 02:52:03,858][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 32 and human policies 1. [2025-11-13 02:52:03,859][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 02:52:09,216][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 02:52:20,566][__main__][INFO] - Number of regex retries in iteration 327: 1 [2025-11-13 02:52:20,566][__main__][INFO] - agents played in iteration 327 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 02:52:21,449][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:52:21,477][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:52:21,505][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:52:21,529][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:52:21,530][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 02:52:21,531][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 02:52:22,273][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 02:52:22,748][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 02:52:23,257][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 02:52:23,760][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 02:52:24,274][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 02:52:24,776][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 02:52:25,277][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 02:52:25,780][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 02:52:26,286][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 02:52:26,791][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 02:52:27,292][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 02:52:27,799][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 02:52:28,305][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 02:52:28,808][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 02:52:29,310][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 02:52:29,811][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 02:52:30,314][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 02:52:30,819][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 02:52:31,319][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 02:52:31,821][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 02:52:32,324][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 02:52:32,826][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 02:52:33,336][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 02:52:33,839][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 02:52:34,341][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 02:52:34,849][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 02:52:35,349][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 02:52:35,862][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 02:52:36,367][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 02:52:36,867][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 02:52:37,380][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 02:52:37,881][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 02:52:38,385][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 02:52:38,887][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 02:52:39,389][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 02:52:39,893][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 02:52:40,394][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 02:52:40,898][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 02:52:41,399][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 02:52:41,898][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 02:52:42,398][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 02:52:42,899][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 02:52:43,398][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 02:52:43,906][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 02:52:44,410][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 02:52:44,914][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 02:52:45,416][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 02:52:45,921][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 02:52:46,426][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 02:52:46,929][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 02:52:47,435][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 02:52:47,949][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 02:52:48,452][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 02:52:48,970][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 02:52:49,472][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 02:52:49,977][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 02:52:50,478][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 02:52:50,980][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 02:52:51,485][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 02:52:51,986][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 02:52:52,487][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 02:52:52,990][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 02:52:53,487][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 02:52:53,988][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 02:52:54,490][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10878 tokens. [2025-11-13 02:52:55,245][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.23%, Current % of VRAM taken: 59.68%, Block Peak % of device VRAM: 62.31%, ΔTime: 00:00:32 [2025-11-13 02:52:56,020][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 02:52:56,022][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 02:52:56,024][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 02:52:57,122][__main__][INFO] - Iteration 328 took 53s (31.37% Gen, 66.57% Train). Generation: 16s, Training: 35s. Estimated remaining time: 39h 39m 57s. Estimated total time: 44h 23m 12s. Time estimates for 10 more iterations: 8m 52s, 100 more iterations: 1h 28m 46s, 500 more iterations: 7h 23m 52s. [2025-11-13 02:52:57,124][__main__][INFO] - Starting iteration 328. [2025-11-13 02:52:57,597][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 32 and human policies 1. [2025-11-13 02:52:57,598][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 02:53:13,505][__main__][INFO] - Number of regex retries in iteration 328: 0 [2025-11-13 02:53:13,506][__main__][INFO] - agents played in iteration 328 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 02:53:14,314][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:53:14,344][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:53:14,371][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:53:14,394][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:53:14,395][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 02:53:14,396][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 02:53:15,100][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 02:53:15,558][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 02:53:16,065][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 02:53:16,569][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 02:53:17,073][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 02:53:17,582][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 02:53:18,084][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 02:53:18,585][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 02:53:19,093][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 02:53:19,593][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 02:53:20,095][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 02:53:20,597][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 02:53:21,099][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 02:53:21,607][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 02:53:22,107][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 02:53:22,611][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 02:53:23,115][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 02:53:23,616][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 02:53:24,116][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 02:53:24,614][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 02:53:25,115][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 02:53:25,618][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 02:53:26,119][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 02:53:26,619][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 02:53:27,120][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 02:53:27,624][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 02:53:28,132][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 02:53:28,634][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 02:53:29,135][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 02:53:29,638][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 02:53:30,139][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 02:53:30,639][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 02:53:31,140][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 02:53:31,647][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 02:53:32,162][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 02:53:32,687][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 02:53:33,196][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 02:53:33,695][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 02:53:34,198][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 02:53:34,708][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 02:53:35,213][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 02:53:35,716][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 02:53:36,217][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 02:53:36,716][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 02:53:37,215][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 02:53:37,715][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 02:53:38,215][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 02:53:38,715][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 02:53:39,214][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 02:53:39,716][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 02:53:40,222][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 02:53:40,725][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 02:53:41,229][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 02:53:41,735][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 02:53:42,239][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 02:53:42,745][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 02:53:43,250][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 02:53:43,754][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 02:53:44,267][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 02:53:44,771][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 02:53:45,274][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 02:53:45,780][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 02:53:46,282][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 02:53:46,793][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 02:53:47,294][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10875 tokens. [2025-11-13 02:53:47,994][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.25%, Current % of VRAM taken: 59.70%, Block Peak % of device VRAM: 62.49%, ΔTime: 00:00:32 [2025-11-13 02:53:48,780][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 02:53:48,782][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 02:53:48,784][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 02:53:49,706][__main__][INFO] - Iteration 329 took 52s (30.53% Gen, 67.70% Train). Generation: 15s, Training: 35s. Estimated remaining time: 38h 41m 19s. Estimated total time: 43h 25m 27s. Time estimates for 10 more iterations: 8m 41s, 100 more iterations: 1h 26m 50s, 500 more iterations: 7h 14m 14s. [2025-11-13 02:53:49,708][__main__][INFO] - Starting iteration 329. [2025-11-13 02:53:50,184][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 32 and human policies 1. [2025-11-13 02:53:50,185][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 02:53:56,097][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 02:54:01,723][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 02:54:02,546][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 02:54:07,652][__main__][INFO] - Number of regex retries in iteration 329: 3 [2025-11-13 02:54:07,653][__main__][INFO] - agents played in iteration 329 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 02:54:08,503][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:54:08,531][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:54:08,557][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:54:08,581][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:54:08,582][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 02:54:08,582][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 02:54:09,349][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 02:54:09,812][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 02:54:10,321][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 02:54:10,827][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 02:54:11,331][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 02:54:11,837][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 02:54:12,342][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 02:54:12,845][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 02:54:13,349][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 02:54:13,852][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 02:54:14,356][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 02:54:14,870][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 02:54:15,373][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 02:54:15,876][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 02:54:16,378][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 02:54:16,881][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 02:54:17,387][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 02:54:17,888][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 02:54:18,391][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 02:54:18,893][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 02:54:19,395][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 02:54:19,899][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 02:54:20,399][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 02:54:20,901][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 02:54:21,405][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 02:54:21,907][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 02:54:22,408][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 02:54:22,910][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 02:54:23,411][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 02:54:23,916][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 02:54:24,416][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 02:54:24,918][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 02:54:25,419][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 02:54:25,920][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 02:54:26,425][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 02:54:26,929][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 02:54:27,432][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 02:54:27,935][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 02:54:28,437][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 02:54:28,937][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 02:54:29,439][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 02:54:29,941][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 02:54:30,447][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 02:54:30,952][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 02:54:31,458][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 02:54:31,971][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 02:54:32,472][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 02:54:32,983][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 02:54:33,484][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 02:54:33,990][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 02:54:34,507][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 02:54:35,008][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 02:54:35,511][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 02:54:36,017][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 02:54:36,524][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 02:54:37,035][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 02:54:37,541][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 02:54:38,043][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 02:54:38,546][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 02:54:39,050][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 02:54:39,561][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 02:54:40,062][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 02:54:40,563][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 02:54:41,077][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 02:54:41,580][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10878 tokens. [2025-11-13 02:54:42,328][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.21%, Current % of VRAM taken: 59.66%, Block Peak % of device VRAM: 62.41%, ΔTime: 00:00:32 [2025-11-13 02:54:43,104][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 02:54:43,106][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 02:54:43,107][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 02:54:44,031][__main__][INFO] - Iteration 330 took 53s (32.44% Gen, 65.84% Train). Generation: 17s, Training: 35s. Estimated remaining time: 40h 7m 19s. Estimated total time: 44h 52m 22s. Time estimates for 10 more iterations: 8m 58s, 100 more iterations: 1h 29m 44s, 500 more iterations: 7h 28m 43s. [2025-11-13 02:54:44,033][__main__][INFO] - Starting iteration 330. [2025-11-13 02:54:44,528][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 32 and human policies 1. [2025-11-13 02:54:44,528][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 02:54:49,137][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 02:54:59,371][__main__][INFO] - Number of regex retries in iteration 330: 1 [2025-11-13 02:54:59,372][__main__][INFO] - agents played in iteration 330 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 02:55:00,338][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:55:00,362][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:55:00,386][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:55:00,409][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:55:00,409][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 02:55:00,411][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 02:55:01,075][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 02:55:01,532][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 02:55:02,043][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 02:55:02,543][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 02:55:03,049][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 02:55:03,553][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 02:55:04,059][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 02:55:04,566][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 02:55:05,072][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 02:55:05,576][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 02:55:06,082][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 02:55:06,585][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 02:55:07,103][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 02:55:07,607][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 02:55:08,111][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 02:55:08,614][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 02:55:09,117][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 02:55:09,620][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 02:55:10,124][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 02:55:10,625][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 02:55:11,131][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 02:55:11,633][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 02:55:17,655][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 02:55:18,159][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 02:55:18,661][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 02:55:19,175][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 02:55:19,677][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 02:55:20,194][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 02:55:20,696][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 02:55:21,197][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 02:55:21,723][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 02:55:22,230][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 02:55:22,734][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 02:55:23,240][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 02:55:23,744][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 02:55:24,246][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 02:55:24,748][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 02:55:25,249][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 02:55:25,748][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 02:55:26,247][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 02:55:26,749][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 02:55:27,248][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 02:55:27,747][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 02:55:28,246][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 02:55:28,747][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 02:55:29,247][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 02:55:29,749][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 02:55:30,249][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 02:55:30,751][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 02:55:31,251][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 02:55:31,753][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 02:55:32,256][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 02:55:32,760][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 02:55:33,265][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10879 tokens. [2025-11-13 02:55:33,991][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.27%, Current % of VRAM taken: 59.72%, Block Peak % of device VRAM: 62.34%, ΔTime: 00:00:32 [2025-11-13 02:55:34,772][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 02:55:34,774][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 02:55:34,775][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 02:55:36,548][__main__][INFO] - Iteration 331 took 52s (28.53% Gen, 68.06% Train). Generation: 14s, Training: 35s. Estimated remaining time: 38h 35m 8s. Estimated total time: 43h 21m 3s. Time estimates for 10 more iterations: 8m 40s, 100 more iterations: 1h 26m 42s, 500 more iterations: 7h 13m 30s. [2025-11-13 02:55:36,550][__main__][INFO] - Starting iteration 331. [2025-11-13 02:55:37,031][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 33 and human policies 1. [2025-11-13 02:55:37,032][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 02:55:42,163][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 02:55:43,094][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 02:55:52,522][__main__][INFO] - Number of regex retries in iteration 331: 2 [2025-11-13 02:55:52,523][__main__][INFO] - agents played in iteration 331 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 02:55:53,371][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:55:53,399][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:55:53,425][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:55:53,449][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:55:53,449][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 02:55:53,450][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 02:55:54,129][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 02:55:54,587][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 02:55:55,096][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 02:55:55,603][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 02:55:56,111][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 02:55:56,616][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 02:55:57,124][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 02:55:57,628][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 02:55:58,132][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 02:55:58,642][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 02:55:59,148][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 02:55:59,652][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 02:56:00,156][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 02:56:00,663][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 02:56:01,170][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 02:56:01,673][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 02:56:02,176][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 02:56:02,692][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 02:56:03,194][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 02:56:03,699][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 02:56:04,202][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 02:56:04,703][mllm.training.trainer_common][INFO] - 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Processing mini-batch 54 of 64 [2025-11-13 02:56:21,786][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 02:56:22,293][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 02:56:22,793][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 02:56:23,292][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 02:56:23,809][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 02:56:24,311][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 02:56:24,814][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 02:56:25,314][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 02:56:25,813][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 02:56:26,323][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10869 tokens. [2025-11-13 02:56:27,003][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.23%, Current % of VRAM taken: 59.68%, Block Peak % of device VRAM: 62.30%, ΔTime: 00:00:32 [2025-11-13 02:56:27,794][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 02:56:27,796][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 02:56:27,798][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 02:56:28,887][__main__][INFO] - Iteration 332 took 51s (29.87% Gen, 68.02% Train). Generation: 15s, Training: 35s. Estimated remaining time: 38h 26m 2s. Estimated total time: 43h 12m 50s. Time estimates for 10 more iterations: 8m 38s, 100 more iterations: 1h 26m 25s, 500 more iterations: 7h 12m 8s. [2025-11-13 02:56:28,889][__main__][INFO] - Starting iteration 332. [2025-11-13 02:56:29,379][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 33 and human policies 1. [2025-11-13 02:56:29,380][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 02:56:45,144][__main__][INFO] - Number of regex retries in iteration 332: 0 [2025-11-13 02:56:45,144][__main__][INFO] - agents played in iteration 332 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 02:56:45,967][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:56:45,995][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:56:46,022][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:56:46,047][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:56:46,047][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 02:56:46,048][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 02:56:46,733][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 02:56:47,190][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 02:56:47,702][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 02:56:48,206][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 02:56:48,718][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 02:56:49,221][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 02:56:49,726][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 02:56:50,236][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 02:56:50,743][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 02:56:51,250][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 02:56:51,758][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 02:57:03,360][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 02:57:03,858][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 02:57:04,359][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 02:57:04,859][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 02:57:05,359][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 02:57:05,861][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 02:57:06,361][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 02:57:06,860][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 02:57:07,367][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 02:57:07,869][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 02:57:08,371][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 02:57:08,872][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 02:57:09,374][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 02:57:09,876][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 02:57:10,378][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 02:57:10,880][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 02:57:11,380][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 02:57:11,880][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 02:57:12,394][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 02:57:12,897][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 02:57:13,397][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 02:57:13,904][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 02:57:14,402][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 02:57:14,917][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 02:57:15,416][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 02:57:15,916][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 02:57:16,419][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 02:57:16,920][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 02:57:17,420][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 02:57:17,920][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 02:57:18,422][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 02:57:18,931][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10877 tokens. [2025-11-13 02:57:19,614][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.21%, Current % of VRAM taken: 59.66%, Block Peak % of device VRAM: 62.31%, ΔTime: 00:00:32 [2025-11-13 02:57:20,418][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 02:57:20,419][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 02:57:20,421][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 02:57:21,720][__main__][INFO] - Iteration 333 took 52s (30.12% Gen, 67.40% Train). Generation: 15s, Training: 35s. Estimated remaining time: 38h 49m 21s. Estimated total time: 43h 37m 1s. Time estimates for 10 more iterations: 8m 43s, 100 more iterations: 1h 27m 14s, 500 more iterations: 7h 16m 10s. [2025-11-13 02:57:21,722][__main__][INFO] - Starting iteration 333. [2025-11-13 02:57:22,203][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 33 and human policies 1. [2025-11-13 02:57:22,204][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 02:57:36,996][__main__][INFO] - Number of regex retries in iteration 333: 0 [2025-11-13 02:57:36,997][__main__][INFO] - agents played in iteration 333 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 02:57:37,807][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:57:37,834][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:57:37,860][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:57:37,883][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:57:37,884][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 02:57:37,884][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 02:57:38,593][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 02:57:39,050][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 02:57:39,556][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 02:57:40,057][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 02:57:40,558][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 02:57:41,061][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 02:57:41,565][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 02:57:42,067][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 02:57:42,574][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 02:57:43,079][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 02:57:43,586][mllm.training.trainer_common][INFO] - 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Processing mini-batch 43 of 64 [2025-11-13 02:58:00,742][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 02:58:01,245][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 02:58:01,748][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 02:58:02,248][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 02:58:02,750][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 02:58:03,253][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 02:58:03,753][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 02:58:04,255][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 02:58:04,755][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 02:58:05,256][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 02:58:05,758][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 02:58:06,261][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 02:58:06,761][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 02:58:07,259][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 02:58:07,758][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 02:58:08,257][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 02:58:08,755][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 02:58:09,254][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 02:58:09,753][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 02:58:10,252][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 02:58:10,750][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10875 tokens. [2025-11-13 02:58:11,426][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.24%, Current % of VRAM taken: 59.69%, Block Peak % of device VRAM: 62.39%, ΔTime: 00:00:32 [2025-11-13 02:58:12,198][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 02:58:12,200][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 02:58:12,202][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 02:58:13,192][__main__][INFO] - Iteration 334 took 50s (29.01% Gen, 69.04% Train). Generation: 14s, Training: 35s. Estimated remaining time: 37h 40m 54s. Estimated total time: 42h 29m 26s. Time estimates for 10 more iterations: 8m 29s, 100 more iterations: 1h 24m 58s, 500 more iterations: 7h 4m 54s. [2025-11-13 02:58:13,194][__main__][INFO] - Starting iteration 334. [2025-11-13 02:58:13,670][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 33 and human policies 1. [2025-11-13 02:58:13,671][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 02:58:18,624][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 02:58:20,479][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 02:58:29,343][__main__][INFO] - Number of regex retries in iteration 334: 2 [2025-11-13 02:58:29,344][__main__][INFO] - agents played in iteration 334 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 02:58:30,168][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:58:30,196][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:58:30,222][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:58:30,245][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:58:30,246][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 02:58:30,246][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 02:58:30,926][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 02:58:31,389][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 02:58:31,897][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 02:58:32,401][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 02:58:32,907][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 02:58:33,410][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 02:58:33,936][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 02:58:34,440][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 02:58:34,943][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 02:58:35,447][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 02:58:35,950][mllm.training.trainer_common][INFO] - 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Processing mini-batch 21 of 64 [2025-11-13 02:58:42,026][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 02:58:42,530][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 02:58:43,042][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 02:58:43,547][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 02:58:44,052][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 02:58:44,555][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 02:58:45,058][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 02:58:45,562][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 02:58:46,066][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 02:58:46,568][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 02:58:47,073][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 02:58:47,574][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 02:58:48,075][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 02:58:48,575][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 02:58:49,079][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 02:58:49,581][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 02:58:50,079][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 02:58:50,580][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 02:58:51,082][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 02:58:51,584][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 02:58:52,088][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 02:58:52,590][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 02:58:53,092][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 02:58:53,594][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 02:58:54,097][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 02:58:54,599][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 02:58:55,102][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 02:58:55,604][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 02:58:56,107][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 02:58:56,608][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 02:58:57,108][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 02:58:57,608][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 02:58:58,110][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 02:58:58,613][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 02:58:59,129][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 02:58:59,630][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 02:59:00,142][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 02:59:00,644][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 02:59:01,145][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 02:59:01,649][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 02:59:02,153][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 02:59:02,655][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 02:59:03,154][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10880 tokens. [2025-11-13 02:59:03,840][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.18%, Current % of VRAM taken: 59.64%, Block Peak % of device VRAM: 62.29%, ΔTime: 00:00:32 [2025-11-13 02:59:04,634][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 02:59:04,636][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 02:59:04,637][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 02:59:05,633][__main__][INFO] - Iteration 335 took 51s (30.16% Gen, 67.92% Train). Generation: 15s, Training: 35s. Estimated remaining time: 38h 28m 46s. Estimated total time: 43h 18m 10s. Time estimates for 10 more iterations: 8m 39s, 100 more iterations: 1h 26m 36s, 500 more iterations: 7h 13m 1s. [2025-11-13 02:59:05,636][__main__][INFO] - Starting iteration 335. [2025-11-13 02:59:06,102][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 33 and human policies 1. [2025-11-13 02:59:06,103][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 02:59:19,707][__main__][INFO] - Number of regex retries in iteration 335: 0 [2025-11-13 02:59:19,707][__main__][INFO] - agents played in iteration 335 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 02:59:20,509][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:59:20,531][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:59:20,554][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:59:20,576][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 02:59:20,577][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 02:59:20,577][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 02:59:21,292][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 02:59:21,750][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 02:59:22,256][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 02:59:22,759][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 02:59:23,259][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 02:59:23,767][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 02:59:24,268][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 02:59:24,770][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 02:59:25,271][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 02:59:25,775][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 02:59:26,277][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 02:59:26,777][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 02:59:27,277][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 02:59:27,779][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 02:59:28,280][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 02:59:28,781][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 02:59:29,283][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 02:59:29,787][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 02:59:30,290][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 02:59:30,791][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 02:59:31,297][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 02:59:31,799][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 02:59:32,301][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 02:59:32,808][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 02:59:33,313][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 02:59:33,822][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 02:59:34,339][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 02:59:34,842][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 02:59:35,362][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 02:59:35,868][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 02:59:36,373][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 02:59:36,890][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 02:59:37,397][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 02:59:37,904][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 02:59:38,411][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 02:59:38,915][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 02:59:39,422][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 02:59:39,925][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 02:59:40,428][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 02:59:40,945][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 02:59:41,449][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 02:59:41,971][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 02:59:42,475][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 02:59:42,976][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 02:59:43,476][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 02:59:43,976][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 02:59:44,478][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 02:59:44,977][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 02:59:45,477][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 02:59:45,984][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 02:59:46,484][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 02:59:46,985][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 02:59:47,486][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 02:59:47,987][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 02:59:48,490][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 02:59:48,990][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 02:59:49,490][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 02:59:49,992][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 02:59:50,491][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 02:59:50,992][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 02:59:51,492][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 02:59:51,992][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 02:59:52,495][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 02:59:52,995][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 02:59:53,494][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10878 tokens. [2025-11-13 02:59:54,188][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.19%, Current % of VRAM taken: 59.64%, Block Peak % of device VRAM: 62.57%, ΔTime: 00:00:32 [2025-11-13 02:59:54,979][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 02:59:54,981][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 02:59:54,982][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 02:59:55,938][__main__][INFO] - Iteration 336 took 49s (27.30% Gen, 70.78% Train). Generation: 13s, Training: 35s. Estimated remaining time: 36h 41m 35s. Estimated total time: 41h 31m 50s. Time estimates for 10 more iterations: 8m 18s, 100 more iterations: 1h 23m 3s, 500 more iterations: 6h 55m 18s. [2025-11-13 02:59:55,940][__main__][INFO] - Starting iteration 336. [2025-11-13 02:59:56,422][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 33 and human policies 1. [2025-11-13 02:59:56,422][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 03:00:00,600][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 03:00:00,691][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 03:00:11,273][__main__][INFO] - Number of regex retries in iteration 336: 2 [2025-11-13 03:00:11,274][__main__][INFO] - agents played in iteration 336 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 03:00:12,125][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:00:12,148][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:00:12,171][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:00:12,193][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:00:12,194][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 03:00:12,195][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 03:00:12,949][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 03:00:13,413][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 03:00:13,924][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 03:00:14,429][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 03:00:14,935][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 03:00:15,439][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 03:00:15,956][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 03:00:16,461][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 03:00:16,968][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 03:00:17,471][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 03:00:17,976][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 03:00:29,595][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 03:00:30,100][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 03:00:30,606][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 03:00:31,111][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 03:00:31,618][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 03:00:32,120][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 03:00:32,622][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 03:00:33,126][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 03:00:33,627][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 03:00:34,133][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 03:00:34,647][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 03:00:35,151][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 03:00:35,668][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 03:00:36,168][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 03:00:36,669][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 03:00:37,170][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 03:00:37,670][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 03:00:38,175][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 03:00:38,673][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 03:00:39,172][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 03:00:39,681][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 03:00:40,180][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 03:00:40,680][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 03:00:41,189][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 03:00:41,689][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 03:00:42,192][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 03:00:42,692][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 03:00:43,194][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 03:00:43,697][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 03:00:44,199][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 03:00:44,699][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 03:00:45,201][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10877 tokens. [2025-11-13 03:00:45,902][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.25%, Current % of VRAM taken: 59.70%, Block Peak % of device VRAM: 62.35%, ΔTime: 00:00:32 [2025-11-13 03:00:46,708][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 03:00:46,710][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 03:00:46,712][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 03:00:47,660][__main__][INFO] - Iteration 337 took 51s (28.98% Gen, 69.16% Train). Generation: 14s, Training: 35s. Estimated remaining time: 37h 50m 49s. Estimated total time: 42h 41m 55s. Time estimates for 10 more iterations: 8m 32s, 100 more iterations: 1h 25m 23s, 500 more iterations: 7h 6m 59s. [2025-11-13 03:00:47,662][__main__][INFO] - Starting iteration 337. [2025-11-13 03:00:48,128][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 33 and human policies 1. [2025-11-13 03:00:48,129][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 03:00:52,389][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 03:01:03,444][__main__][INFO] - Number of regex retries in iteration 337: 1 [2025-11-13 03:01:03,445][__main__][INFO] - agents played in iteration 337 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 03:01:04,308][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:01:04,336][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:01:04,363][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:01:04,386][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:01:04,387][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 03:01:04,388][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 03:01:05,099][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 03:01:05,560][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 03:01:06,071][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 03:01:06,577][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 03:01:07,080][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 03:01:07,589][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 03:01:08,093][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 03:01:08,597][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 03:01:09,107][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 03:01:09,611][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 03:01:10,117][mllm.training.trainer_common][INFO] - 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Processing mini-batch 21 of 64 [2025-11-13 03:01:16,158][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 03:01:16,659][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 03:01:17,161][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 03:01:17,664][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 03:01:18,176][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 03:01:18,677][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 03:01:19,179][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 03:01:19,688][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 03:01:20,190][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 03:01:20,694][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 03:01:21,196][mllm.training.trainer_common][INFO] - 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Processing mini-batch 43 of 64 [2025-11-13 03:01:27,272][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 03:01:27,776][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 03:01:28,280][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 03:01:28,793][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 03:01:29,296][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 03:01:29,803][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 03:01:30,311][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 03:01:30,811][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 03:01:31,314][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 03:01:31,814][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 03:01:32,315][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 03:01:32,817][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 03:01:33,319][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 03:01:33,819][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 03:01:34,322][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 03:01:34,822][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 03:01:35,326][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 03:01:35,826][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 03:01:36,330][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 03:01:36,834][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 03:01:37,335][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10876 tokens. [2025-11-13 03:01:38,042][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.21%, Current % of VRAM taken: 59.66%, Block Peak % of device VRAM: 62.29%, ΔTime: 00:00:32 [2025-11-13 03:01:38,833][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 03:01:38,835][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 03:01:38,837][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 03:01:39,795][__main__][INFO] - Iteration 338 took 51s (29.64% Gen, 68.50% Train). Generation: 15s, Training: 35s. Estimated remaining time: 38h 11m 23s. Estimated total time: 43h 3m 22s. Time estimates for 10 more iterations: 8m 36s, 100 more iterations: 1h 26m 6s, 500 more iterations: 7h 10m 33s. [2025-11-13 03:01:39,798][__main__][INFO] - Starting iteration 338. [2025-11-13 03:01:40,292][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 33 and human policies 1. [2025-11-13 03:01:40,293][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 03:01:51,318][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 03:01:57,122][__main__][INFO] - Number of regex retries in iteration 338: 1 [2025-11-13 03:01:57,123][__main__][INFO] - agents played in iteration 338 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 03:01:57,904][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:01:57,929][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:01:57,954][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:01:57,976][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:01:57,976][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 03:01:57,977][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 03:01:58,694][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 03:01:59,167][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 03:01:59,676][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 03:02:00,179][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 03:02:00,682][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 03:02:01,182][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 03:02:01,698][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 03:02:02,198][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 03:02:02,701][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 03:02:03,211][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 03:02:03,716][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 03:02:15,330][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 03:02:15,837][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 03:02:16,341][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 03:02:16,846][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 03:02:17,361][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 03:02:17,864][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 03:02:18,368][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 03:02:18,874][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 03:02:19,378][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 03:02:19,888][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 03:02:20,391][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 03:02:20,895][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 03:02:21,400][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 03:02:21,905][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 03:02:22,407][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 03:02:22,910][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 03:02:23,410][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 03:02:23,914][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 03:02:24,416][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 03:02:24,919][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 03:02:25,425][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 03:02:25,931][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 03:02:26,433][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 03:02:26,934][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 03:02:27,435][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 03:02:27,937][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 03:02:28,437][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 03:02:28,937][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 03:02:29,439][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 03:02:29,938][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 03:02:30,439][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 03:02:30,940][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10857 tokens. [2025-11-13 03:02:31,632][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.24%, Current % of VRAM taken: 59.69%, Block Peak % of device VRAM: 62.31%, ΔTime: 00:00:32 [2025-11-13 03:02:32,419][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 03:02:32,420][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 03:02:32,422][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 03:02:33,370][__main__][INFO] - Iteration 339 took 53s (31.70% Gen, 66.50% Train). Generation: 16s, Training: 35s. Estimated remaining time: 39h 21m 6s. Estimated total time: 44h 13m 57s. Time estimates for 10 more iterations: 8m 50s, 100 more iterations: 1h 28m 27s, 500 more iterations: 7h 22m 19s. [2025-11-13 03:02:33,372][__main__][INFO] - Starting iteration 339. [2025-11-13 03:02:33,861][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 33 and human policies 1. [2025-11-13 03:02:33,861][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 03:02:46,880][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 03:02:49,682][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 03:02:50,664][__main__][INFO] - Number of regex retries in iteration 339: 2 [2025-11-13 03:02:50,665][__main__][INFO] - agents played in iteration 339 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 03:02:51,510][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:02:51,532][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:02:51,555][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:02:51,577][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:02:51,578][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 03:02:51,579][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 03:02:52,272][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 03:02:52,734][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 03:02:53,244][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 03:02:53,748][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 03:02:54,253][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 03:02:54,758][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 03:02:55,263][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 03:02:55,765][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 03:02:56,266][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 03:02:56,768][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 03:02:57,268][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 03:02:57,769][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 03:02:58,270][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 03:02:58,776][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 03:02:59,298][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 03:02:59,800][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 03:03:00,305][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 03:03:00,810][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 03:03:01,315][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 03:03:01,818][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 03:03:02,322][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 03:03:02,829][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 03:03:03,337][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 03:03:03,843][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 03:03:04,347][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 03:03:04,850][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 03:03:05,352][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 03:03:05,866][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 03:03:06,370][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 03:03:06,873][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 03:03:07,375][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 03:03:07,877][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 03:03:08,397][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 03:03:08,901][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 03:03:09,402][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 03:03:09,913][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 03:03:10,417][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 03:03:10,922][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 03:03:11,424][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 03:03:11,930][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 03:03:12,434][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 03:03:12,940][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 03:03:13,446][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 03:03:13,949][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 03:03:14,452][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 03:03:14,958][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 03:03:15,460][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 03:03:15,964][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 03:03:16,470][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 03:03:16,975][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 03:03:17,499][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 03:03:18,000][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 03:03:18,502][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 03:03:19,007][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 03:03:19,509][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 03:03:20,014][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 03:03:20,515][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 03:03:21,016][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 03:03:21,519][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 03:03:22,022][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 03:03:22,526][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 03:03:23,031][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 03:03:23,534][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 03:03:24,038][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 03:03:24,539][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10880 tokens. [2025-11-13 03:03:25,236][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.26%, Current % of VRAM taken: 59.72%, Block Peak % of device VRAM: 62.32%, ΔTime: 00:00:32 [2025-11-13 03:03:26,022][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 03:03:26,024][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 03:03:26,025][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 03:03:27,063][__main__][INFO] - Iteration 340 took 53s (31.58% Gen, 66.46% Train). Generation: 16s, Training: 35s. Estimated remaining time: 39h 26m 23s. Estimated total time: 44h 20m 8s. Time estimates for 10 more iterations: 8m 52s, 100 more iterations: 1h 28m 40s, 500 more iterations: 7h 23m 21s. [2025-11-13 03:03:27,065][__main__][INFO] - Starting iteration 340. [2025-11-13 03:03:27,548][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 33 and human policies 1. [2025-11-13 03:03:27,548][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 03:03:36,227][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 03:03:44,367][__main__][INFO] - Number of regex retries in iteration 340: 1 [2025-11-13 03:03:44,368][__main__][INFO] - agents played in iteration 340 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 03:03:45,214][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:03:45,238][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:03:45,261][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:03:45,284][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:03:45,284][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 03:03:45,286][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 03:03:45,989][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 03:03:46,449][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 03:03:46,959][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 03:03:47,463][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 03:03:47,970][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 03:03:48,474][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 03:03:48,977][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 03:03:49,483][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 03:03:49,987][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 03:03:50,492][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 03:03:50,994][mllm.training.trainer_common][INFO] - 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Processing mini-batch 21 of 64 [2025-11-13 03:03:57,050][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 03:03:57,555][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 03:03:58,061][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 03:03:58,564][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 03:03:59,068][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 03:03:59,578][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 03:04:00,081][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 03:04:00,585][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 03:04:01,099][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 03:04:01,603][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 03:04:02,118][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 03:04:02,625][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 03:04:03,130][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 03:04:03,634][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 03:04:04,139][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 03:04:04,640][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 03:04:05,141][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 03:04:05,645][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 03:04:06,153][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 03:04:06,657][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 03:04:07,161][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 03:04:07,668][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 03:04:08,171][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 03:04:08,678][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 03:04:09,181][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 03:04:09,687][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 03:04:10,204][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 03:04:10,707][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 03:04:11,211][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 03:04:11,714][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 03:04:12,218][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 03:04:12,719][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 03:04:13,219][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 03:04:13,721][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 03:04:14,238][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 03:04:14,740][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 03:04:15,242][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 03:04:15,741][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 03:04:16,242][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 03:04:16,749][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 03:04:17,252][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 03:04:17,755][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 03:04:18,260][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10878 tokens. [2025-11-13 03:04:18,951][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.22%, Current % of VRAM taken: 59.67%, Block Peak % of device VRAM: 62.41%, ΔTime: 00:00:32 [2025-11-13 03:04:19,743][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 03:04:19,745][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 03:04:19,747][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 03:04:21,585][__main__][INFO] - Iteration 341 took 54s (31.13% Gen, 65.47% Train). Generation: 16s, Training: 35s. Estimated remaining time: 40h 7m 14s. Estimated total time: 45h 1m 54s. Time estimates for 10 more iterations: 9m 0s, 100 more iterations: 1h 30m 3s, 500 more iterations: 7h 30m 19s. [2025-11-13 03:04:21,588][__main__][INFO] - Starting iteration 341. [2025-11-13 03:04:22,075][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 34 and human policies 1. [2025-11-13 03:04:22,076][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 03:04:26,206][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 03:04:28,366][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 03:04:30,472][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 03:04:31,877][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 03:04:37,615][__main__][INFO] - Number of regex retries in iteration 341: 4 [2025-11-13 03:04:37,616][__main__][INFO] - agents played in iteration 341 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 03:04:38,495][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:04:38,518][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:04:38,542][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:04:38,564][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:04:38,565][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 03:04:38,565][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 03:04:39,279][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 03:04:39,737][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 03:04:40,249][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 03:04:40,751][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 03:04:41,256][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 03:04:41,761][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 03:04:42,263][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 03:04:42,765][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 03:04:43,274][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 03:04:43,778][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 03:04:44,280][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 03:04:44,781][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 03:04:45,282][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 03:04:45,786][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 03:04:46,289][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 03:04:46,791][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 03:04:47,294][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 03:04:47,794][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 03:04:48,300][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 03:04:48,800][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 03:04:49,302][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 03:04:49,803][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 03:04:50,307][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 03:04:50,825][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 03:04:51,329][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 03:04:51,835][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 03:04:52,340][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 03:04:52,843][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 03:04:53,347][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 03:04:53,850][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 03:04:54,353][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 03:04:54,857][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 03:04:55,358][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 03:04:55,974][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 03:04:56,477][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 03:04:56,981][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 03:04:57,486][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 03:04:57,991][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 03:04:58,497][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 03:04:59,003][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 03:04:59,509][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 03:05:00,034][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 03:05:00,536][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 03:05:01,040][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 03:05:01,549][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 03:05:02,050][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 03:05:02,558][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 03:05:03,062][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 03:05:03,564][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 03:05:04,071][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 03:05:04,574][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 03:05:05,075][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 03:05:05,578][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 03:05:06,080][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 03:05:06,584][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 03:05:07,087][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 03:05:07,592][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 03:05:08,092][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 03:05:08,597][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 03:05:09,097][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 03:05:09,598][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 03:05:10,099][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 03:05:10,611][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 03:05:11,113][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 03:05:11,614][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10878 tokens. [2025-11-13 03:05:12,328][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.21%, Current % of VRAM taken: 59.66%, Block Peak % of device VRAM: 62.41%, ΔTime: 00:00:33 [2025-11-13 03:05:13,148][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 03:05:13,150][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 03:05:13,152][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 03:05:14,211][__main__][INFO] - Iteration 342 took 52s (29.81% Gen, 68.16% Train). Generation: 15s, Training: 35s. Estimated remaining time: 38h 31m 16s. Estimated total time: 43h 26m 49s. Time estimates for 10 more iterations: 8m 41s, 100 more iterations: 1h 26m 53s, 500 more iterations: 7h 14m 28s. [2025-11-13 03:05:14,213][__main__][INFO] - Starting iteration 342. [2025-11-13 03:05:14,702][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 34 and human policies 1. [2025-11-13 03:05:14,703][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 03:05:23,656][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 03:05:26,174][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the values assigned to the items, a balanced approach seems best. We should consider Bob's high value for balls and our own high values for hats and books. A fair allocation, considering the values, would be: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 03:05:28,692][__main__][INFO] - Number of regex retries in iteration 342: 2 [2025-11-13 03:05:28,693][__main__][INFO] - agents played in iteration 342 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 03:05:29,596][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:05:29,621][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:05:29,645][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:05:29,667][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:05:29,668][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 03:05:29,668][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 03:05:30,351][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 03:05:30,809][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 03:05:31,318][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 03:05:31,832][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 03:05:32,340][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 03:05:32,854][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 03:05:33,362][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 03:05:33,869][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 03:05:34,375][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 03:05:34,876][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 03:05:35,379][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 03:05:46,935][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 03:05:47,454][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 03:05:47,958][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 03:05:48,463][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 03:05:48,972][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 03:05:49,474][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 03:05:49,978][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 03:05:50,486][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 03:05:50,990][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 03:05:51,500][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 03:05:52,004][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 03:05:52,507][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 03:05:53,024][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 03:05:53,526][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 03:05:54,044][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 03:05:54,547][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 03:05:55,051][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 03:05:55,554][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 03:05:56,055][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 03:05:56,562][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 03:05:57,066][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 03:05:57,568][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 03:05:58,073][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 03:05:58,575][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 03:05:59,079][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 03:05:59,582][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 03:06:00,087][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 03:06:00,596][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 03:06:01,098][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 03:06:01,602][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 03:06:02,110][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 03:06:02,613][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10879 tokens. [2025-11-13 03:06:03,300][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.29%, Current % of VRAM taken: 59.74%, Block Peak % of device VRAM: 62.35%, ΔTime: 00:00:32 [2025-11-13 03:06:04,086][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 03:06:04,088][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 03:06:04,090][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 03:06:05,079][__main__][INFO] - Iteration 343 took 50s (27.77% Gen, 70.26% Train). Generation: 13s, Training: 35s. Estimated remaining time: 37h 2m 31s. Estimated total time: 41h 58m 54s. Time estimates for 10 more iterations: 8m 23s, 100 more iterations: 1h 23m 57s, 500 more iterations: 6h 59m 49s. [2025-11-13 03:06:05,081][__main__][INFO] - Starting iteration 343. [2025-11-13 03:06:05,549][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 34 and human policies 1. [2025-11-13 03:06:05,550][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 03:06:17,821][__main__][INFO] - Number of regex retries in iteration 343: 0 [2025-11-13 03:06:17,822][__main__][INFO] - agents played in iteration 343 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 03:06:18,675][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:06:18,704][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:06:18,732][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:06:18,756][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:06:18,757][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 03:06:18,758][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 03:06:19,444][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 03:06:19,903][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 03:06:20,412][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 03:06:20,914][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 03:06:21,416][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 03:06:21,918][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 03:06:22,420][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 03:06:22,934][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 03:06:23,438][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 03:06:23,941][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 03:06:24,446][mllm.training.trainer_common][INFO] - 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Processing mini-batch 21 of 64 [2025-11-13 03:06:30,504][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 03:06:31,011][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 03:06:31,516][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 03:06:32,022][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 03:06:32,538][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 03:06:33,042][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 03:06:33,555][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 03:06:34,058][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 03:06:34,560][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 03:06:35,063][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 03:06:35,567][mllm.training.trainer_common][INFO] - 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Processing mini-batch 43 of 64 [2025-11-13 03:06:41,580][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 03:06:42,080][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 03:06:42,583][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 03:06:43,085][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 03:06:43,587][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 03:06:44,093][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 03:06:44,597][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 03:06:45,100][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 03:06:45,603][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 03:06:46,106][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 03:06:46,611][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 03:06:47,115][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 03:06:47,620][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 03:06:48,121][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 03:06:48,622][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 03:06:49,124][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 03:06:49,623][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 03:06:50,126][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 03:06:50,628][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 03:06:51,129][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 03:06:51,631][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10809 tokens. [2025-11-13 03:06:52,353][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.18%, Current % of VRAM taken: 59.63%, Block Peak % of device VRAM: 62.32%, ΔTime: 00:00:32 [2025-11-13 03:06:53,133][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 03:06:53,135][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 03:06:53,136][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 03:06:54,191][__main__][INFO] - Iteration 344 took 48s (25.23% Gen, 72.60% Train). Generation: 12s, Training: 35s. Estimated remaining time: 35h 34m 54s. Estimated total time: 40h 32m 7s. Time estimates for 10 more iterations: 8m 6s, 100 more iterations: 1h 21m 4s, 500 more iterations: 6h 45m 21s. [2025-11-13 03:06:54,193][__main__][INFO] - Starting iteration 344. [2025-11-13 03:06:54,665][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 34 and human policies 1. [2025-11-13 03:06:54,666][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 03:07:01,970][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 03:07:05,126][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 0 balls Given the values: - You value hats at 10, books at 1, and balls at 1. - Bob values hats at 1, books at 10, and balls at 10. Since the items are split proportionally and you have the highest value for hats, it makes strategic sense to propose keeping all 10 hats. This way, you maximize your potential points from hats, which have the highest value to you. The books and balls, which you value less, might be better留给Bob，因为他对这些物品的价值更高。 did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 03:07:11,213][__main__][INFO] - Number of regex retries in iteration 344: 2 [2025-11-13 03:07:11,214][__main__][INFO] - agents played in iteration 344 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 03:07:12,049][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:07:12,071][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:07:12,093][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:07:12,114][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:07:12,115][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 03:07:12,116][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 03:07:12,787][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 03:07:13,251][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 03:07:13,760][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 03:07:14,262][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 03:07:14,763][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 03:07:15,265][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 03:07:15,780][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 03:07:16,281][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 03:07:16,783][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 03:07:17,283][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 03:07:17,783][mllm.training.trainer_common][INFO] - 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Processing mini-batch 43 of 64 [2025-11-13 03:07:34,883][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 03:07:35,391][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 03:07:35,897][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 03:07:36,401][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 03:07:36,908][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 03:07:37,413][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 03:07:37,916][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 03:07:38,419][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 03:07:38,924][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 03:07:39,430][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 03:07:39,940][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 03:07:40,444][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 03:07:40,949][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 03:07:41,452][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 03:07:41,956][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 03:07:42,459][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 03:07:42,963][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 03:07:43,479][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 03:07:43,981][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 03:07:44,485][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 03:07:44,990][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10879 tokens. [2025-11-13 03:07:45,719][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.23%, Current % of VRAM taken: 59.69%, Block Peak % of device VRAM: 62.42%, ΔTime: 00:00:32 [2025-11-13 03:07:46,468][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 03:07:46,470][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 03:07:46,472][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 03:07:47,610][__main__][INFO] - Iteration 345 took 52s (31.25% Gen, 66.59% Train). Generation: 16s, Training: 35s. Estimated remaining time: 39h 9m 12s. Estimated total time: 44h 7m 18s. Time estimates for 10 more iterations: 8m 49s, 100 more iterations: 1h 28m 14s, 500 more iterations: 7h 21m 13s. [2025-11-13 03:07:47,612][__main__][INFO] - Starting iteration 345. [2025-11-13 03:07:48,081][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 34 and human policies 1. [2025-11-13 03:07:48,082][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 03:07:57,338][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 03:08:02,654][__main__][INFO] - Number of regex retries in iteration 345: 1 [2025-11-13 03:08:02,655][__main__][INFO] - agents played in iteration 345 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 03:08:03,492][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:08:03,519][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:08:03,545][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:08:03,568][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:08:03,569][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 03:08:03,570][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 03:08:04,286][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 03:08:04,745][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 03:08:05,256][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 03:08:05,758][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 03:08:06,261][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 03:08:06,760][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 03:08:07,259][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 03:08:07,758][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 03:08:08,261][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 03:08:08,780][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 03:08:09,280][mllm.training.trainer_common][INFO] - 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Processing mini-batch 43 of 64 [2025-11-13 03:08:26,391][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 03:08:26,893][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 03:08:27,392][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 03:08:27,892][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 03:08:28,392][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 03:08:28,893][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 03:08:29,394][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 03:08:29,895][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 03:08:30,394][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 03:08:30,895][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 03:08:31,394][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 03:08:31,895][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 03:08:32,400][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 03:08:32,903][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 03:08:33,407][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 03:08:33,912][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 03:08:34,416][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 03:08:34,922][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 03:08:35,428][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 03:08:35,934][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 03:08:36,448][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10878 tokens. [2025-11-13 03:08:37,170][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.26%, Current % of VRAM taken: 59.71%, Block Peak % of device VRAM: 62.31%, ΔTime: 00:00:32 [2025-11-13 03:08:37,931][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 03:08:37,932][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 03:08:37,934][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 03:08:38,921][__main__][INFO] - Iteration 346 took 50s (28.66% Gen, 69.39% Train). Generation: 14s, Training: 35s. Estimated remaining time: 37h 23m 4s. Estimated total time: 42h 22m 1s. Time estimates for 10 more iterations: 8m 28s, 100 more iterations: 1h 24m 44s, 500 more iterations: 7h 3m 40s. [2025-11-13 03:08:38,923][__main__][INFO] - Starting iteration 346. [2025-11-13 03:08:39,406][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 34 and human policies 1. [2025-11-13 03:08:39,407][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 03:08:55,663][__main__][INFO] - Number of regex retries in iteration 346: 0 [2025-11-13 03:08:55,664][__main__][INFO] - agents played in iteration 346 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 03:08:56,514][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:08:56,541][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:08:56,567][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:08:56,590][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:08:56,590][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 03:08:56,591][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 03:08:57,264][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 03:08:57,724][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 03:08:58,232][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 03:08:58,735][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 03:08:59,239][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 03:08:59,742][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 03:09:00,243][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 03:09:00,747][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 03:09:01,248][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 03:09:01,748][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 03:09:02,247][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 03:09:13,843][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 03:09:14,353][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 03:09:14,857][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 03:09:15,360][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 03:09:15,862][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 03:09:16,363][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 03:09:16,864][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 03:09:17,366][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 03:09:17,870][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 03:09:18,392][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 03:09:18,893][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 03:09:19,396][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 03:09:19,895][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 03:09:20,396][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 03:09:20,899][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 03:09:21,401][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 03:09:21,903][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 03:09:22,403][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 03:09:22,904][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 03:09:23,405][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 03:09:23,904][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 03:09:24,407][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 03:09:24,910][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 03:09:25,413][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 03:09:25,915][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 03:09:26,415][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 03:09:26,917][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 03:09:27,419][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 03:09:27,920][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 03:09:28,422][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 03:09:28,928][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 03:09:29,434][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10876 tokens. [2025-11-13 03:09:30,222][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.24%, Current % of VRAM taken: 59.70%, Block Peak % of device VRAM: 62.31%, ΔTime: 00:00:32 [2025-11-13 03:09:31,016][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 03:09:31,018][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 03:09:31,020][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 03:09:31,914][__main__][INFO] - Iteration 347 took 52s (30.96% Gen, 67.33% Train). Generation: 16s, Training: 35s. Estimated remaining time: 38h 45m 34s. Estimated total time: 43h 45m 25s. Time estimates for 10 more iterations: 8m 45s, 100 more iterations: 1h 27m 30s, 500 more iterations: 7h 17m 34s. [2025-11-13 03:09:31,916][__main__][INFO] - Starting iteration 347. [2025-11-13 03:09:32,396][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 34 and human policies 1. [2025-11-13 03:09:32,396][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 03:09:40,086][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 03:09:47,875][__main__][INFO] - Number of regex retries in iteration 347: 1 [2025-11-13 03:09:47,875][__main__][INFO] - agents played in iteration 347 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 03:09:48,730][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:09:48,755][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:09:48,781][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:09:48,803][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:09:48,804][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 03:09:48,805][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 03:09:49,555][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 03:09:50,015][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 03:09:50,527][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 03:09:51,034][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 03:09:51,538][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 03:09:52,043][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 03:09:52,550][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 03:09:53,054][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 03:09:53,560][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 03:09:54,062][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 03:09:54,563][mllm.training.trainer_common][INFO] - 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Processing mini-batch 54 of 64 [2025-11-13 03:10:17,235][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 03:10:17,736][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 03:10:18,239][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 03:10:18,739][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 03:10:19,241][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 03:10:19,741][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 03:10:20,242][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 03:10:20,746][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 03:10:21,247][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 03:10:21,748][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10875 tokens. [2025-11-13 03:10:22,505][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.22%, Current % of VRAM taken: 59.68%, Block Peak % of device VRAM: 62.29%, ΔTime: 00:00:32 [2025-11-13 03:10:23,298][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 03:10:23,300][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 03:10:23,302][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 03:10:24,266][__main__][INFO] - Iteration 348 took 51s (29.84% Gen, 68.30% Train). Generation: 15s, Training: 35s. Estimated remaining time: 38h 12m 48s. Estimated total time: 43h 13m 30s. Time estimates for 10 more iterations: 8m 38s, 100 more iterations: 1h 26m 27s, 500 more iterations: 7h 12m 15s. [2025-11-13 03:10:24,268][__main__][INFO] - Starting iteration 348. [2025-11-13 03:10:24,750][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 34 and human policies 1. [2025-11-13 03:10:24,751][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 03:10:34,876][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 03:10:41,623][__main__][INFO] - Number of regex retries in iteration 348: 1 [2025-11-13 03:10:41,624][__main__][INFO] - agents played in iteration 348 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 03:10:42,444][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:10:42,474][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:10:42,501][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:10:42,524][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:10:42,525][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 03:10:42,526][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 03:10:43,209][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 03:10:43,669][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 03:10:44,179][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 03:10:44,692][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 03:10:45,194][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 03:10:45,709][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 03:10:46,213][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 03:10:46,718][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 03:10:47,224][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 03:10:47,729][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 03:10:48,234][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 03:10:59,781][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 03:11:00,285][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 03:11:00,789][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 03:11:01,293][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 03:11:01,797][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 03:11:02,300][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 03:11:02,802][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 03:11:03,305][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 03:11:03,803][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 03:11:04,302][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 03:11:04,800][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 03:11:05,302][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 03:11:05,822][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 03:11:06,322][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 03:11:06,820][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 03:11:07,319][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 03:11:07,820][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 03:11:08,324][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 03:11:08,824][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 03:11:09,325][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 03:11:09,827][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 03:11:10,328][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 03:11:10,826][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 03:11:11,327][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 03:11:11,828][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 03:11:12,333][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 03:11:12,834][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 03:11:13,335][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 03:11:13,841][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 03:11:14,348][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 03:11:14,850][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 03:11:15,351][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10877 tokens. [2025-11-13 03:11:16,080][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.21%, Current % of VRAM taken: 59.66%, Block Peak % of device VRAM: 62.24%, ΔTime: 00:00:32 [2025-11-13 03:11:16,869][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 03:11:16,870][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 03:11:16,872][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 03:11:17,802][__main__][INFO] - Iteration 349 took 53s (31.80% Gen, 66.44% Train). Generation: 16s, Training: 35s. Estimated remaining time: 39h 11m 0s. Estimated total time: 44h 12m 36s. Time estimates for 10 more iterations: 8m 50s, 100 more iterations: 1h 28m 25s, 500 more iterations: 7h 22m 6s. [2025-11-13 03:11:17,804][__main__][INFO] - Starting iteration 349. [2025-11-13 03:11:18,269][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 34 and human policies 1. [2025-11-13 03:11:18,269][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 03:11:32,084][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 03:11:32,900][__main__][INFO] - Number of regex retries in iteration 349: 1 [2025-11-13 03:11:32,901][__main__][INFO] - agents played in iteration 349 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 03:11:33,722][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:11:33,748][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:11:33,773][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:11:33,796][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:11:33,797][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 03:11:33,797][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 03:11:34,480][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 03:11:34,936][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 03:11:35,442][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 03:11:35,948][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 03:11:36,451][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 03:11:36,955][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 03:11:37,456][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 03:11:37,958][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 03:11:38,460][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 03:11:38,961][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 03:11:39,465][mllm.training.trainer_common][INFO] - 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Processing mini-batch 54 of 64 [2025-11-13 03:12:02,141][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 03:12:02,643][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 03:12:03,148][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 03:12:03,651][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 03:12:04,154][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 03:12:04,658][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 03:12:05,161][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 03:12:05,662][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 03:12:06,166][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 03:12:06,670][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10875 tokens. [2025-11-13 03:12:07,384][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.26%, Current % of VRAM taken: 59.71%, Block Peak % of device VRAM: 62.31%, ΔTime: 00:00:32 [2025-11-13 03:12:08,170][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 03:12:08,171][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 03:12:08,173][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 03:12:09,178][__main__][INFO] - Iteration 350 took 50s (28.74% Gen, 69.28% Train). Generation: 14s, Training: 35s. Estimated remaining time: 37h 23m 0s. Estimated total time: 42h 25m 28s. Time estimates for 10 more iterations: 8m 29s, 100 more iterations: 1h 24m 50s, 500 more iterations: 7h 4m 14s. [2025-11-13 03:12:09,180][__main__][INFO] - Starting iteration 350. [2025-11-13 03:12:09,679][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 34 and human policies 1. [2025-11-13 03:12:09,679][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 03:12:23,901][__main__][INFO] - Number of regex retries in iteration 350: 0 [2025-11-13 03:12:23,902][__main__][INFO] - agents played in iteration 350 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 03:12:24,708][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:12:24,732][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:12:24,755][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:12:24,777][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:12:24,778][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 03:12:24,779][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 03:12:25,485][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 03:12:25,942][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 03:12:26,449][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 03:12:26,952][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 03:12:27,455][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 03:12:27,958][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 03:12:28,459][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 03:12:28,965][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 03:12:29,472][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 03:12:29,978][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 03:12:30,481][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 03:12:42,077][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 03:12:42,579][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 03:12:43,081][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 03:12:43,585][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 03:12:44,093][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 03:12:44,597][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 03:12:45,106][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 03:12:45,615][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 03:12:46,117][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 03:12:46,638][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 03:12:47,143][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 03:12:47,649][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 03:12:48,151][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 03:12:48,654][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 03:12:49,160][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 03:12:49,664][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 03:12:50,166][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 03:12:50,673][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 03:12:51,174][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 03:12:51,678][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 03:12:52,179][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 03:12:52,684][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 03:12:53,187][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 03:12:53,689][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 03:12:54,193][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 03:12:54,695][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 03:12:55,196][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 03:12:55,707][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 03:12:56,211][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 03:12:56,714][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 03:12:57,222][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 03:12:57,726][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10809 tokens. [2025-11-13 03:12:58,431][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.17%, Current % of VRAM taken: 59.63%, Block Peak % of device VRAM: 62.34%, ΔTime: 00:00:32 [2025-11-13 03:12:59,210][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 03:12:59,211][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 03:12:59,213][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 03:13:01,593][__main__][INFO] - Iteration 351 took 51s (27.40% Gen, 68.02% Train). Generation: 14s, Training: 35s. Estimated remaining time: 38h 12m 23s. Estimated total time: 43h 15m 43s. Time estimates for 10 more iterations: 8m 39s, 100 more iterations: 1h 26m 31s, 500 more iterations: 7h 12m 37s. [2025-11-13 03:13:01,595][__main__][INFO] - Starting iteration 351. [2025-11-13 03:13:02,096][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 35 and human policies 1. [2025-11-13 03:13:02,096][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 03:13:16,699][__main__][INFO] - Number of regex retries in iteration 351: 0 [2025-11-13 03:13:16,699][__main__][INFO] - agents played in iteration 351 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 03:13:17,488][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:13:17,511][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:13:17,534][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:13:17,556][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:13:17,556][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 03:13:17,557][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 03:13:18,283][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 03:13:18,740][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 03:13:19,255][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 03:13:19,758][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 03:13:20,263][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 03:13:20,763][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 03:13:21,264][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 03:13:21,763][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 03:13:22,264][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 03:13:22,771][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 03:13:23,275][mllm.training.trainer_common][INFO] - 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Processing mini-batch 43 of 64 [2025-11-13 03:13:40,366][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 03:13:40,867][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 03:13:41,372][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 03:13:41,873][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 03:13:42,375][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 03:13:42,877][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 03:13:43,379][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 03:13:43,882][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 03:13:44,387][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 03:13:44,894][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 03:13:45,394][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 03:13:45,896][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 03:13:46,397][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 03:13:46,897][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 03:13:47,398][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 03:13:47,911][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 03:13:48,411][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 03:13:48,917][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 03:13:49,418][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 03:13:49,918][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 03:13:50,423][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10878 tokens. [2025-11-13 03:13:51,120][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.20%, Current % of VRAM taken: 59.65%, Block Peak % of device VRAM: 62.24%, ΔTime: 00:00:32 [2025-11-13 03:13:51,908][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 03:13:51,910][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 03:13:51,912][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 03:13:52,930][__main__][INFO] - Iteration 352 took 50s (28.72% Gen, 69.27% Train). Generation: 14s, Training: 35s. Estimated remaining time: 37h 17m 32s. Estimated total time: 42h 21m 44s. Time estimates for 10 more iterations: 8m 28s, 100 more iterations: 1h 24m 43s, 500 more iterations: 7h 3m 37s. [2025-11-13 03:13:52,932][__main__][INFO] - Starting iteration 352. [2025-11-13 03:13:53,398][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 35 and human policies 1. [2025-11-13 03:13:53,400][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 03:13:57,273][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 03:14:06,258][__main__][INFO] - Number of regex retries in iteration 352: 1 [2025-11-13 03:14:06,259][__main__][INFO] - agents played in iteration 352 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 03:14:07,133][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:14:07,156][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:14:07,179][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:14:07,201][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:14:07,202][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 03:14:07,203][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 03:14:07,941][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 03:14:08,401][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 03:14:08,912][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 03:14:09,418][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 03:14:09,923][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 03:14:10,431][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 03:14:10,934][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 03:14:11,437][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 03:14:11,941][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 03:14:12,444][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 03:14:12,948][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 03:14:24,522][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 03:14:25,023][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 03:14:25,522][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 03:14:26,021][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 03:14:26,521][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 03:14:27,020][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 03:14:27,523][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 03:14:28,023][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 03:14:28,524][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 03:14:29,024][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 03:14:29,525][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 03:14:30,026][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 03:14:30,529][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 03:14:31,039][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 03:14:31,544][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 03:14:32,046][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 03:14:32,564][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 03:14:33,064][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 03:14:33,567][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 03:14:34,068][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 03:14:34,571][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 03:14:35,074][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 03:14:35,576][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 03:14:36,078][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 03:14:36,583][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 03:14:37,085][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 03:14:37,588][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 03:14:38,089][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 03:14:38,592][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 03:14:39,095][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 03:14:39,596][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 03:14:40,096][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10880 tokens. [2025-11-13 03:14:40,804][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.18%, Current % of VRAM taken: 59.63%, Block Peak % of device VRAM: 62.31%, ΔTime: 00:00:32 [2025-11-13 03:14:41,571][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 03:14:41,573][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 03:14:41,576][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 03:14:42,513][__main__][INFO] - Iteration 353 took 49s (26.18% Gen, 71.90% Train). Generation: 12s, Training: 35s. Estimated remaining time: 35h 50m 47s. Estimated total time: 40h 55m 48s. Time estimates for 10 more iterations: 8m 11s, 100 more iterations: 1h 21m 51s, 500 more iterations: 6h 49m 18s. [2025-11-13 03:14:42,515][__main__][INFO] - Starting iteration 353. [2025-11-13 03:14:43,004][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 35 and human policies 1. [2025-11-13 03:14:43,005][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 03:14:47,822][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 03:14:59,601][__main__][INFO] - Number of regex retries in iteration 353: 1 [2025-11-13 03:14:59,601][__main__][INFO] - agents played in iteration 353 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 03:15:00,400][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:15:00,423][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:15:00,446][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:15:00,468][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:15:00,469][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 03:15:00,470][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 03:15:01,169][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 03:15:01,639][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 03:15:02,143][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 03:15:02,657][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 03:15:03,158][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 03:15:03,662][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 03:15:04,177][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 03:15:04,681][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 03:15:05,186][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 03:15:05,689][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 03:15:06,194][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 03:15:06,698][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 03:15:07,199][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 03:15:07,704][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 03:15:08,204][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 03:15:08,705][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 03:15:09,209][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 03:15:09,717][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 03:15:10,223][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 03:15:10,738][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 03:15:11,241][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 03:15:11,756][mllm.training.trainer_common][INFO] - 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Processing mini-batch 43 of 64 [2025-11-13 03:15:23,326][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 03:15:23,834][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 03:15:24,334][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 03:15:24,840][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 03:15:25,345][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 03:15:25,850][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 03:15:26,358][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 03:15:26,859][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 03:15:27,359][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 03:15:27,863][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 03:15:28,369][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 03:15:28,882][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 03:15:29,386][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 03:15:29,916][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 03:15:30,425][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 03:15:30,930][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 03:15:31,439][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 03:15:31,942][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 03:15:32,446][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 03:15:32,954][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 03:15:33,457][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10869 tokens. [2025-11-13 03:15:34,147][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.30%, Current % of VRAM taken: 59.75%, Block Peak % of device VRAM: 62.41%, ΔTime: 00:00:32 [2025-11-13 03:15:34,911][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 03:15:34,913][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 03:15:34,915][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 03:15:35,862][__main__][INFO] - Iteration 354 took 52s (31.40% Gen, 66.81% Train). Generation: 16s, Training: 35s. Estimated remaining time: 38h 56m 58s. Estimated total time: 44h 2m 53s. Time estimates for 10 more iterations: 8m 48s, 100 more iterations: 1h 28m 5s, 500 more iterations: 7h 20m 28s. [2025-11-13 03:15:35,864][__main__][INFO] - Starting iteration 354. [2025-11-13 03:15:36,345][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 35 and human policies 1. [2025-11-13 03:15:36,346][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 03:15:49,089][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 03:15:49,906][__main__][INFO] - Number of regex retries in iteration 354: 1 [2025-11-13 03:15:49,906][__main__][INFO] - agents played in iteration 354 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 03:15:50,775][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:15:50,800][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:15:50,826][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:15:50,849][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:15:50,850][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 03:15:50,851][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 03:15:51,565][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 03:15:52,023][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 03:15:52,528][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 03:15:53,033][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 03:15:53,536][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 03:15:54,052][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 03:15:54,555][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 03:15:55,059][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 03:15:55,561][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 03:15:56,062][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 03:15:56,580][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 03:16:08,158][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 03:16:08,657][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 03:16:09,156][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 03:16:09,669][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 03:16:10,166][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 03:16:10,664][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 03:16:11,165][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 03:16:11,663][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 03:16:12,180][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 03:16:12,681][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 03:16:13,182][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 03:16:13,693][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 03:16:14,195][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 03:16:14,698][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 03:16:15,199][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 03:16:15,700][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 03:16:16,203][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 03:16:16,703][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 03:16:17,203][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 03:16:17,704][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 03:16:18,203][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 03:16:18,705][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 03:16:19,204][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 03:16:19,702][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 03:16:20,211][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 03:16:20,716][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 03:16:21,220][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 03:16:21,722][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 03:16:22,224][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 03:16:22,735][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 03:16:23,240][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 03:16:23,743][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10880 tokens. [2025-11-13 03:16:24,504][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.23%, Current % of VRAM taken: 59.68%, Block Peak % of device VRAM: 62.31%, ΔTime: 00:00:32 [2025-11-13 03:16:25,275][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 03:16:25,277][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 03:16:25,280][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 03:16:26,464][__main__][INFO] - Iteration 355 took 50s (27.06% Gen, 70.58% Train). Generation: 13s, Training: 35s. Estimated remaining time: 36h 39m 14s. Estimated total time: 41h 45m 58s. Time estimates for 10 more iterations: 8m 21s, 100 more iterations: 1h 23m 31s, 500 more iterations: 6h 57m 39s. [2025-11-13 03:16:26,466][__main__][INFO] - Starting iteration 355. [2025-11-13 03:16:26,952][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 35 and human policies 1. [2025-11-13 03:16:26,952][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 03:16:33,644][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 03:16:43,327][__main__][INFO] - Number of regex retries in iteration 355: 1 [2025-11-13 03:16:43,327][__main__][INFO] - agents played in iteration 355 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 03:16:44,123][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:16:44,145][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:16:44,168][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:16:44,190][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:16:44,191][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 03:16:44,192][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 03:16:44,900][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 03:16:45,371][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 03:16:45,878][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 03:16:46,395][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 03:16:46,896][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 03:16:47,397][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 03:16:47,908][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 03:16:48,409][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 03:16:48,915][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 03:16:49,420][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 03:16:49,925][mllm.training.trainer_common][INFO] - 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Processing mini-batch 43 of 64 [2025-11-13 03:17:07,040][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 03:17:07,540][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 03:17:08,039][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 03:17:08,541][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 03:17:09,043][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 03:17:09,544][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 03:17:10,045][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 03:17:10,546][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 03:17:11,046][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 03:17:11,549][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 03:17:12,049][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 03:17:12,550][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 03:17:13,055][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 03:17:13,557][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 03:17:14,074][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 03:17:14,577][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 03:17:15,078][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 03:17:15,589][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 03:17:16,094][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 03:17:16,604][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 03:17:17,109][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10879 tokens. [2025-11-13 03:17:17,835][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.23%, Current % of VRAM taken: 59.68%, Block Peak % of device VRAM: 62.24%, ΔTime: 00:00:32 [2025-11-13 03:17:18,585][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 03:17:18,587][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 03:17:18,589][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 03:17:19,466][__main__][INFO] - Iteration 356 took 52s (31.18% Gen, 67.15% Train). Generation: 16s, Training: 35s. Estimated remaining time: 38h 38m 4s. Estimated total time: 43h 45m 42s. Time estimates for 10 more iterations: 8m 45s, 100 more iterations: 1h 27m 31s, 500 more iterations: 7h 17m 37s. [2025-11-13 03:17:19,468][__main__][INFO] - Starting iteration 356. [2025-11-13 03:17:19,935][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 35 and human policies 1. [2025-11-13 03:17:19,936][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 03:17:26,457][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 03:17:34,356][__main__][INFO] - Number of regex retries in iteration 356: 1 [2025-11-13 03:17:34,356][__main__][INFO] - agents played in iteration 356 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 03:17:35,163][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:17:35,186][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:17:35,209][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:17:35,231][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:17:35,232][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 03:17:35,233][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 03:17:36,007][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 03:17:36,487][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 03:17:36,997][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 03:17:37,503][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 03:17:38,004][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 03:17:38,510][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 03:17:39,018][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 03:17:39,521][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 03:17:40,023][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 03:17:40,526][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 03:17:41,028][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 03:17:41,534][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 03:17:42,036][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 03:17:42,539][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 03:17:43,045][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 03:17:43,547][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 03:17:44,049][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 03:17:44,558][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 03:17:45,061][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 03:17:45,563][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 03:17:46,066][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 03:17:46,571][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 03:17:47,075][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 03:17:47,577][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 03:17:48,078][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 03:17:48,591][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 03:17:49,094][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 03:17:49,618][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 03:17:50,122][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 03:17:50,626][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 03:17:51,131][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 03:17:51,636][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 03:17:52,144][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 03:17:52,650][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 03:17:53,156][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 03:17:53,658][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 03:17:54,161][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 03:17:54,665][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 03:17:55,168][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 03:17:55,670][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 03:17:56,177][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 03:17:56,680][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 03:17:57,182][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 03:17:57,689][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 03:17:58,190][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 03:17:58,708][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 03:17:59,211][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 03:17:59,712][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 03:18:00,223][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 03:18:00,729][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 03:18:01,235][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 03:18:01,738][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 03:18:02,239][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 03:18:02,747][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 03:18:03,249][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 03:18:03,751][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 03:18:04,254][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 03:18:04,754][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 03:18:05,257][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 03:18:05,756][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 03:18:06,257][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 03:18:06,759][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 03:18:07,259][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 03:18:07,759][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 03:18:08,260][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10879 tokens. [2025-11-13 03:18:09,001][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.17%, Current % of VRAM taken: 59.62%, Block Peak % of device VRAM: 62.31%, ΔTime: 00:00:33 [2025-11-13 03:18:09,781][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 03:18:09,783][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 03:18:09,785][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 03:18:10,735][__main__][INFO] - Iteration 357 took 50s (28.39% Gen, 69.74% Train). Generation: 14s, Training: 35s. Estimated remaining time: 37h 11m 32s. Estimated total time: 42h 20m 1s. Time estimates for 10 more iterations: 8m 28s, 100 more iterations: 1h 24m 40s, 500 more iterations: 7h 3m 20s. [2025-11-13 03:18:10,737][__main__][INFO] - Starting iteration 357. [2025-11-13 03:18:11,210][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 35 and human policies 1. [2025-11-13 03:18:11,211][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 03:18:16,043][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 03:18:17,453][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 03:18:24,960][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 03:18:25,932][__main__][INFO] - Number of regex retries in iteration 357: 3 [2025-11-13 03:18:25,933][__main__][INFO] - agents played in iteration 357 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 03:18:26,735][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:18:26,763][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:18:26,789][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:18:26,811][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:18:26,812][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 03:18:26,813][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 03:18:27,528][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 03:18:27,989][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 03:18:28,503][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 03:18:29,013][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 03:18:29,518][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 03:18:30,024][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 03:18:30,536][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 03:18:31,043][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 03:18:31,549][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 03:18:32,051][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 03:18:32,552][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 03:18:33,054][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 03:18:33,556][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 03:18:34,057][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 03:18:34,557][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 03:18:35,059][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 03:18:35,560][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 03:18:36,062][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 03:18:36,564][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 03:18:37,067][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 03:18:37,569][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 03:18:38,073][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 03:18:38,575][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 03:18:39,077][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 03:18:39,582][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 03:18:40,085][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 03:18:40,587][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 03:18:41,089][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 03:18:41,594][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 03:18:42,109][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 03:18:42,613][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 03:18:43,117][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 03:18:43,620][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 03:18:44,124][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 03:18:44,635][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 03:18:45,139][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 03:18:45,638][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 03:18:46,144][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 03:18:46,648][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 03:18:47,149][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 03:18:47,653][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 03:18:48,157][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 03:18:48,661][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 03:18:49,165][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 03:18:49,668][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 03:18:50,173][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 03:18:50,677][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 03:18:51,179][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 03:18:51,681][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 03:18:52,185][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 03:18:52,703][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 03:18:53,209][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 03:18:53,712][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 03:18:54,217][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 03:18:54,721][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 03:18:55,231][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 03:18:55,734][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 03:18:56,238][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 03:18:56,746][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 03:18:57,249][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 03:18:57,751][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 03:18:58,253][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 03:18:58,757][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 03:18:59,264][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 03:18:59,766][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10880 tokens. [2025-11-13 03:19:00,492][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.26%, Current % of VRAM taken: 59.72%, Block Peak % of device VRAM: 62.31%, ΔTime: 00:00:32 [2025-11-13 03:19:01,287][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 03:19:01,289][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 03:19:01,290][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 03:19:02,559][__main__][INFO] - Iteration 358 took 51s (28.67% Gen, 68.86% Train). Generation: 14s, Training: 35s. Estimated remaining time: 37h 38m 7s. Estimated total time: 42h 47m 27s. Time estimates for 10 more iterations: 8m 33s, 100 more iterations: 1h 25m 34s, 500 more iterations: 7h 7m 54s. [2025-11-13 03:19:02,561][__main__][INFO] - Starting iteration 358. [2025-11-13 03:19:03,034][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 35 and human policies 1. [2025-11-13 03:19:03,035][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 03:19:08,103][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 03:19:09,178][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 03:19:10,034][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 03:19:15,565][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 03:19:17,547][__main__][INFO] - Number of regex retries in iteration 358: 4 [2025-11-13 03:19:17,547][__main__][INFO] - agents played in iteration 358 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 03:19:18,336][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:19:18,360][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:19:18,385][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:19:18,407][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:19:18,408][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 03:19:18,409][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 03:19:19,133][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 03:19:19,592][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 03:19:20,100][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 03:19:20,604][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 03:19:21,108][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 03:19:21,626][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 03:19:22,130][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 03:19:22,636][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 03:19:23,139][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 03:19:23,643][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 03:19:24,148][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 03:19:35,749][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 03:19:36,251][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 03:19:36,753][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 03:19:37,261][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 03:19:37,762][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 03:19:38,263][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 03:19:38,762][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 03:19:39,267][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 03:19:39,775][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 03:19:40,280][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 03:19:40,784][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 03:19:41,300][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 03:19:41,804][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 03:19:42,322][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 03:19:42,826][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 03:19:43,329][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 03:19:43,834][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 03:19:44,338][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 03:19:44,843][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 03:19:45,345][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 03:19:45,847][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 03:19:46,352][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 03:19:46,855][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 03:19:47,358][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 03:19:47,861][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 03:19:48,362][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 03:19:48,865][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 03:19:49,369][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 03:19:49,870][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 03:19:50,373][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 03:19:50,874][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 03:19:51,376][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10876 tokens. [2025-11-13 03:19:52,072][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.23%, Current % of VRAM taken: 59.68%, Block Peak % of device VRAM: 62.41%, ΔTime: 00:00:32 [2025-11-13 03:19:52,867][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 03:19:52,868][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 03:19:52,870][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 03:19:53,787][__main__][INFO] - Iteration 359 took 50s (28.59% Gen, 69.60% Train). Generation: 14s, Training: 35s. Estimated remaining time: 37h 7m 28s. Estimated total time: 42h 17m 40s. Time estimates for 10 more iterations: 8m 27s, 100 more iterations: 1h 24m 35s, 500 more iterations: 7h 2m 56s. [2025-11-13 03:19:53,789][__main__][INFO] - Starting iteration 359. [2025-11-13 03:19:54,284][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 35 and human policies 1. [2025-11-13 03:19:54,286][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 03:20:08,101][__main__][INFO] - Number of regex retries in iteration 359: 0 [2025-11-13 03:20:08,101][__main__][INFO] - agents played in iteration 359 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 03:20:08,901][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:20:08,928][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:20:08,954][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:20:08,977][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:20:08,978][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 03:20:08,979][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 03:20:09,656][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 03:20:10,113][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 03:20:10,620][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 03:20:11,143][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 03:20:11,646][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 03:20:12,152][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 03:20:12,657][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 03:20:13,160][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 03:20:13,669][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 03:20:14,175][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 03:20:14,681][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 03:20:15,184][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 03:20:15,687][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 03:20:16,190][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 03:20:16,697][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 03:20:17,205][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 03:20:17,714][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 03:20:18,217][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 03:20:18,719][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 03:20:19,224][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 03:20:19,727][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 03:20:20,241][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 03:20:26,270][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 03:20:26,771][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 03:20:27,274][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 03:20:27,776][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 03:20:28,279][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 03:20:28,781][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 03:20:29,282][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 03:20:29,783][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 03:20:30,285][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 03:20:30,789][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 03:20:31,288][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 03:20:31,789][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 03:20:32,292][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 03:20:32,795][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 03:20:33,299][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 03:20:33,799][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 03:20:34,300][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 03:20:34,802][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 03:20:35,306][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 03:20:35,810][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 03:20:36,325][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 03:20:36,829][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 03:20:37,341][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 03:20:37,842][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 03:20:38,344][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 03:20:38,849][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 03:20:39,352][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 03:20:39,855][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 03:20:40,365][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 03:20:40,867][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 03:20:41,370][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 03:20:41,872][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10878 tokens. [2025-11-13 03:20:42,575][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.25%, Current % of VRAM taken: 59.70%, Block Peak % of device VRAM: 62.35%, ΔTime: 00:00:32 [2025-11-13 03:20:43,348][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 03:20:43,350][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 03:20:43,352][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 03:20:44,401][__main__][INFO] - Iteration 360 took 50s (27.57% Gen, 70.34% Train). Generation: 13s, Training: 35s. Estimated remaining time: 36h 34m 47s. Estimated total time: 41h 45m 50s. Time estimates for 10 more iterations: 8m 21s, 100 more iterations: 1h 23m 31s, 500 more iterations: 6h 57m 38s. [2025-11-13 03:20:44,403][__main__][INFO] - Starting iteration 360. [2025-11-13 03:20:44,886][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 35 and human policies 1. [2025-11-13 03:20:44,886][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 03:20:50,186][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 03:20:51,087][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 03:20:53,104][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 03:20:53,199][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 03:21:01,345][__main__][INFO] - Number of regex retries in iteration 360: 4 [2025-11-13 03:21:01,345][__main__][INFO] - agents played in iteration 360 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 03:21:02,188][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:21:02,211][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:21:02,234][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:21:02,256][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:21:02,257][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 03:21:02,258][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 03:21:02,922][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 03:21:03,379][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 03:21:03,890][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 03:21:04,393][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 03:21:04,898][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 03:21:05,399][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 03:21:05,904][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 03:21:06,423][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 03:21:06,927][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 03:21:07,443][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 03:21:07,947][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 03:21:08,448][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 03:21:08,956][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 03:21:09,460][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 03:21:09,963][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 03:21:10,471][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 03:21:10,975][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 03:21:11,480][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 03:21:11,984][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 03:21:12,489][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 03:21:12,992][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 03:21:13,498][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 03:21:14,002][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 03:21:14,507][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 03:21:15,007][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 03:21:15,509][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 03:21:16,011][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 03:21:16,513][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 03:21:17,028][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 03:21:17,529][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 03:21:18,029][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 03:21:18,529][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 03:21:19,030][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 03:21:19,545][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 03:21:20,045][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 03:21:20,547][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 03:21:21,048][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 03:21:21,548][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 03:21:22,050][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 03:21:22,552][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 03:21:23,054][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 03:21:23,565][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 03:21:24,067][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 03:21:24,569][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 03:21:25,072][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 03:21:25,575][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 03:21:26,081][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 03:21:26,585][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 03:21:27,087][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 03:21:27,588][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 03:21:28,088][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 03:21:28,590][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 03:21:29,090][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 03:21:29,591][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 03:21:30,098][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 03:21:30,603][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 03:21:31,105][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 03:21:31,612][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 03:21:32,116][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 03:21:32,635][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 03:21:33,138][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 03:21:33,642][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 03:21:34,149][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 03:21:34,655][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 03:21:35,164][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10879 tokens. [2025-11-13 03:21:35,895][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.25%, Current % of VRAM taken: 59.71%, Block Peak % of device VRAM: 62.28%, ΔTime: 00:00:32 [2025-11-13 03:21:36,669][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 03:21:36,670][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 03:21:36,672][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 03:21:38,453][__main__][INFO] - Iteration 361 took 53s (30.73% Gen, 65.95% Train). Generation: 16s, Training: 35s. Estimated remaining time: 39h 26m 27s. Estimated total time: 44h 38m 23s. Time estimates for 10 more iterations: 8m 55s, 100 more iterations: 1h 29m 16s, 500 more iterations: 7h 26m 23s. [2025-11-13 03:21:38,456][__main__][INFO] - Starting iteration 361. [2025-11-13 03:21:38,929][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 36 and human policies 1. [2025-11-13 03:21:38,930][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 03:21:49,399][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 03:21:49,400][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 03:21:56,232][__main__][INFO] - Number of regex retries in iteration 361: 2 [2025-11-13 03:21:56,232][__main__][INFO] - agents played in iteration 361 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 03:21:57,068][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:21:57,093][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:21:57,118][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:21:57,142][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:21:57,142][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 03:21:57,143][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 03:21:57,836][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 03:21:58,294][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 03:21:58,806][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 03:21:59,312][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 03:21:59,818][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 03:22:00,325][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 03:22:00,831][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 03:22:01,352][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 03:22:01,860][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 03:22:02,366][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 03:22:02,872][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 03:22:14,460][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 03:22:14,961][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 03:22:15,462][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 03:22:15,964][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 03:22:16,465][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 03:22:16,967][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 03:22:17,470][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 03:22:17,973][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 03:22:18,476][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 03:22:18,976][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 03:22:19,475][mllm.training.trainer_common][INFO] - 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Processing mini-batch 54 of 64 [2025-11-13 03:22:25,502][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 03:22:26,010][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 03:22:26,523][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 03:22:27,030][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 03:22:27,536][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 03:22:28,039][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 03:22:28,542][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 03:22:29,044][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 03:22:29,547][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 03:22:30,049][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10871 tokens. [2025-11-13 03:22:30,790][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.21%, Current % of VRAM taken: 59.66%, Block Peak % of device VRAM: 62.32%, ΔTime: 00:00:32 [2025-11-13 03:22:31,555][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 03:22:31,557][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 03:22:31,560][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 03:22:32,461][__main__][INFO] - Iteration 362 took 53s (32.32% Gen, 66.00% Train). Generation: 17s, Training: 35s. Estimated remaining time: 39h 23m 45s. Estimated total time: 44h 36m 36s. Time estimates for 10 more iterations: 8m 55s, 100 more iterations: 1h 29m 13s, 500 more iterations: 7h 26m 6s. [2025-11-13 03:22:32,463][__main__][INFO] - Starting iteration 362. [2025-11-13 03:22:32,953][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 36 and human policies 1. [2025-11-13 03:22:32,954][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 03:22:43,731][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 03:22:49,932][__main__][INFO] - Number of regex retries in iteration 362: 1 [2025-11-13 03:22:49,932][__main__][INFO] - agents played in iteration 362 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 03:22:50,774][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:22:50,803][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:22:50,830][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:22:50,853][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:22:50,854][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 03:22:50,855][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 03:22:51,556][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 03:22:52,016][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 03:22:52,522][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 03:22:53,023][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 03:22:53,527][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 03:22:54,056][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 03:22:54,559][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 03:22:55,069][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 03:22:55,577][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 03:22:56,146][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 03:22:56,669][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 03:23:08,292][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 03:23:08,797][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 03:23:09,301][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 03:23:09,804][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 03:23:10,306][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 03:23:10,806][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 03:23:11,309][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 03:23:11,811][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 03:23:12,313][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 03:23:12,817][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 03:23:13,324][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 03:23:13,836][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 03:23:14,342][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 03:23:14,844][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 03:23:15,349][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 03:23:15,852][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 03:23:16,358][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 03:23:16,862][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 03:23:17,364][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 03:23:17,874][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 03:23:18,376][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 03:23:18,878][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 03:23:19,378][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 03:23:19,879][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 03:23:20,383][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 03:23:20,883][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 03:23:21,386][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 03:23:21,891][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 03:23:22,396][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 03:23:22,903][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 03:23:23,407][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 03:23:23,909][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10876 tokens. [2025-11-13 03:23:24,679][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.23%, Current % of VRAM taken: 59.68%, Block Peak % of device VRAM: 62.28%, ΔTime: 00:00:33 [2025-11-13 03:23:25,450][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 03:23:25,451][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 03:23:25,456][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 03:23:26,304][__main__][INFO] - Iteration 363 took 53s (31.82% Gen, 66.58% Train). Generation: 16s, Training: 35s. Estimated remaining time: 39h 13m 50s. Estimated total time: 44h 27m 35s. Time estimates for 10 more iterations: 8m 53s, 100 more iterations: 1h 28m 55s, 500 more iterations: 7h 24m 35s. [2025-11-13 03:23:26,306][__main__][INFO] - Starting iteration 363. [2025-11-13 03:23:26,804][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 36 and human policies 1. [2025-11-13 03:23:26,805][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 03:23:35,337][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 03:23:36,331][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 03:23:43,057][__main__][INFO] - Number of regex retries in iteration 363: 2 [2025-11-13 03:23:43,057][__main__][INFO] - agents played in iteration 363 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 03:23:43,919][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:23:43,947][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:23:43,974][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:23:43,999][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:23:44,000][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 03:23:44,000][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 03:23:44,741][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 03:23:45,201][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 03:23:45,709][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 03:23:46,210][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 03:23:46,714][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 03:23:47,217][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 03:23:47,719][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 03:23:48,221][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 03:23:48,723][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 03:23:49,235][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 03:23:49,741][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 03:24:01,387][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 03:24:01,896][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 03:24:02,399][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 03:24:02,901][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 03:24:03,404][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 03:24:03,908][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 03:24:04,409][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 03:24:04,912][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 03:24:05,435][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 03:24:05,941][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 03:24:06,458][mllm.training.trainer_common][INFO] - 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Processing mini-batch 54 of 64 [2025-11-13 03:24:12,501][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 03:24:13,006][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 03:24:13,508][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 03:24:14,008][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 03:24:14,510][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 03:24:15,015][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 03:24:15,520][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 03:24:16,023][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 03:24:16,524][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 03:24:17,034][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10879 tokens. [2025-11-13 03:24:17,789][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.29%, Current % of VRAM taken: 59.74%, Block Peak % of device VRAM: 62.41%, ΔTime: 00:00:33 [2025-11-13 03:24:18,586][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 03:24:18,587][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 03:24:18,590][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 03:24:19,510][__main__][INFO] - Iteration 364 took 52s (30.83% Gen, 67.42% Train). Generation: 16s, Training: 35s. Estimated remaining time: 38h 40m 42s. Estimated total time: 43h 55m 20s. Time estimates for 10 more iterations: 8m 47s, 100 more iterations: 1h 27m 50s, 500 more iterations: 7h 19m 13s. [2025-11-13 03:24:19,512][__main__][INFO] - Starting iteration 364. [2025-11-13 03:24:20,008][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 36 and human policies 1. [2025-11-13 03:24:20,008][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 03:24:29,634][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 03:24:36,292][__main__][INFO] - Number of regex retries in iteration 364: 1 [2025-11-13 03:24:36,293][__main__][INFO] - agents played in iteration 364 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 03:24:37,117][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:24:37,140][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:24:37,164][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:24:37,186][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:24:37,187][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 03:24:37,188][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 03:24:37,919][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 03:24:38,379][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 03:24:38,894][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 03:24:39,398][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 03:24:39,905][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 03:24:40,412][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 03:24:40,915][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 03:24:41,417][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 03:24:41,918][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 03:24:42,420][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 03:24:42,923][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 03:24:54,549][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 03:24:55,055][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 03:24:55,559][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 03:24:56,077][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 03:24:56,579][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 03:24:57,082][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 03:24:57,584][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 03:24:58,088][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 03:24:58,594][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 03:24:59,096][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 03:24:59,597][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 03:25:00,102][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 03:25:00,602][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 03:25:01,103][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 03:25:01,604][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 03:25:02,105][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 03:25:02,610][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 03:25:03,114][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 03:25:03,616][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 03:25:04,120][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 03:25:04,622][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 03:25:05,127][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 03:25:05,632][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 03:25:06,136][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 03:25:06,639][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 03:25:07,140][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 03:25:07,641][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 03:25:08,141][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 03:25:08,641][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 03:25:09,143][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 03:25:09,643][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 03:25:10,144][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10852 tokens. [2025-11-13 03:25:10,833][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.23%, Current % of VRAM taken: 59.68%, Block Peak % of device VRAM: 62.28%, ΔTime: 00:00:32 [2025-11-13 03:25:11,613][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 03:25:11,615][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 03:25:11,616][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 03:25:12,578][__main__][INFO] - Iteration 365 took 52s (30.98% Gen, 67.19% Train). Generation: 16s, Training: 35s. Estimated remaining time: 38h 33m 1s. Estimated total time: 43h 48m 32s. Time estimates for 10 more iterations: 8m 45s, 100 more iterations: 1h 27m 37s, 500 more iterations: 7h 18m 5s. [2025-11-13 03:25:12,580][__main__][INFO] - Starting iteration 365. [2025-11-13 03:25:13,073][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 36 and human policies 1. [2025-11-13 03:25:13,074][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 03:25:18,592][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 03:25:22,579][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 03:25:28,180][__main__][INFO] - Number of regex retries in iteration 365: 2 [2025-11-13 03:25:28,181][__main__][INFO] - agents played in iteration 365 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 03:25:28,978][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:25:29,006][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:25:29,032][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:25:29,055][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:25:29,055][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 03:25:29,056][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 03:25:29,755][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 03:25:30,220][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 03:25:30,728][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 03:25:31,230][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 03:25:31,737][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 03:25:32,240][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 03:25:32,744][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 03:25:33,249][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 03:25:33,752][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 03:25:34,265][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 03:25:34,768][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 03:25:46,402][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 03:25:46,906][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 03:25:47,412][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 03:25:47,918][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 03:25:48,424][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 03:25:48,928][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 03:25:49,430][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 03:25:49,937][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 03:25:50,440][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 03:25:50,941][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 03:25:51,443][mllm.training.trainer_common][INFO] - 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Processing mini-batch 54 of 64 [2025-11-13 03:25:57,499][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 03:25:58,000][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 03:25:58,529][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 03:25:59,038][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 03:25:59,541][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 03:26:00,047][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 03:26:00,551][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 03:26:01,053][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 03:26:01,565][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 03:26:02,067][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10859 tokens. [2025-11-13 03:26:02,761][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.32%, Current % of VRAM taken: 59.77%, Block Peak % of device VRAM: 62.51%, ΔTime: 00:00:33 [2025-11-13 03:26:03,523][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 03:26:03,524][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 03:26:03,526][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 03:26:04,454][__main__][INFO] - Iteration 366 took 51s (29.40% Gen, 68.79% Train). Generation: 15s, Training: 35s. Estimated remaining time: 37h 32m 41s. Estimated total time: 42h 49m 4s. Time estimates for 10 more iterations: 8m 33s, 100 more iterations: 1h 25m 38s, 500 more iterations: 7h 8m 10s. [2025-11-13 03:26:04,456][__main__][INFO] - Starting iteration 366. [2025-11-13 03:26:04,922][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 36 and human policies 1. [2025-11-13 03:26:04,922][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 03:26:20,261][__main__][INFO] - Number of regex retries in iteration 366: 0 [2025-11-13 03:26:20,262][__main__][INFO] - agents played in iteration 366 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 03:26:21,059][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.62%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:26:21,086][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.62%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:26:21,123][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.62%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:26:21,148][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.62%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:26:21,149][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 03:26:21,149][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 03:26:21,888][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 03:26:22,349][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 03:26:22,882][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 03:26:23,386][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 03:26:23,902][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 03:26:24,405][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 03:26:24,909][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 03:26:25,420][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 03:26:25,926][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 03:26:26,434][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 03:26:26,938][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 03:26:38,566][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 03:26:39,069][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 03:26:39,571][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 03:26:40,072][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 03:26:40,575][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 03:26:41,090][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 03:26:41,592][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 03:26:42,097][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 03:26:42,599][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 03:26:43,100][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 03:26:43,616][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 03:26:44,121][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 03:26:44,623][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 03:26:45,125][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 03:26:45,626][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 03:26:46,129][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 03:26:46,631][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 03:26:47,133][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 03:26:47,634][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 03:26:48,135][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 03:26:48,637][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 03:26:49,138][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 03:26:49,639][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 03:26:50,143][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 03:26:50,647][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 03:26:51,148][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 03:26:51,651][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 03:26:52,156][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 03:26:52,658][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 03:26:53,161][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 03:26:53,666][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 03:26:54,171][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10874 tokens. [2025-11-13 03:26:54,873][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.25%, Current % of VRAM taken: 59.70%, Block Peak % of device VRAM: 62.28%, ΔTime: 00:00:32 [2025-11-13 03:26:55,656][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 03:26:55,658][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 03:26:55,660][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 03:26:56,622][__main__][INFO] - Iteration 367 took 51s (29.67% Gen, 68.47% Train). Generation: 15s, Training: 35s. Estimated remaining time: 37h 47m 47s. Estimated total time: 43h 5m 2s. Time estimates for 10 more iterations: 8m 37s, 100 more iterations: 1h 26m 10s, 500 more iterations: 7h 10m 50s. [2025-11-13 03:26:56,624][__main__][INFO] - Starting iteration 367. [2025-11-13 03:26:57,096][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 36 and human policies 1. [2025-11-13 03:26:57,096][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 03:27:11,791][__main__][INFO] - Number of regex retries in iteration 367: 0 [2025-11-13 03:27:11,792][__main__][INFO] - agents played in iteration 367 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 03:27:12,578][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:27:12,603][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:27:12,628][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:27:12,650][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:27:12,651][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 03:27:12,652][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 03:27:13,325][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 03:27:13,801][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 03:27:14,309][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 03:27:14,814][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 03:27:15,318][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 03:27:15,822][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 03:27:16,328][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 03:27:16,836][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 03:27:17,339][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 03:27:17,840][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 03:27:18,343][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 03:27:18,849][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 03:27:19,354][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 03:27:19,857][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 03:27:20,363][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 03:27:20,865][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 03:27:21,372][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 03:27:21,882][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 03:27:22,385][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 03:27:22,913][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 03:27:23,418][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 03:27:23,930][mllm.training.trainer_common][INFO] - 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Processing mini-batch 43 of 64 [2025-11-13 03:27:35,562][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 03:27:36,073][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 03:27:36,576][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 03:27:37,079][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 03:27:37,596][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 03:27:38,099][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 03:27:38,614][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 03:27:39,120][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 03:27:39,620][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 03:27:40,125][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 03:27:40,624][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 03:27:41,126][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 03:27:41,628][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 03:27:42,130][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 03:27:42,641][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 03:27:43,143][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 03:27:43,646][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 03:27:44,149][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 03:27:44,651][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 03:27:45,159][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 03:27:45,662][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10875 tokens. [2025-11-13 03:27:46,435][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.28%, Current % of VRAM taken: 59.73%, Block Peak % of device VRAM: 62.52%, ΔTime: 00:00:33 [2025-11-13 03:27:47,214][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 03:27:47,215][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 03:27:47,217][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 03:27:48,120][__main__][INFO] - Iteration 368 took 51s (28.80% Gen, 69.43% Train). Generation: 14s, Training: 35s. Estimated remaining time: 37h 13m 9s. Estimated total time: 42h 31m 16s. Time estimates for 10 more iterations: 8m 30s, 100 more iterations: 1h 25m 2s, 500 more iterations: 7h 5m 12s. [2025-11-13 03:27:48,122][__main__][INFO] - Starting iteration 368. [2025-11-13 03:27:48,609][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 36 and human policies 1. [2025-11-13 03:27:48,609][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 03:28:06,561][__main__][INFO] - Number of regex retries in iteration 368: 0 [2025-11-13 03:28:06,562][__main__][INFO] - agents played in iteration 368 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 03:28:07,337][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:28:07,359][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:28:07,381][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:28:07,403][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:28:07,404][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 03:28:07,405][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 03:28:08,079][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 03:28:08,535][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 03:28:09,048][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 03:28:09,551][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 03:28:10,052][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 03:28:10,552][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 03:28:11,058][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 03:28:11,564][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 03:28:12,067][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 03:28:12,571][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 03:28:13,073][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 03:28:24,698][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 03:28:25,203][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 03:28:25,718][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 03:28:26,223][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 03:28:26,727][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 03:28:27,234][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 03:28:27,742][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 03:28:28,246][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 03:28:28,753][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 03:28:29,262][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 03:28:29,770][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 03:28:30,273][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 03:28:30,776][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 03:28:31,283][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 03:28:31,791][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 03:28:32,299][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 03:28:32,800][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 03:28:33,304][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 03:28:33,808][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 03:28:34,309][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 03:28:34,811][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 03:28:35,320][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 03:28:35,822][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 03:28:36,331][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 03:28:36,834][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 03:28:37,336][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 03:28:37,844][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 03:28:38,343][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 03:28:38,846][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 03:28:39,344][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 03:28:39,841][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 03:28:40,346][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10876 tokens. [2025-11-13 03:28:41,145][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.15%, Current % of VRAM taken: 59.60%, Block Peak % of device VRAM: 62.31%, ΔTime: 00:00:33 [2025-11-13 03:28:41,938][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 03:28:41,939][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 03:28:41,941][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 03:28:42,942][__main__][INFO] - Iteration 369 took 54s (33.04% Gen, 65.11% Train). Generation: 17s, Training: 35s. Estimated remaining time: 39h 57m 40s. Estimated total time: 45h 16m 42s. Time estimates for 10 more iterations: 9m 3s, 100 more iterations: 1h 30m 33s, 500 more iterations: 7h 32m 47s. [2025-11-13 03:28:42,945][__main__][INFO] - Starting iteration 369. [2025-11-13 03:28:43,449][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 36 and human policies 1. [2025-11-13 03:28:43,450][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 03:28:56,731][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 03:29:01,043][__main__][INFO] - Number of regex retries in iteration 369: 1 [2025-11-13 03:29:01,043][__main__][INFO] - agents played in iteration 369 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 03:29:01,875][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:29:01,903][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:29:01,930][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:29:01,953][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:29:01,954][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 03:29:01,955][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 03:29:02,633][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 03:29:03,090][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 03:29:03,598][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 03:29:04,104][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 03:29:04,609][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 03:29:05,113][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 03:29:05,618][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 03:29:06,123][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 03:29:06,626][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 03:29:07,131][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 03:29:07,635][mllm.training.trainer_common][INFO] - 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Processing mini-batch 43 of 64 [2025-11-13 03:29:24,806][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 03:29:25,323][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 03:29:25,824][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 03:29:26,326][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 03:29:26,832][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 03:29:27,334][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 03:29:27,836][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 03:29:28,340][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 03:29:28,843][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 03:29:29,360][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 03:29:29,863][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 03:29:30,366][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 03:29:30,867][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 03:29:31,368][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 03:29:31,874][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 03:29:32,375][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 03:29:32,875][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 03:29:33,380][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 03:29:33,881][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 03:29:34,382][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 03:29:34,882][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10877 tokens. [2025-11-13 03:29:35,662][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.21%, Current % of VRAM taken: 59.66%, Block Peak % of device VRAM: 62.32%, ΔTime: 00:00:33 [2025-11-13 03:29:36,444][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 03:29:36,446][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 03:29:36,448][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 03:29:37,436][__main__][INFO] - Iteration 370 took 53s (32.59% Gen, 65.58% Train). Generation: 17s, Training: 35s. Estimated remaining time: 39h 39m 25s. Estimated total time: 44h 59m 21s. Time estimates for 10 more iterations: 8m 59s, 100 more iterations: 1h 29m 58s, 500 more iterations: 7h 29m 53s. [2025-11-13 03:29:37,438][__main__][INFO] - Starting iteration 370. [2025-11-13 03:29:37,913][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 36 and human policies 1. [2025-11-13 03:29:37,914][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 03:29:43,407][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 03:29:53,079][__main__][INFO] - Number of regex retries in iteration 370: 1 [2025-11-13 03:29:53,079][__main__][INFO] - agents played in iteration 370 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 03:29:53,908][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:29:53,936][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:29:53,960][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:29:53,982][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:29:53,982][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 03:29:53,984][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 03:29:54,698][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 03:29:55,157][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 03:29:55,663][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 03:29:56,166][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 03:29:56,670][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 03:29:57,176][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 03:29:57,680][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 03:29:58,184][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 03:29:58,689][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 03:29:59,200][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 03:29:59,704][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 03:30:11,355][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 03:30:11,856][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 03:30:12,360][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 03:30:12,864][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 03:30:13,370][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 03:30:13,873][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 03:30:14,376][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 03:30:14,883][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 03:30:15,387][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 03:30:15,889][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 03:30:16,389][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 03:30:16,889][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 03:30:17,392][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 03:30:17,895][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 03:30:18,399][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 03:30:18,900][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 03:30:19,401][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 03:30:19,904][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 03:30:20,404][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 03:30:20,904][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 03:30:21,404][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 03:30:21,906][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 03:30:22,408][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 03:30:22,909][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 03:30:23,408][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 03:30:23,916][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 03:30:24,417][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 03:30:24,916][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 03:30:25,417][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 03:30:25,917][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 03:30:26,429][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 03:30:26,931][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10877 tokens. [2025-11-13 03:30:27,640][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.17%, Current % of VRAM taken: 59.62%, Block Peak % of device VRAM: 62.31%, ΔTime: 00:00:32 [2025-11-13 03:30:28,402][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 03:30:28,404][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 03:30:28,406][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 03:30:30,426][__main__][INFO] - Iteration 371 took 52s (28.88% Gen, 67.27% Train). Generation: 15s, Training: 35s. Estimated remaining time: 38h 24m 51s. Estimated total time: 43h 45m 40s. Time estimates for 10 more iterations: 8m 45s, 100 more iterations: 1h 27m 31s, 500 more iterations: 7h 17m 36s. [2025-11-13 03:30:30,428][__main__][INFO] - Starting iteration 371. [2025-11-13 03:30:30,895][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 37 and human policies 1. [2025-11-13 03:30:30,895][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 03:30:36,176][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 03:30:46,971][__main__][INFO] - Number of regex retries in iteration 371: 1 [2025-11-13 03:30:46,972][__main__][INFO] - agents played in iteration 371 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 03:30:47,813][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:30:47,835][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:30:47,858][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:30:47,884][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:30:47,884][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 03:30:47,885][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 03:30:48,607][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 03:30:49,064][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 03:30:49,572][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 03:30:50,077][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 03:30:50,583][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 03:30:51,088][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 03:30:51,590][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 03:30:52,096][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 03:30:52,604][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 03:30:53,110][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 03:30:53,615][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 03:30:54,125][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 03:30:54,628][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 03:30:55,134][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 03:30:55,648][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 03:30:56,152][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 03:30:56,656][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 03:30:57,165][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 03:30:57,666][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 03:30:58,170][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 03:30:58,673][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 03:30:59,181][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 03:31:05,240][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 03:31:05,752][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 03:31:06,255][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 03:31:06,762][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 03:31:07,271][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 03:31:07,782][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 03:31:08,288][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 03:31:08,792][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 03:31:09,293][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 03:31:09,795][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 03:31:10,298][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 03:31:10,800][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 03:31:11,304][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 03:31:11,806][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 03:31:12,305][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 03:31:12,803][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 03:31:13,306][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 03:31:13,808][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 03:31:14,311][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 03:31:14,813][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 03:31:15,318][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 03:31:15,821][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 03:31:16,326][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 03:31:16,828][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 03:31:17,328][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 03:31:17,829][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 03:31:18,330][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 03:31:18,833][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 03:31:19,334][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 03:31:19,836][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 03:31:20,337][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 03:31:20,839][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10879 tokens. [2025-11-13 03:31:21,608][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.21%, Current % of VRAM taken: 59.66%, Block Peak % of device VRAM: 62.39%, ΔTime: 00:00:33 [2025-11-13 03:31:22,367][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 03:31:22,369][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 03:31:22,370][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 03:31:23,286][__main__][INFO] - Iteration 372 took 52s (30.68% Gen, 67.56% Train). Generation: 16s, Training: 35s. Estimated remaining time: 38h 17m 54s. Estimated total time: 43h 39m 36s. Time estimates for 10 more iterations: 8m 43s, 100 more iterations: 1h 27m 19s, 500 more iterations: 7h 16m 36s. [2025-11-13 03:31:23,288][__main__][INFO] - Starting iteration 372. [2025-11-13 03:31:23,786][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 37 and human policies 1. [2025-11-13 03:31:23,786][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 03:31:29,766][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 03:31:40,236][__main__][INFO] - Number of regex retries in iteration 372: 1 [2025-11-13 03:31:40,237][__main__][INFO] - agents played in iteration 372 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 03:31:41,019][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:31:41,047][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:31:41,073][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:31:41,096][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:31:41,096][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 03:31:41,097][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 03:31:41,841][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 03:31:42,299][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 03:31:42,808][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 03:31:43,312][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 03:31:43,819][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 03:31:44,323][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 03:31:44,829][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 03:31:45,340][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 03:31:45,855][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 03:31:46,367][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 03:31:46,888][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 03:31:58,505][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 03:31:59,011][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 03:31:59,517][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 03:32:00,022][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 03:32:00,526][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 03:32:01,027][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 03:32:01,533][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 03:32:02,035][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 03:32:02,540][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 03:32:03,044][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 03:32:03,549][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 03:32:04,062][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 03:32:04,568][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 03:32:05,086][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 03:32:05,589][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 03:32:06,090][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 03:32:06,594][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 03:32:07,097][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 03:32:07,600][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 03:32:08,100][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 03:32:08,605][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 03:32:09,108][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 03:32:09,610][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 03:32:10,117][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 03:32:10,619][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 03:32:11,123][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 03:32:11,626][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 03:32:12,129][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 03:32:12,633][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 03:32:13,139][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 03:32:13,643][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 03:32:14,146][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10868 tokens. [2025-11-13 03:32:14,851][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.28%, Current % of VRAM taken: 59.74%, Block Peak % of device VRAM: 62.35%, ΔTime: 00:00:33 [2025-11-13 03:32:15,627][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 03:32:15,629][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 03:32:15,631][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 03:32:16,527][__main__][INFO] - Iteration 373 took 52s (31.19% Gen, 67.11% Train). Generation: 16s, Training: 35s. Estimated remaining time: 38h 34m 32s. Estimated total time: 43h 57m 7s. Time estimates for 10 more iterations: 8m 47s, 100 more iterations: 1h 27m 54s, 500 more iterations: 7h 19m 31s. [2025-11-13 03:32:16,529][__main__][INFO] - Starting iteration 373. [2025-11-13 03:32:17,010][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 37 and human policies 1. [2025-11-13 03:32:17,011][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 03:32:23,565][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 03:32:28,713][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 03:32:34,815][__main__][INFO] - Number of regex retries in iteration 373: 2 [2025-11-13 03:32:34,815][__main__][INFO] - agents played in iteration 373 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 03:32:35,648][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:32:35,673][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:32:35,695][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:32:35,717][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:32:35,718][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 03:32:35,719][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 03:32:36,460][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 03:32:36,925][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 03:32:37,440][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 03:32:37,948][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 03:32:38,459][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 03:32:38,965][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 03:32:39,484][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 03:32:39,988][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 03:32:40,492][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 03:32:40,998][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 03:32:41,501][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 03:32:42,008][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 03:32:42,511][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 03:32:43,014][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 03:32:43,518][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 03:32:44,020][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 03:32:44,523][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 03:32:45,023][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 03:32:45,525][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 03:32:46,024][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 03:32:46,527][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 03:32:47,029][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 03:32:47,530][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 03:32:48,031][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 03:32:48,534][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 03:32:49,041][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 03:32:49,546][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 03:32:50,050][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 03:32:50,555][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 03:32:51,060][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 03:32:51,566][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 03:32:52,072][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 03:32:52,580][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 03:32:53,085][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 03:32:53,587][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 03:32:54,090][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 03:32:54,592][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 03:32:55,099][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 03:32:55,605][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 03:32:56,115][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 03:32:56,620][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 03:32:57,123][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 03:32:57,629][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 03:32:58,137][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 03:32:58,642][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 03:32:59,148][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 03:32:59,651][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 03:33:00,158][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 03:33:00,665][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 03:33:01,168][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 03:33:01,681][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 03:33:02,183][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 03:33:02,699][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 03:33:03,202][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 03:33:03,703][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 03:33:04,206][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 03:33:04,709][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 03:33:05,211][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 03:33:05,713][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 03:33:06,215][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 03:33:06,722][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 03:33:07,228][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 03:33:07,730][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 03:33:08,234][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 03:33:08,737][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10878 tokens. [2025-11-13 03:33:09,456][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.27%, Current % of VRAM taken: 59.72%, Block Peak % of device VRAM: 62.52%, ΔTime: 00:00:33 [2025-11-13 03:33:10,220][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 03:33:10,222][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 03:33:10,223][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 03:33:11,138][__main__][INFO] - Iteration 374 took 54s (32.89% Gen, 65.42% Train). Generation: 17s, Training: 35s. Estimated remaining time: 39h 42m 55s. Estimated total time: 45h 6m 25s. Time estimates for 10 more iterations: 9m 1s, 100 more iterations: 1h 30m 12s, 500 more iterations: 7h 31m 4s. [2025-11-13 03:33:11,140][__main__][INFO] - Starting iteration 374. [2025-11-13 03:33:11,605][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 37 and human policies 1. [2025-11-13 03:33:11,606][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 03:33:16,412][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 03:33:26,143][__main__][INFO] - Number of regex retries in iteration 374: 1 [2025-11-13 03:33:26,144][__main__][INFO] - agents played in iteration 374 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 03:33:26,914][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:33:26,944][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:33:26,972][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:33:26,996][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:33:26,997][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 03:33:26,998][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 03:33:27,671][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 03:33:28,127][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 03:33:28,635][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 03:33:29,142][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 03:33:29,646][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 03:33:30,151][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 03:33:30,656][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 03:33:31,161][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 03:33:31,684][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 03:33:32,187][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 03:33:32,691][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 03:33:44,264][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 03:33:44,769][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 03:33:45,273][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 03:33:45,776][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 03:33:46,280][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 03:33:46,783][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 03:33:47,286][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 03:33:47,790][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 03:33:48,294][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 03:33:48,814][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 03:33:49,320][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 03:33:49,829][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 03:33:50,338][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 03:33:50,842][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 03:33:51,350][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 03:33:51,856][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 03:33:52,361][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 03:33:52,867][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 03:33:53,374][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 03:33:53,885][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 03:33:54,388][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 03:33:54,891][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 03:33:55,397][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 03:33:55,900][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 03:33:56,402][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 03:33:56,903][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 03:33:57,403][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 03:33:57,913][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 03:33:58,414][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 03:33:58,917][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 03:33:59,417][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 03:33:59,920][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10877 tokens. [2025-11-13 03:34:00,628][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.24%, Current % of VRAM taken: 59.69%, Block Peak % of device VRAM: 62.41%, ΔTime: 00:00:32 [2025-11-13 03:34:01,391][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 03:34:01,393][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 03:34:01,395][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 03:34:02,313][__main__][INFO] - Iteration 375 took 50s (28.67% Gen, 69.52% Train). Generation: 14s, Training: 35s. Estimated remaining time: 36h 51m 3s. Estimated total time: 42h 15m 24s. Time estimates for 10 more iterations: 8m 27s, 100 more iterations: 1h 24m 30s, 500 more iterations: 7h 2m 34s. [2025-11-13 03:34:02,315][__main__][INFO] - Starting iteration 375. [2025-11-13 03:34:02,795][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 37 and human policies 1. [2025-11-13 03:34:02,796][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 03:34:07,784][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 03:34:18,044][__main__][INFO] - Number of regex retries in iteration 375: 1 [2025-11-13 03:34:18,045][__main__][INFO] - agents played in iteration 375 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 03:34:18,824][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:34:18,861][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:34:18,889][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:34:18,913][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:34:18,914][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 03:34:18,916][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 03:34:19,570][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 03:34:20,031][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 03:34:20,546][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 03:34:21,050][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 03:34:21,553][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 03:34:22,056][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 03:34:22,559][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 03:34:23,063][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 03:34:23,566][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 03:34:24,069][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 03:34:24,580][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 03:34:25,084][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 03:34:25,587][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 03:34:26,093][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 03:34:26,598][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 03:34:27,118][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 03:34:27,624][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 03:34:28,129][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 03:34:28,633][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 03:34:29,138][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 03:34:29,650][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 03:34:30,156][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 03:34:30,661][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 03:34:31,165][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 03:34:31,670][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 03:34:32,173][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 03:34:32,676][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 03:34:33,178][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 03:34:33,682][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 03:34:34,185][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 03:34:34,688][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 03:34:35,195][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 03:34:35,699][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 03:34:36,205][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 03:34:36,709][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 03:34:37,216][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 03:34:37,723][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 03:34:38,226][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 03:34:38,730][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 03:34:39,234][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 03:34:39,737][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 03:34:40,242][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 03:34:40,745][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 03:34:41,248][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 03:34:41,751][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 03:34:42,254][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 03:34:42,761][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 03:34:43,267][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 03:34:43,772][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 03:34:44,279][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 03:34:44,785][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 03:34:45,292][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 03:34:45,799][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 03:34:46,304][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 03:34:46,808][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 03:34:47,312][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 03:34:47,817][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 03:34:48,321][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 03:34:48,826][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 03:34:49,340][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 03:34:49,842][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 03:34:50,346][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 03:34:50,848][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 03:34:51,348][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 03:34:51,853][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10877 tokens. [2025-11-13 03:34:52,552][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.25%, Current % of VRAM taken: 59.70%, Block Peak % of device VRAM: 62.50%, ΔTime: 00:00:32 [2025-11-13 03:34:53,330][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 03:34:53,332][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 03:34:53,334][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 03:34:54,291][__main__][INFO] - Iteration 376 took 51s (29.61% Gen, 68.53% Train). Generation: 15s, Training: 35s. Estimated remaining time: 37h 29m 34s. Estimated total time: 42h 54m 47s. Time estimates for 10 more iterations: 8m 34s, 100 more iterations: 1h 25m 49s, 500 more iterations: 7h 9m 7s. [2025-11-13 03:34:54,293][__main__][INFO] - Starting iteration 376. [2025-11-13 03:34:54,781][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 37 and human policies 1. [2025-11-13 03:34:54,782][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 03:35:00,505][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 03:35:01,732][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 03:35:07,933][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 03:35:10,464][__main__][INFO] - Number of regex retries in iteration 376: 3 [2025-11-13 03:35:10,465][__main__][INFO] - agents played in iteration 376 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 03:35:11,286][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:35:11,311][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:35:11,337][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:35:11,373][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:35:11,373][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 03:35:11,375][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 03:35:12,042][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 03:35:12,497][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 03:35:13,003][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 03:35:13,504][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 03:35:14,004][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 03:35:14,504][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 03:35:15,003][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 03:35:15,519][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 03:35:16,018][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 03:35:16,520][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 03:35:17,027][mllm.training.trainer_common][INFO] - 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Processing mini-batch 21 of 64 [2025-11-13 03:35:23,075][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 03:35:23,580][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 03:35:24,083][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 03:35:24,586][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 03:35:25,089][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 03:35:25,592][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 03:35:26,097][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 03:35:26,600][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 03:35:27,102][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 03:35:27,604][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 03:35:28,104][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 03:35:28,610][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 03:35:29,112][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 03:35:29,614][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 03:35:30,118][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 03:35:30,620][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 03:35:31,122][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 03:35:31,623][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 03:35:32,125][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 03:35:32,643][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 03:35:33,145][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 03:35:33,646][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 03:35:34,147][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 03:35:34,648][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 03:35:35,152][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 03:35:35,652][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 03:35:36,153][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 03:35:36,658][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 03:35:37,165][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 03:35:37,670][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 03:35:38,175][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 03:35:38,681][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 03:35:39,186][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 03:35:39,689][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 03:35:40,195][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 03:35:40,707][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 03:35:41,217][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 03:35:41,722][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 03:35:42,227][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 03:35:42,732][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 03:35:43,235][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 03:35:43,739][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 03:35:44,243][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10878 tokens. [2025-11-13 03:35:44,969][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.25%, Current % of VRAM taken: 59.71%, Block Peak % of device VRAM: 62.28%, ΔTime: 00:00:32 [2025-11-13 03:35:45,758][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 03:35:45,760][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 03:35:45,762][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 03:35:46,693][__main__][INFO] - Iteration 377 took 51s (30.21% Gen, 67.99% Train). Generation: 15s, Training: 35s. Estimated remaining time: 37h 49m 31s. Estimated total time: 43h 15m 36s. Time estimates for 10 more iterations: 8m 39s, 100 more iterations: 1h 26m 31s, 500 more iterations: 7h 12m 36s. [2025-11-13 03:35:46,695][__main__][INFO] - Starting iteration 377. [2025-11-13 03:35:47,212][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 37 and human policies 1. [2025-11-13 03:35:47,213][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 03:36:03,922][__main__][INFO] - Number of regex retries in iteration 377: 0 [2025-11-13 03:36:03,922][__main__][INFO] - agents played in iteration 377 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 03:36:04,691][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:36:04,716][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:36:04,740][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:36:04,762][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:36:04,762][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 03:36:04,763][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 03:36:05,428][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 03:36:05,885][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 03:36:06,387][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 03:36:06,887][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 03:36:07,386][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 03:36:07,887][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 03:36:08,387][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 03:36:08,886][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 03:36:09,388][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 03:36:09,890][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 03:36:10,391][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 03:36:10,890][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 03:36:11,389][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 03:36:11,892][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 03:36:12,393][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 03:36:12,897][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 03:36:13,407][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 03:36:13,909][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 03:36:14,418][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 03:36:14,922][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 03:36:15,435][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 03:36:15,968][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 03:36:22,058][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 03:36:22,560][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 03:36:23,062][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 03:36:23,565][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 03:36:24,066][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 03:36:24,568][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 03:36:25,071][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 03:36:25,574][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 03:36:26,076][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 03:36:26,576][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 03:36:27,081][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 03:36:27,586][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 03:36:28,086][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 03:36:28,589][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 03:36:29,090][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 03:36:29,591][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 03:36:30,098][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 03:36:30,600][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 03:36:31,102][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 03:36:31,611][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 03:36:32,115][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 03:36:32,622][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 03:36:33,126][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 03:36:33,630][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 03:36:34,153][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 03:36:34,658][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 03:36:35,165][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 03:36:35,669][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 03:36:36,173][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 03:36:36,681][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 03:36:37,184][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 03:36:37,687][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10879 tokens. [2025-11-13 03:36:38,420][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.26%, Current % of VRAM taken: 59.71%, Block Peak % of device VRAM: 62.52%, ΔTime: 00:00:33 [2025-11-13 03:36:39,201][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 03:36:39,204][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 03:36:39,206][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 03:36:40,221][__main__][INFO] - Iteration 378 took 53s (31.52% Gen, 66.56% Train). Generation: 16s, Training: 35s. Estimated remaining time: 38h 43m 30s. Estimated total time: 44h 10m 29s. Time estimates for 10 more iterations: 8m 50s, 100 more iterations: 1h 28m 20s, 500 more iterations: 7h 21m 44s. [2025-11-13 03:36:40,223][__main__][INFO] - Starting iteration 378. [2025-11-13 03:36:40,716][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 37 and human policies 1. [2025-11-13 03:36:40,717][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 03:36:47,850][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 03:36:47,894][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 03:36:59,201][__main__][INFO] - Number of regex retries in iteration 378: 2 [2025-11-13 03:36:59,202][__main__][INFO] - agents played in iteration 378 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 03:37:00,030][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:37:00,054][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:37:00,078][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:37:00,101][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:37:00,101][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 03:37:00,102][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 03:37:00,787][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 03:37:01,243][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 03:37:01,756][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 03:37:02,257][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 03:37:02,757][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 03:37:03,260][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 03:37:03,762][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 03:37:04,261][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 03:37:04,762][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 03:37:05,272][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 03:37:05,774][mllm.training.trainer_common][INFO] - 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Processing mini-batch 21 of 64 [2025-11-13 03:37:11,859][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 03:37:12,366][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 03:37:12,872][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 03:37:13,384][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 03:37:13,891][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 03:37:14,398][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 03:37:14,904][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 03:37:15,412][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 03:37:15,921][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 03:37:16,433][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 03:37:16,937][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 03:37:17,454][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 03:37:17,955][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 03:37:18,460][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 03:37:18,962][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 03:37:19,464][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 03:37:19,971][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 03:37:20,474][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 03:37:20,977][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 03:37:21,479][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 03:37:21,981][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 03:37:22,485][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 03:37:22,986][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 03:37:23,485][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 03:37:23,988][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 03:37:24,490][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 03:37:24,991][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 03:37:25,494][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 03:37:25,997][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 03:37:26,503][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 03:37:27,005][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 03:37:27,510][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 03:37:28,020][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 03:37:28,527][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 03:37:29,035][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 03:37:29,545][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 03:37:30,053][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 03:37:30,556][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 03:37:31,061][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 03:37:31,567][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 03:37:32,079][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 03:37:32,583][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 03:37:33,086][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10878 tokens. [2025-11-13 03:37:33,813][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.25%, Current % of VRAM taken: 59.71%, Block Peak % of device VRAM: 62.41%, ΔTime: 00:00:33 [2025-11-13 03:37:34,592][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 03:37:34,593][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 03:37:34,596][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 03:37:35,462][__main__][INFO] - Iteration 379 took 54s (33.76% Gen, 64.65% Train). Generation: 18s, Training: 35s. Estimated remaining time: 40h 9m 24s. Estimated total time: 45h 37m 18s. Time estimates for 10 more iterations: 9m 7s, 100 more iterations: 1h 31m 14s, 500 more iterations: 7h 36m 13s. [2025-11-13 03:37:35,464][__main__][INFO] - Starting iteration 379. [2025-11-13 03:37:36,133][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 37 and human policies 1. [2025-11-13 03:37:36,134][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 03:37:42,759][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 03:37:52,475][__main__][INFO] - Number of regex retries in iteration 379: 1 [2025-11-13 03:37:52,476][__main__][INFO] - agents played in iteration 379 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 03:37:53,331][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:37:53,360][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:37:53,387][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:37:53,411][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:37:53,411][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 03:37:53,413][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 03:37:54,099][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 03:37:54,563][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 03:37:55,071][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 03:37:55,574][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 03:37:56,083][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 03:37:56,586][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 03:37:57,091][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 03:37:57,595][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 03:37:58,097][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 03:37:58,600][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 03:37:59,104][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 03:37:59,606][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 03:38:00,107][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 03:38:00,608][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 03:38:01,115][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 03:38:01,621][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 03:38:02,127][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 03:38:02,639][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 03:38:03,146][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 03:38:03,661][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 03:38:04,169][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 03:38:04,678][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 03:38:10,767][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 03:38:11,271][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 03:38:11,771][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 03:38:12,273][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 03:38:12,776][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 03:38:13,278][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 03:38:13,780][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 03:38:14,283][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 03:38:14,785][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 03:38:15,286][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 03:38:15,787][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 03:38:16,288][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 03:38:16,794][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 03:38:17,294][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 03:38:17,796][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 03:38:18,302][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 03:38:18,803][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 03:38:19,306][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 03:38:19,807][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 03:38:20,308][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 03:38:20,810][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 03:38:21,311][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 03:38:21,816][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 03:38:22,319][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 03:38:22,821][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 03:38:23,332][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 03:38:23,836][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 03:38:24,341][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 03:38:24,847][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 03:38:25,353][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 03:38:25,865][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 03:38:26,370][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10877 tokens. [2025-11-13 03:38:27,178][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.27%, Current % of VRAM taken: 59.72%, Block Peak % of device VRAM: 62.34%, ΔTime: 00:00:33 [2025-11-13 03:38:27,959][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 03:38:27,961][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 03:38:27,963][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 03:38:28,931][__main__][INFO] - Iteration 380 took 52s (30.95% Gen, 67.21% Train). Generation: 16s, Training: 35s. Estimated remaining time: 38h 31m 6s. Estimated total time: 43h 59m 54s. Time estimates for 10 more iterations: 8m 47s, 100 more iterations: 1h 27m 59s, 500 more iterations: 7h 19m 59s. [2025-11-13 03:38:28,933][__main__][INFO] - Starting iteration 380. [2025-11-13 03:38:29,424][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 37 and human policies 1. [2025-11-13 03:38:29,425][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 03:38:35,171][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 03:38:37,863][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 03:38:44,397][__main__][INFO] - Number of regex retries in iteration 380: 2 [2025-11-13 03:38:44,398][__main__][INFO] - agents played in iteration 380 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 03:38:45,190][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:38:45,214][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:38:45,239][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:38:45,261][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:38:45,262][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 03:38:45,263][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 03:38:45,953][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 03:38:46,408][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 03:38:46,913][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 03:38:47,416][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 03:38:47,922][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 03:38:48,439][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 03:38:48,941][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 03:38:49,452][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 03:38:49,952][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 03:38:50,450][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 03:38:50,950][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 03:38:51,449][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 03:38:51,948][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 03:38:52,448][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 03:38:52,950][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 03:38:53,449][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 03:38:53,948][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 03:38:54,447][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 03:38:54,948][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 03:38:55,448][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 03:38:55,956][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 03:38:56,456][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 03:38:56,961][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 03:38:57,464][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 03:38:57,966][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 03:38:58,475][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 03:38:58,980][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 03:38:59,482][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 03:38:59,992][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 03:39:00,501][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 03:39:01,006][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 03:39:01,516][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 03:39:02,022][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 03:39:02,538][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 03:39:03,048][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 03:39:03,553][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 03:39:04,063][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 03:39:04,569][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 03:39:05,075][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 03:39:05,580][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 03:39:06,083][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 03:39:06,587][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 03:39:07,089][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 03:39:07,591][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 03:39:08,098][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 03:39:08,606][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 03:39:09,109][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 03:39:09,610][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 03:39:10,110][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 03:39:10,613][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 03:39:11,115][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 03:39:11,616][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 03:39:12,130][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 03:39:12,633][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 03:39:13,134][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 03:39:13,636][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 03:39:14,138][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 03:39:14,645][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 03:39:15,148][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 03:39:15,651][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 03:39:16,154][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 03:39:16,656][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 03:39:17,158][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 03:39:17,661][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 03:39:18,166][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10878 tokens. [2025-11-13 03:39:18,893][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.26%, Current % of VRAM taken: 59.71%, Block Peak % of device VRAM: 62.31%, ΔTime: 00:00:32 [2025-11-13 03:39:19,705][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 03:39:19,706][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 03:39:19,708][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 03:39:21,473][__main__][INFO] - Iteration 381 took 52s (28.77% Gen, 67.84% Train). Generation: 14s, Training: 35s. Estimated remaining time: 37h 52m 47s. Estimated total time: 43h 22m 27s. Time estimates for 10 more iterations: 8m 40s, 100 more iterations: 1h 26m 44s, 500 more iterations: 7h 13m 44s. [2025-11-13 03:39:21,475][__main__][INFO] - Starting iteration 381. [2025-11-13 03:39:21,989][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 38 and human policies 1. [2025-11-13 03:39:21,990][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 03:39:27,476][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 03:39:28,246][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 03:39:38,301][__main__][INFO] - Number of regex retries in iteration 381: 2 [2025-11-13 03:39:38,301][__main__][INFO] - agents played in iteration 381 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 03:39:39,132][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:39:39,157][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:39:39,195][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:39:39,218][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:39:39,219][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 03:39:39,220][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 03:39:39,907][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 03:39:40,366][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 03:39:40,882][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 03:39:41,387][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 03:39:41,896][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 03:39:42,403][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 03:39:42,906][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 03:39:43,413][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 03:39:43,920][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 03:39:44,426][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 03:39:44,927][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 03:39:45,429][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 03:39:45,929][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 03:39:46,430][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 03:39:46,931][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 03:39:47,432][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 03:39:47,933][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 03:39:48,445][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 03:39:48,946][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 03:39:49,448][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 03:39:49,951][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 03:39:50,451][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 03:39:50,952][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 03:39:51,454][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 03:39:51,955][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 03:39:52,466][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 03:39:52,965][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 03:39:53,471][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 03:39:53,971][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 03:39:54,473][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 03:39:54,977][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 03:39:55,481][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 03:39:55,980][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 03:39:56,485][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 03:39:56,989][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 03:39:57,494][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 03:39:57,998][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 03:39:58,503][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 03:39:59,014][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 03:39:59,517][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 03:40:00,021][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 03:40:00,535][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 03:40:01,039][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 03:40:01,554][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 03:40:02,059][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 03:40:02,562][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 03:40:03,070][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 03:40:03,574][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 03:40:04,076][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 03:40:04,580][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 03:40:05,083][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 03:40:05,590][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 03:40:06,091][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 03:40:06,598][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 03:40:07,101][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 03:40:07,604][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 03:40:08,106][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 03:40:08,608][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 03:40:09,108][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 03:40:09,609][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 03:40:10,111][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 03:40:10,612][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 03:40:11,115][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 03:40:11,620][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 03:40:12,124][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10877 tokens. [2025-11-13 03:40:12,875][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.26%, Current % of VRAM taken: 59.71%, Block Peak % of device VRAM: 62.49%, ΔTime: 00:00:32 [2025-11-13 03:40:13,678][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 03:40:13,680][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 03:40:13,682][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 03:40:14,602][__main__][INFO] - Iteration 382 took 52s (31.00% Gen, 67.25% Train). Generation: 16s, Training: 35s. Estimated remaining time: 38h 20m 6s. Estimated total time: 43h 50m 39s. Time estimates for 10 more iterations: 8m 46s, 100 more iterations: 1h 27m 41s, 500 more iterations: 7h 18m 26s. [2025-11-13 03:40:14,604][__main__][INFO] - Starting iteration 382. [2025-11-13 03:40:15,092][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 38 and human policies 1. [2025-11-13 03:40:15,092][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 03:40:33,863][__main__][INFO] - Number of regex retries in iteration 382: 0 [2025-11-13 03:40:33,864][__main__][INFO] - agents played in iteration 382 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 03:40:34,705][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.62%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:40:34,733][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.62%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:40:34,760][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.62%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:40:34,783][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.62%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:40:34,784][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 03:40:34,785][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 03:40:35,479][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 03:40:35,949][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 03:40:36,456][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 03:40:36,967][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 03:40:37,469][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 03:40:37,973][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 03:40:38,479][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 03:40:38,982][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 03:40:39,486][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 03:40:39,986][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 03:40:40,487][mllm.training.trainer_common][INFO] - 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Processing mini-batch 21 of 64 [2025-11-13 03:40:46,502][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 03:40:47,006][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 03:40:47,513][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 03:40:48,016][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 03:40:48,521][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 03:40:49,029][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 03:40:49,531][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 03:40:50,033][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 03:40:50,547][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 03:40:51,048][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 03:40:51,572][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 03:40:52,078][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 03:40:52,585][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 03:40:53,089][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 03:40:53,594][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 03:40:54,098][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 03:40:54,602][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 03:40:55,105][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 03:40:55,613][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 03:40:56,117][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 03:40:56,621][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 03:40:57,126][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 03:40:57,630][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 03:40:58,137][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 03:40:58,642][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 03:40:59,148][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 03:40:59,668][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 03:41:00,173][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 03:41:00,676][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 03:41:01,178][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 03:41:01,675][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 03:41:02,187][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 03:41:02,688][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 03:41:03,190][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 03:41:03,692][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 03:41:04,196][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 03:41:04,702][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 03:41:05,206][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 03:41:05,708][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 03:41:06,216][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 03:41:06,722][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 03:41:07,228][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 03:41:07,732][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10877 tokens. [2025-11-13 03:41:08,436][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.28%, Current % of VRAM taken: 59.74%, Block Peak % of device VRAM: 62.35%, ΔTime: 00:00:32 [2025-11-13 03:41:09,217][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 03:41:09,219][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 03:41:09,220][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 03:41:10,121][__main__][INFO] - Iteration 383 took 55s (34.11% Gen, 64.25% Train). Generation: 18s, Training: 35s. Estimated remaining time: 40h 19m 59s. Estimated total time: 45h 51m 28s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 42s, 500 more iterations: 7h 38m 34s. [2025-11-13 03:41:10,123][__main__][INFO] - Starting iteration 383. [2025-11-13 03:41:10,611][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 38 and human policies 1. [2025-11-13 03:41:10,612][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 03:41:25,453][__main__][INFO] - Number of regex retries in iteration 383: 0 [2025-11-13 03:41:25,453][__main__][INFO] - agents played in iteration 383 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 03:41:26,286][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:41:26,313][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:41:26,341][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:41:26,364][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:41:26,364][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 03:41:26,365][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 03:41:27,050][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 03:41:27,508][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 03:41:28,013][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 03:41:28,516][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 03:41:29,020][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 03:41:29,522][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 03:41:30,022][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 03:41:30,523][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 03:41:31,025][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 03:41:31,532][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 03:41:32,041][mllm.training.trainer_common][INFO] - 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Processing mini-batch 21 of 64 [2025-11-13 03:41:38,212][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 03:41:38,716][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 03:41:39,225][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 03:41:39,729][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 03:41:40,232][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 03:41:40,736][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 03:41:41,242][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 03:41:41,746][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 03:41:42,250][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 03:41:42,752][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 03:41:43,256][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 03:41:43,758][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 03:41:44,261][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 03:41:44,763][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 03:41:45,266][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 03:41:45,772][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 03:41:46,278][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 03:41:46,781][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 03:41:47,287][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 03:41:47,793][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 03:41:48,300][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 03:41:48,805][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 03:41:49,313][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 03:41:49,824][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 03:41:50,329][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 03:41:50,836][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 03:41:51,341][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 03:41:51,844][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 03:41:52,349][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 03:41:52,852][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 03:41:53,354][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 03:41:53,870][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 03:41:54,374][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 03:41:54,890][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 03:41:55,392][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 03:41:55,895][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 03:41:56,398][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 03:41:56,901][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 03:41:57,405][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 03:41:57,909][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 03:41:58,412][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 03:41:58,917][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 03:41:59,420][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10863 tokens. [2025-11-13 03:42:00,158][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.26%, Current % of VRAM taken: 59.72%, Block Peak % of device VRAM: 62.29%, ΔTime: 00:00:33 [2025-11-13 03:42:00,950][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 03:42:00,951][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 03:42:00,953][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 03:42:01,948][__main__][INFO] - Iteration 384 took 51s (28.91% Gen, 69.15% Train). Generation: 14s, Training: 35s. Estimated remaining time: 37h 14m 33s. Estimated total time: 42h 46m 53s. Time estimates for 10 more iterations: 8m 33s, 100 more iterations: 1h 25m 33s, 500 more iterations: 7h 7m 48s. [2025-11-13 03:42:01,951][__main__][INFO] - Starting iteration 384. [2025-11-13 03:42:02,419][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 38 and human policies 1. [2025-11-13 03:42:02,420][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 03:42:13,439][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 03:42:16,715][__main__][INFO] - Number of regex retries in iteration 384: 1 [2025-11-13 03:42:16,716][__main__][INFO] - agents played in iteration 384 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 03:42:17,492][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:42:17,520][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:42:17,547][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:42:17,570][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:42:17,570][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 03:42:17,571][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 03:42:18,250][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 03:42:18,717][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 03:42:19,225][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 03:42:19,727][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 03:42:20,231][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 03:42:20,732][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 03:42:21,235][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 03:42:21,737][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 03:42:22,241][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 03:42:22,745][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 03:42:23,247][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 03:42:23,751][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 03:42:24,256][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 03:42:24,760][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 03:42:25,265][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 03:42:25,766][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 03:42:26,268][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 03:42:26,767][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 03:42:27,267][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 03:42:27,781][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 03:42:28,282][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 03:42:28,782][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 03:42:29,287][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 03:42:29,787][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 03:42:30,287][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 03:42:30,786][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 03:42:31,286][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 03:42:31,801][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 03:42:32,301][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 03:42:32,802][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 03:42:33,302][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 03:42:33,802][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 03:42:34,306][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 03:42:34,808][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 03:42:35,308][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 03:42:35,815][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 03:42:36,319][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 03:42:36,825][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 03:42:37,329][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 03:42:37,832][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 03:42:38,335][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 03:42:38,841][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 03:42:39,345][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 03:42:39,853][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 03:42:40,357][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 03:42:40,860][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 03:42:41,363][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 03:42:41,867][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 03:42:42,376][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 03:42:42,879][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 03:42:43,382][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 03:42:43,884][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 03:42:44,389][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 03:42:44,906][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 03:42:45,410][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 03:42:45,915][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 03:42:46,417][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 03:42:46,921][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 03:42:47,429][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 03:42:47,933][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 03:42:48,432][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 03:42:48,942][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 03:42:49,445][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 03:42:49,948][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 03:42:50,451][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10878 tokens. [2025-11-13 03:42:51,166][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.21%, Current % of VRAM taken: 59.66%, Block Peak % of device VRAM: 62.28%, ΔTime: 00:00:32 [2025-11-13 03:42:51,958][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 03:42:51,960][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 03:42:51,961][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 03:42:52,873][__main__][INFO] - Iteration 385 took 50s (28.33% Gen, 69.86% Train). Generation: 14s, Training: 35s. Estimated remaining time: 36h 29m 31s. Estimated total time: 42h 2m 42s. Time estimates for 10 more iterations: 8m 24s, 100 more iterations: 1h 24m 5s, 500 more iterations: 7h 0m 27s. [2025-11-13 03:42:52,875][__main__][INFO] - Starting iteration 385. [2025-11-13 03:42:53,351][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 38 and human policies 1. [2025-11-13 03:42:53,352][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 03:43:01,170][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 03:43:04,455][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 03:43:04,807][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 03:43:09,284][__main__][INFO] - Number of regex retries in iteration 385: 3 [2025-11-13 03:43:09,284][__main__][INFO] - agents played in iteration 385 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 03:43:10,103][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:43:10,131][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:43:10,157][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:43:10,180][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:43:10,180][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 03:43:10,181][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 03:43:10,879][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 03:43:11,335][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 03:43:11,843][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 03:43:12,346][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 03:43:12,848][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 03:43:13,352][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 03:43:13,854][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 03:43:14,360][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 03:43:14,871][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 03:43:15,373][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 03:43:15,875][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 03:43:16,381][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 03:43:16,884][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 03:43:17,397][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 03:43:17,898][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 03:43:18,401][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 03:43:18,906][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 03:43:19,411][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 03:43:19,915][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 03:43:20,417][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 03:43:20,918][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 03:43:21,423][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 03:43:21,924][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 03:43:22,426][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 03:43:22,928][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 03:43:23,432][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 03:43:23,933][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 03:43:24,435][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 03:43:24,937][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 03:43:25,444][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 03:43:25,947][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 03:43:26,448][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 03:43:26,950][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 03:43:27,451][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 03:43:27,953][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 03:43:28,453][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 03:43:28,954][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 03:43:29,455][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 03:43:29,961][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 03:43:30,463][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 03:43:30,980][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 03:43:31,486][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 03:43:31,997][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 03:43:32,504][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 03:43:33,009][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 03:43:33,518][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 03:43:34,024][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 03:43:34,530][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 03:43:35,035][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 03:43:35,538][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 03:43:36,046][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 03:43:36,552][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 03:43:37,057][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 03:43:37,562][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 03:43:38,067][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 03:43:38,583][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 03:43:39,086][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 03:43:39,590][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 03:43:40,105][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 03:43:40,610][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 03:43:41,119][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 03:43:41,625][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 03:43:42,130][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 03:43:42,637][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 03:43:43,142][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10875 tokens. [2025-11-13 03:43:43,883][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.19%, Current % of VRAM taken: 59.64%, Block Peak % of device VRAM: 62.31%, ΔTime: 00:00:33 [2025-11-13 03:43:44,664][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 03:43:44,666][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 03:43:44,668][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 03:43:45,702][__main__][INFO] - Iteration 386 took 52s (30.43% Gen, 67.59% Train). Generation: 15s, Training: 35s. Estimated remaining time: 38h 3m 29s. Estimated total time: 43h 37m 33s. Time estimates for 10 more iterations: 8m 43s, 100 more iterations: 1h 27m 15s, 500 more iterations: 7h 16m 15s. [2025-11-13 03:43:45,704][__main__][INFO] - Starting iteration 386. [2025-11-13 03:43:46,174][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 38 and human policies 1. [2025-11-13 03:43:46,174][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 03:44:00,899][__main__][INFO] - Number of regex retries in iteration 386: 0 [2025-11-13 03:44:00,900][__main__][INFO] - agents played in iteration 386 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 03:44:01,721][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:44:01,749][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:44:01,776][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:44:01,799][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:44:01,800][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 03:44:01,801][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 03:44:02,496][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 03:44:02,954][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 03:44:03,462][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 03:44:03,964][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 03:44:04,465][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 03:44:04,969][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 03:44:05,471][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 03:44:05,970][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 03:44:06,473][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 03:44:06,979][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 03:44:07,483][mllm.training.trainer_common][INFO] - 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Processing mini-batch 21 of 64 [2025-11-13 03:44:13,563][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 03:44:14,069][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 03:44:14,578][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 03:44:15,083][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 03:44:15,584][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 03:44:16,087][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 03:44:16,590][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 03:44:17,091][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 03:44:17,592][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 03:44:18,100][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 03:44:18,602][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 03:44:19,107][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 03:44:19,607][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 03:44:20,108][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 03:44:20,609][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 03:44:21,109][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 03:44:21,610][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 03:44:22,114][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 03:44:22,618][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 03:44:23,120][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 03:44:23,626][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 03:44:24,127][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 03:44:24,632][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 03:44:25,137][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 03:44:25,640][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 03:44:26,141][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 03:44:26,644][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 03:44:27,154][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 03:44:27,658][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 03:44:28,160][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 03:44:28,663][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 03:44:29,167][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 03:44:29,670][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 03:44:30,171][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 03:44:30,673][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 03:44:31,180][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 03:44:31,682][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 03:44:32,185][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 03:44:32,690][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 03:44:33,192][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 03:44:33,696][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 03:44:34,198][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 03:44:34,702][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10803 tokens. [2025-11-13 03:44:35,476][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.20%, Current % of VRAM taken: 59.65%, Block Peak % of device VRAM: 62.28%, ΔTime: 00:00:32 [2025-11-13 03:44:36,270][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 03:44:36,272][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 03:44:36,273][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 03:44:37,226][__main__][INFO] - Iteration 387 took 51s (28.84% Gen, 69.29% Train). Generation: 14s, Training: 35s. Estimated remaining time: 36h 57m 42s. Estimated total time: 42h 32m 38s. Time estimates for 10 more iterations: 8m 30s, 100 more iterations: 1h 25m 5s, 500 more iterations: 7h 5m 26s. [2025-11-13 03:44:37,228][__main__][INFO] - Starting iteration 387. [2025-11-13 03:44:37,710][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 38 and human policies 1. [2025-11-13 03:44:37,711][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 03:44:51,025][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 03:44:53,531][__main__][INFO] - Number of regex retries in iteration 387: 1 [2025-11-13 03:44:53,532][__main__][INFO] - agents played in iteration 387 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 03:44:54,325][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:44:54,359][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:44:54,390][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:44:54,415][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:44:54,416][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 03:44:54,416][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 03:44:55,109][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 03:44:55,565][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 03:44:56,200][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 03:44:56,703][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 03:44:57,207][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 03:44:57,709][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 03:44:58,209][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 03:44:58,715][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 03:44:59,217][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 03:44:59,726][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 03:45:00,231][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 03:45:11,806][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 03:45:12,306][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 03:45:12,808][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 03:45:13,315][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 03:45:13,820][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 03:45:14,321][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 03:45:14,832][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 03:45:15,336][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 03:45:15,846][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 03:45:16,352][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 03:45:16,858][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 03:45:17,365][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 03:45:17,869][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 03:45:18,371][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 03:45:18,874][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 03:45:19,377][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 03:45:19,886][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 03:45:20,389][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 03:45:20,892][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 03:45:21,396][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 03:45:21,900][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 03:45:22,404][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 03:45:22,907][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 03:45:23,412][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 03:45:23,918][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 03:45:24,422][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 03:45:24,925][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 03:45:25,433][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 03:45:25,938][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 03:45:26,449][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 03:45:26,953][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 03:45:27,455][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10851 tokens. [2025-11-13 03:45:28,209][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.19%, Current % of VRAM taken: 59.64%, Block Peak % of device VRAM: 62.52%, ΔTime: 00:00:33 [2025-11-13 03:45:29,003][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 03:45:29,005][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 03:45:29,007][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 03:45:29,935][__main__][INFO] - Iteration 388 took 52s (30.29% Gen, 67.93% Train). Generation: 15s, Training: 35s. Estimated remaining time: 37h 55m 28s. Estimated total time: 43h 31m 16s. Time estimates for 10 more iterations: 8m 42s, 100 more iterations: 1h 27m 2s, 500 more iterations: 7h 15m 12s. [2025-11-13 03:45:29,938][__main__][INFO] - Starting iteration 388. [2025-11-13 03:45:30,426][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 38 and human policies 1. [2025-11-13 03:45:30,426][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 03:45:45,236][__main__][INFO] - Number of regex retries in iteration 388: 0 [2025-11-13 03:45:45,236][__main__][INFO] - agents played in iteration 388 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 03:45:46,073][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:45:46,095][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:45:46,118][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:45:46,140][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:45:46,141][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 03:45:46,142][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 03:45:46,846][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 03:45:47,305][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 03:45:47,812][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 03:45:48,313][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 03:45:48,817][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 03:45:49,320][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 03:45:49,823][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 03:45:50,335][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 03:45:50,837][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 03:45:51,353][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 03:45:51,855][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 03:45:52,357][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 03:45:52,857][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 03:45:53,357][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 03:45:53,862][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 03:45:54,363][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 03:45:54,864][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 03:45:55,367][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 03:45:55,869][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 03:45:56,373][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 03:45:56,874][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 03:45:57,375][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 03:46:03,409][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 03:46:03,912][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 03:46:04,420][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 03:46:04,924][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 03:46:05,426][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 03:46:05,937][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 03:46:06,439][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 03:46:06,948][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 03:46:07,450][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 03:46:07,954][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 03:46:08,461][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 03:46:08,961][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 03:46:09,463][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 03:46:09,970][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 03:46:10,474][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 03:46:10,977][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 03:46:11,484][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 03:46:11,987][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 03:46:12,489][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 03:46:12,992][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 03:46:13,493][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 03:46:13,996][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 03:46:14,499][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 03:46:15,003][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 03:46:15,506][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 03:46:16,011][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 03:46:16,519][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 03:46:17,025][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 03:46:17,528][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 03:46:18,033][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 03:46:18,538][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 03:46:19,056][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10880 tokens. [2025-11-13 03:46:19,816][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.21%, Current % of VRAM taken: 59.66%, Block Peak % of device VRAM: 62.32%, ΔTime: 00:00:32 [2025-11-13 03:46:20,600][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 03:46:20,601][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 03:46:20,603][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 03:46:21,579][__main__][INFO] - Iteration 389 took 51s (28.95% Gen, 69.14% Train). Generation: 14s, Training: 35s. Estimated remaining time: 37h 1m 2s. Estimated total time: 42h 37m 42s. Time estimates for 10 more iterations: 8m 31s, 100 more iterations: 1h 25m 15s, 500 more iterations: 7h 6m 17s. [2025-11-13 03:46:21,583][__main__][INFO] - Starting iteration 389. [2025-11-13 03:46:22,063][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 38 and human policies 1. [2025-11-13 03:46:22,063][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 03:46:26,619][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 03:46:37,190][__main__][INFO] - Number of regex retries in iteration 389: 1 [2025-11-13 03:46:37,190][__main__][INFO] - agents played in iteration 389 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 03:46:38,035][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:46:38,058][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:46:38,080][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:46:38,102][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:46:38,103][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 03:46:38,103][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 03:46:38,820][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 03:46:39,279][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 03:46:39,785][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 03:46:40,290][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 03:46:40,796][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 03:46:41,299][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 03:46:41,805][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 03:46:42,308][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 03:46:42,811][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 03:46:43,314][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 03:46:43,815][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 03:46:55,391][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 03:46:55,896][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 03:46:56,398][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 03:46:56,900][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 03:46:57,406][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 03:46:57,909][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 03:46:58,409][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 03:46:58,911][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 03:46:59,411][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 03:46:59,911][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 03:47:00,413][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 03:47:00,912][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 03:47:01,412][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 03:47:01,911][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 03:47:02,412][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 03:47:02,915][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 03:47:03,425][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 03:47:03,938][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 03:47:04,444][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 03:47:04,949][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 03:47:05,457][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 03:47:05,962][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 03:47:06,472][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 03:47:06,980][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 03:47:07,483][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 03:47:07,991][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 03:47:08,497][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 03:47:09,003][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 03:47:09,507][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 03:47:10,012][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 03:47:10,516][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 03:47:11,026][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10873 tokens. [2025-11-13 03:47:11,808][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.31%, Current % of VRAM taken: 59.76%, Block Peak % of device VRAM: 62.52%, ΔTime: 00:00:32 [2025-11-13 03:47:12,593][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 03:47:12,595][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 03:47:12,599][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 03:47:13,563][__main__][INFO] - Iteration 390 took 51s (29.37% Gen, 68.75% Train). Generation: 15s, Training: 35s. Estimated remaining time: 37h 17m 29s. Estimated total time: 42h 55m 1s. Time estimates for 10 more iterations: 8m 35s, 100 more iterations: 1h 25m 50s, 500 more iterations: 7h 9m 10s. [2025-11-13 03:47:13,565][__main__][INFO] - Starting iteration 390. [2025-11-13 03:47:14,038][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 38 and human policies 1. [2025-11-13 03:47:14,038][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 03:47:19,384][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 03:47:26,404][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 03:47:29,678][__main__][INFO] - Number of regex retries in iteration 390: 2 [2025-11-13 03:47:29,678][__main__][INFO] - agents played in iteration 390 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 03:47:30,499][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:47:30,523][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:47:30,548][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:47:30,571][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:47:30,572][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 03:47:30,573][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 03:47:31,358][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 03:47:31,822][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 03:47:32,332][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 03:47:32,838][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 03:47:33,355][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 03:47:33,860][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 03:47:34,379][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 03:47:34,883][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 03:47:35,388][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 03:47:35,889][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 03:47:36,392][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 03:47:36,903][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 03:47:37,406][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 03:47:37,909][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 03:47:38,411][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 03:47:38,913][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 03:47:39,416][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 03:47:39,917][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 03:47:40,418][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 03:47:40,921][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 03:47:41,424][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 03:47:41,927][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 03:47:47,990][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 03:47:48,496][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 03:47:48,998][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 03:47:49,500][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 03:47:50,005][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 03:47:50,507][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 03:47:51,010][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 03:47:51,516][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 03:47:52,019][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 03:47:52,524][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 03:47:53,031][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 03:47:53,532][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 03:47:54,035][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 03:47:54,534][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 03:47:55,035][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 03:47:55,541][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 03:47:56,044][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 03:47:56,545][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 03:47:57,045][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 03:47:57,549][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 03:47:58,057][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 03:47:58,563][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 03:47:59,070][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 03:47:59,578][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 03:48:00,084][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 03:48:00,586][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 03:48:01,090][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 03:48:01,595][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 03:48:02,097][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 03:48:02,601][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 03:48:03,106][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 03:48:03,612][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10877 tokens. [2025-11-13 03:48:04,430][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.23%, Current % of VRAM taken: 59.68%, Block Peak % of device VRAM: 62.31%, ΔTime: 00:00:33 [2025-11-13 03:48:05,214][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 03:48:05,218][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 03:48:05,220][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 03:48:07,311][__main__][INFO] - Iteration 391 took 53s (29.36% Gen, 66.71% Train). Generation: 15s, Training: 35s. Estimated remaining time: 38h 45m 16s. Estimated total time: 44h 23m 42s. Time estimates for 10 more iterations: 8m 52s, 100 more iterations: 1h 28m 47s, 500 more iterations: 7h 23m 57s. [2025-11-13 03:48:07,313][__main__][INFO] - Starting iteration 391. [2025-11-13 03:48:07,785][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 39 and human policies 1. [2025-11-13 03:48:07,786][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 03:48:12,436][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 03:48:23,677][__main__][INFO] - Number of regex retries in iteration 391: 1 [2025-11-13 03:48:23,678][__main__][INFO] - agents played in iteration 391 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 03:48:24,527][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:48:24,555][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:48:24,582][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:48:24,605][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:48:24,606][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 03:48:24,607][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 03:48:25,412][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 03:48:25,889][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 03:48:26,401][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 03:48:26,909][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 03:48:27,412][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 03:48:27,918][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 03:48:28,423][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 03:48:28,928][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 03:48:29,430][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 03:48:29,931][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 03:48:30,430][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 03:48:41,969][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 03:48:42,470][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 03:48:42,980][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 03:48:43,484][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 03:48:43,986][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 03:48:44,492][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 03:48:44,993][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 03:48:45,506][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 03:48:46,014][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 03:48:46,518][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 03:48:47,025][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 03:48:47,525][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 03:48:48,026][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 03:48:48,527][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 03:48:49,028][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 03:48:49,530][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 03:48:50,030][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 03:48:50,529][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 03:48:51,029][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 03:48:51,530][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 03:48:52,033][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 03:48:52,537][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 03:48:53,039][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 03:48:53,545][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 03:48:54,047][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 03:48:54,550][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 03:48:55,051][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 03:48:55,555][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 03:48:56,061][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 03:48:56,565][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 03:48:57,069][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 03:48:57,575][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10858 tokens. [2025-11-13 03:48:58,310][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.23%, Current % of VRAM taken: 59.68%, Block Peak % of device VRAM: 62.29%, ΔTime: 00:00:32 [2025-11-13 03:48:59,082][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 03:48:59,083][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 03:48:59,085][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 03:49:00,016][__main__][INFO] - Iteration 392 took 52s (30.43% Gen, 67.79% Train). Generation: 15s, Training: 35s. Estimated remaining time: 37h 52m 17s. Estimated total time: 43h 31m 35s. Time estimates for 10 more iterations: 8m 42s, 100 more iterations: 1h 27m 3s, 500 more iterations: 7h 15m 15s. [2025-11-13 03:49:00,018][__main__][INFO] - Starting iteration 392. [2025-11-13 03:49:00,491][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 39 and human policies 1. [2025-11-13 03:49:00,492][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 03:49:15,563][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 03:49:24,368][__main__][INFO] - Number of regex retries in iteration 392: 1 [2025-11-13 03:49:24,369][__main__][INFO] - agents played in iteration 392 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 03:49:25,246][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:49:25,275][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:49:25,301][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:49:25,324][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:49:25,324][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 03:49:25,325][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 03:49:26,108][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 03:49:26,578][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 03:49:27,092][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 03:49:27,618][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 03:49:28,129][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 03:49:28,641][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 03:49:29,149][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 03:49:29,656][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 03:49:30,166][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 03:49:30,676][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 03:49:31,185][mllm.training.trainer_common][INFO] - 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Processing mini-batch 43 of 64 [2025-11-13 03:49:48,567][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 03:49:49,075][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 03:49:49,583][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 03:49:50,091][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 03:49:50,598][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 03:49:51,119][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 03:49:51,627][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 03:49:52,136][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 03:49:52,647][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 03:49:53,153][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 03:49:53,661][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 03:49:54,168][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 03:49:54,675][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 03:49:55,189][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 03:49:55,694][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 03:49:56,204][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 03:49:56,712][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 03:49:57,219][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 03:49:57,737][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 03:49:58,248][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 03:49:58,755][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10880 tokens. [2025-11-13 03:49:59,551][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.19%, Current % of VRAM taken: 59.65%, Block Peak % of device VRAM: 62.35%, ΔTime: 00:00:33 [2025-11-13 03:50:00,231][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 03:50:00,234][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 03:50:00,236][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 03:50:01,128][__main__][INFO] - Iteration 393 took 1m 0s (39.38% Gen, 59.15% Train). Generation: 23s, Training: 35s. Estimated remaining time: 44h 51m 33s. Estimated total time: 50h 31m 52s. Time estimates for 10 more iterations: 10m 6s, 100 more iterations: 1h 41m 3s, 500 more iterations: 8h 25m 18s. [2025-11-13 03:50:01,130][__main__][INFO] - Starting iteration 393. [2025-11-13 03:50:01,637][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 39 and human policies 1. [2025-11-13 03:50:01,638][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 03:50:25,921][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 03:50:26,498][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 03:50:31,971][__main__][INFO] - Number of regex retries in iteration 393: 2 [2025-11-13 03:50:31,972][__main__][INFO] - agents played in iteration 393 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 03:50:32,804][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:50:32,832][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:50:32,858][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:50:32,881][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:50:32,882][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 03:50:32,883][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 03:50:33,566][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 03:50:34,030][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 03:50:34,539][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 03:50:35,046][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 03:50:35,553][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 03:50:36,055][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 03:50:36,563][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 03:50:37,067][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 03:50:37,572][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 03:50:38,082][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 03:50:38,586][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 03:50:50,298][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 03:50:50,807][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 03:50:51,330][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 03:50:51,840][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 03:50:52,349][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 03:50:52,853][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 03:50:53,356][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 03:50:53,858][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 03:50:54,360][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 03:50:54,864][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 03:50:55,366][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 03:50:55,867][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 03:50:56,373][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 03:50:56,878][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 03:50:57,383][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 03:50:57,890][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 03:50:58,394][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 03:50:58,892][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 03:50:59,408][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 03:50:59,915][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 03:51:00,434][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 03:51:00,942][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 03:51:01,448][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 03:51:01,952][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 03:51:02,454][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 03:51:02,958][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 03:51:03,467][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 03:51:03,978][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 03:51:04,484][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 03:51:04,994][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 03:51:05,501][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 03:51:06,010][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10869 tokens. [2025-11-13 03:51:06,825][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.23%, Current % of VRAM taken: 59.68%, Block Peak % of device VRAM: 62.41%, ΔTime: 00:00:33 [2025-11-13 03:51:07,587][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 03:51:07,589][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 03:51:07,591][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 03:51:08,520][__main__][INFO] - Iteration 394 took 1m 6s (45.35% Gen, 53.26% Train). Generation: 30s, Training: 35s. Estimated remaining time: 50h 2m 42s. Estimated total time: 55h 44m 9s. Time estimates for 10 more iterations: 11m 8s, 100 more iterations: 1h 51m 28s, 500 more iterations: 9h 17m 21s. [2025-11-13 03:51:08,522][__main__][INFO] - Starting iteration 394. [2025-11-13 03:51:09,021][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 39 and human policies 1. [2025-11-13 03:51:09,021][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 03:51:27,965][__main__][INFO] - Number of regex retries in iteration 394: 0 [2025-11-13 03:51:27,966][__main__][INFO] - agents played in iteration 394 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 03:51:28,811][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:51:28,838][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:51:28,865][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:51:28,888][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:51:28,889][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 03:51:28,889][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 03:51:29,584][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 03:51:30,042][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 03:51:30,552][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 03:51:31,056][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 03:51:31,557][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 03:51:32,065][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 03:51:32,567][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 03:51:33,071][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 03:51:33,576][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 03:51:34,080][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 03:51:34,586][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 03:51:35,087][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 03:51:35,590][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 03:51:36,093][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 03:51:36,599][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 03:51:37,104][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 03:51:37,607][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 03:51:38,110][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 03:51:38,628][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 03:51:39,129][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 03:51:39,632][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 03:51:40,137][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 03:51:46,222][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 03:51:46,731][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 03:51:47,234][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 03:51:47,748][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 03:51:48,251][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 03:51:48,755][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 03:51:49,256][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 03:51:49,757][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 03:51:50,264][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 03:51:50,765][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 03:51:51,267][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 03:51:51,772][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 03:51:52,274][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 03:51:52,777][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 03:51:53,280][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 03:51:53,782][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 03:51:54,289][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 03:51:54,792][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 03:51:55,293][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 03:51:55,797][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 03:51:56,299][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 03:51:56,801][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 03:51:57,303][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 03:51:57,807][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 03:51:58,312][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 03:51:58,817][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 03:51:59,322][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 03:51:59,830][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 03:52:00,335][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 03:52:00,847][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 03:52:01,354][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 03:52:01,861][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10878 tokens. [2025-11-13 03:52:02,659][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.25%, Current % of VRAM taken: 59.70%, Block Peak % of device VRAM: 62.41%, ΔTime: 00:00:33 [2025-11-13 03:52:03,436][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 03:52:03,438][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 03:52:03,440][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 03:52:04,356][__main__][INFO] - Iteration 395 took 55s (34.24% Gen, 64.11% Train). Generation: 18s, Training: 35s. Estimated remaining time: 40h 24m 23s. Estimated total time: 46h 6m 46s. Time estimates for 10 more iterations: 9m 13s, 100 more iterations: 1h 32m 13s, 500 more iterations: 7h 41m 7s. [2025-11-13 03:52:04,358][__main__][INFO] - Starting iteration 395. [2025-11-13 03:52:04,863][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 39 and human policies 1. [2025-11-13 03:52:04,864][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 03:52:13,082][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 03:52:13,147][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 03:52:13,922][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 03:52:17,795][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 03:52:23,936][__main__][INFO] - Number of regex retries in iteration 395: 4 [2025-11-13 03:52:23,936][__main__][INFO] - agents played in iteration 395 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 03:52:24,786][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:52:24,814][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:52:24,841][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:52:24,865][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:52:24,866][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 03:52:24,867][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 03:52:25,613][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 03:52:26,074][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 03:52:26,585][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 03:52:27,088][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 03:52:27,587][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 03:52:28,086][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 03:52:28,591][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 03:52:29,089][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 03:52:29,588][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 03:52:30,093][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 03:52:30,592][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 03:52:31,094][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 03:52:31,600][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 03:52:32,106][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 03:52:32,611][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 03:52:33,113][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 03:52:33,613][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 03:52:34,114][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 03:52:34,616][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 03:52:35,138][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 03:52:35,642][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 03:52:36,146][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 03:52:36,653][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 03:52:37,155][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 03:52:37,664][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 03:52:38,170][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 03:52:38,679][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 03:52:39,189][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 03:52:39,695][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 03:52:40,198][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 03:52:40,701][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 03:52:41,204][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 03:52:41,711][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 03:52:42,218][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 03:52:42,729][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 03:52:43,246][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 03:52:43,750][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 03:52:44,266][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 03:52:44,768][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 03:52:45,268][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 03:52:45,769][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 03:52:46,270][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 03:52:46,775][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 03:52:47,275][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 03:52:47,777][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 03:52:48,284][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 03:52:48,785][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 03:52:49,286][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 03:52:49,787][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 03:52:50,288][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 03:52:50,791][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 03:52:51,293][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 03:52:51,794][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 03:52:52,295][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 03:52:52,797][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 03:52:53,298][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 03:52:53,798][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 03:52:54,298][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 03:52:54,797][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 03:52:55,292][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 03:52:55,791][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 03:52:56,290][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 03:52:56,792][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 03:52:57,293][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 03:52:57,795][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10868 tokens. [2025-11-13 03:52:58,547][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.12%, Current % of VRAM taken: 59.57%, Block Peak % of device VRAM: 62.18%, ΔTime: 00:00:32 [2025-11-13 03:52:59,289][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 03:52:59,291][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 03:52:59,293][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 03:53:00,643][__main__][INFO] - Iteration 396 took 55s (34.19% Gen, 63.39% Train). Generation: 19s, Training: 35s. Estimated remaining time: 40h 45m 42s. Estimated total time: 46h 29m 1s. Time estimates for 10 more iterations: 9m 17s, 100 more iterations: 1h 32m 58s, 500 more iterations: 7h 44m 50s. [2025-11-13 03:53:00,645][__main__][INFO] - Starting iteration 396. [2025-11-13 03:53:01,144][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 39 and human policies 1. [2025-11-13 03:53:01,145][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 03:53:05,068][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 03:53:11,547][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 03:53:15,004][__main__][INFO] - Number of regex retries in iteration 396: 2 [2025-11-13 03:53:15,005][__main__][INFO] - agents played in iteration 396 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 03:53:15,826][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.52%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:53:15,853][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.52%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:53:15,879][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.52%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:53:15,902][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.52%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:53:15,902][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 03:53:15,903][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 03:53:16,599][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 03:53:17,056][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 03:53:17,563][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 03:53:18,083][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 03:53:18,586][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 03:53:19,091][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 03:53:19,594][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 03:53:20,096][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 03:53:20,600][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 03:53:21,099][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 03:53:21,603][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 03:53:22,109][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 03:53:22,613][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 03:53:23,115][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 03:53:23,616][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 03:53:24,122][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 03:53:24,627][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 03:53:25,133][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 03:53:25,636][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 03:53:26,137][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 03:53:26,637][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 03:53:27,151][mllm.training.trainer_common][INFO] - 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Processing mini-batch 43 of 64 [2025-11-13 03:53:38,771][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 03:53:39,273][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 03:53:39,774][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 03:53:40,277][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 03:53:40,778][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 03:53:41,282][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 03:53:41,784][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 03:53:42,287][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 03:53:42,793][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 03:53:43,296][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 03:53:43,799][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 03:53:44,301][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 03:53:44,803][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 03:53:45,308][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 03:53:45,811][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 03:53:46,313][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 03:53:46,818][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 03:53:47,319][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 03:53:47,822][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 03:53:48,327][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 03:53:48,830][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10880 tokens. [2025-11-13 03:53:49,563][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.22%, Current % of VRAM taken: 59.67%, Block Peak % of device VRAM: 62.42%, ΔTime: 00:00:32 [2025-11-13 03:53:50,292][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 03:53:50,294][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 03:53:50,295][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 03:53:51,209][__main__][INFO] - Iteration 397 took 50s (27.68% Gen, 70.49% Train). Generation: 13s, Training: 35s. Estimated remaining time: 35h 59m 6s. Estimated total time: 41h 43m 16s. Time estimates for 10 more iterations: 8m 20s, 100 more iterations: 1h 23m 26s, 500 more iterations: 6h 57m 12s. [2025-11-13 03:53:51,211][__main__][INFO] - Starting iteration 397. [2025-11-13 03:53:51,691][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 39 and human policies 1. [2025-11-13 03:53:51,692][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 03:54:03,837][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 03:54:09,263][__main__][INFO] - Number of regex retries in iteration 397: 1 [2025-11-13 03:54:09,264][__main__][INFO] - agents played in iteration 397 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 03:54:10,080][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:54:10,108][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:54:10,135][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:54:10,159][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:54:10,160][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 03:54:10,161][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 03:54:10,864][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 03:54:11,320][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 03:54:11,829][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 03:54:12,335][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 03:54:12,838][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 03:54:13,349][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 03:54:13,852][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 03:54:14,356][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 03:54:14,866][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 03:54:15,367][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 03:54:15,881][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 03:54:27,454][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 03:54:27,968][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 03:54:28,474][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 03:54:28,992][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 03:54:29,494][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 03:54:29,998][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 03:54:30,507][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 03:54:31,012][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 03:54:31,515][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 03:54:32,018][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 03:54:32,521][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 03:54:33,025][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 03:54:33,527][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 03:54:34,030][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 03:54:34,532][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 03:54:35,035][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 03:54:35,539][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 03:54:36,041][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 03:54:36,542][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 03:54:37,050][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 03:54:37,553][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 03:54:38,057][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 03:54:38,564][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 03:54:39,068][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 03:54:39,587][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 03:54:40,094][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 03:54:40,598][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 03:54:41,101][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 03:54:41,603][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 03:54:42,107][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 03:54:42,607][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 03:54:43,109][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10877 tokens. [2025-11-13 03:54:43,878][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.17%, Current % of VRAM taken: 59.62%, Block Peak % of device VRAM: 62.28%, ΔTime: 00:00:33 [2025-11-13 03:54:44,622][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 03:54:44,624][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 03:54:44,626][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 03:54:45,540][__main__][INFO] - Iteration 398 took 53s (32.63% Gen, 65.67% Train). Generation: 17s, Training: 35s. Estimated remaining time: 39h 7m 25s. Estimated total time: 44h 52m 29s. Time estimates for 10 more iterations: 8m 58s, 100 more iterations: 1h 29m 44s, 500 more iterations: 7h 28m 44s. [2025-11-13 03:54:45,542][__main__][INFO] - Starting iteration 398. [2025-11-13 03:54:46,023][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 39 and human policies 1. [2025-11-13 03:54:46,023][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 03:54:59,834][__main__][INFO] - Number of regex retries in iteration 398: 0 [2025-11-13 03:54:59,835][__main__][INFO] - agents played in iteration 398 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 03:55:00,632][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:55:00,657][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:55:00,682][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:55:00,705][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:55:00,706][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 03:55:00,707][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 03:55:01,472][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 03:55:01,933][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 03:55:02,443][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 03:55:02,949][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 03:55:03,455][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 03:55:03,962][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 03:55:04,475][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 03:55:04,979][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 03:55:05,492][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 03:55:05,997][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 03:55:06,500][mllm.training.trainer_common][INFO] - 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Processing mini-batch 43 of 64 [2025-11-13 03:55:23,675][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 03:55:24,178][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 03:55:24,682][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 03:55:25,207][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 03:55:25,712][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 03:55:26,220][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 03:55:26,722][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 03:55:27,224][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 03:55:27,728][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 03:55:28,230][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 03:55:28,732][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 03:55:29,239][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 03:55:29,741][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 03:55:30,244][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 03:55:30,749][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 03:55:31,251][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 03:55:31,753][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 03:55:32,256][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 03:55:32,757][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 03:55:33,259][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 03:55:33,763][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10880 tokens. [2025-11-13 03:55:34,485][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.25%, Current % of VRAM taken: 59.71%, Block Peak % of device VRAM: 62.49%, ΔTime: 00:00:33 [2025-11-13 03:55:35,226][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 03:55:35,228][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 03:55:35,230][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 03:55:36,165][__main__][INFO] - Iteration 399 took 50s (27.54% Gen, 70.59% Train). Generation: 13s, Training: 35s. Estimated remaining time: 36h 1m 14s. Estimated total time: 41h 47m 8s. Time estimates for 10 more iterations: 8m 21s, 100 more iterations: 1h 23m 34s, 500 more iterations: 6h 57m 51s. [2025-11-13 03:55:36,167][__main__][INFO] - Starting iteration 399. [2025-11-13 03:55:36,657][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 39 and human policies 1. [2025-11-13 03:55:36,658][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 03:55:50,635][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 03:55:51,433][__main__][INFO] - Number of regex retries in iteration 399: 1 [2025-11-13 03:55:51,433][__main__][INFO] - agents played in iteration 399 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 03:55:52,227][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:55:52,256][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:55:52,283][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:55:52,307][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:55:52,308][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 03:55:52,309][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 03:55:53,035][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 03:55:53,494][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 03:55:54,013][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 03:55:54,518][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 03:55:55,022][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 03:55:55,533][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 03:55:56,037][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 03:55:56,542][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 03:55:57,045][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 03:55:57,552][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 03:55:58,057][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 03:56:09,657][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 03:56:10,161][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 03:56:10,663][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 03:56:11,167][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 03:56:11,671][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 03:56:12,172][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 03:56:12,674][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 03:56:13,177][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 03:56:13,681][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 03:56:14,184][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 03:56:14,686][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 03:56:15,189][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 03:56:15,692][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 03:56:16,193][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 03:56:16,708][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 03:56:17,210][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 03:56:17,720][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 03:56:18,220][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 03:56:18,722][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 03:56:19,233][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 03:56:19,733][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 03:56:20,235][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 03:56:20,736][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 03:56:21,239][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 03:56:21,747][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 03:56:22,251][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 03:56:22,758][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 03:56:23,260][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 03:56:23,763][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 03:56:24,268][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 03:56:24,770][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 03:56:25,273][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10879 tokens. [2025-11-13 03:56:26,001][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.32%, Current % of VRAM taken: 59.77%, Block Peak % of device VRAM: 62.49%, ΔTime: 00:00:32 [2025-11-13 03:56:26,746][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 03:56:26,749][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 03:56:26,752][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 03:56:28,107][__main__][INFO] - Iteration 400 took 51s (28.72% Gen, 68.65% Train). Generation: 14s, Training: 35s. Estimated remaining time: 37h 5m 45s. Estimated total time: 42h 52m 31s. Time estimates for 10 more iterations: 8m 34s, 100 more iterations: 1h 25m 45s, 500 more iterations: 7h 8m 45s. [2025-11-13 03:56:28,109][__main__][INFO] - Starting iteration 400. [2025-11-13 03:56:28,603][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 39 and human policies 1. [2025-11-13 03:56:28,604][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 03:56:43,143][__main__][INFO] - Number of regex retries in iteration 400: 0 [2025-11-13 03:56:43,143][__main__][INFO] - agents played in iteration 400 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 03:56:43,929][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.62%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:56:43,956][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.62%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:56:43,983][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.62%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:56:44,006][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.62%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:56:44,007][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 03:56:44,007][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 03:56:44,720][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 03:56:45,179][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 03:56:45,688][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 03:56:46,190][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 03:56:46,693][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 03:56:47,195][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 03:56:47,700][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 03:56:48,202][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 03:56:48,705][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 03:56:49,216][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 03:56:49,725][mllm.training.trainer_common][INFO] - 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Processing mini-batch 43 of 64 [2025-11-13 03:57:06,853][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 03:57:07,354][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 03:57:07,855][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 03:57:08,359][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 03:57:08,867][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 03:57:09,371][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 03:57:09,878][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 03:57:10,383][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 03:57:10,886][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 03:57:11,391][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 03:57:11,895][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 03:57:12,398][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 03:57:12,902][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 03:57:13,406][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 03:57:13,910][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 03:57:14,413][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 03:57:14,916][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 03:57:15,419][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 03:57:15,922][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 03:57:16,423][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 03:57:16,936][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10878 tokens. [2025-11-13 03:57:17,650][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.25%, Current % of VRAM taken: 59.70%, Block Peak % of device VRAM: 62.28%, ΔTime: 00:00:32 [2025-11-13 03:57:18,427][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 03:57:18,429][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 03:57:18,430][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 03:57:20,275][__main__][INFO] - Iteration 401 took 51s (28.14% Gen, 68.29% Train). Generation: 14s, Training: 35s. Estimated remaining time: 37h 15m 59s. Estimated total time: 43h 3m 38s. Time estimates for 10 more iterations: 8m 36s, 100 more iterations: 1h 26m 7s, 500 more iterations: 7h 10m 36s. [2025-11-13 03:57:20,278][__main__][INFO] - Starting iteration 401. [2025-11-13 03:57:20,764][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 40 and human policies 1. [2025-11-13 03:57:20,764][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 03:57:26,314][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 03:57:29,491][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 03:57:30,486][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 03:57:37,060][__main__][INFO] - Number of regex retries in iteration 401: 3 [2025-11-13 03:57:37,061][__main__][INFO] - agents played in iteration 401 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 03:57:37,902][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:57:37,929][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:57:37,955][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:57:37,978][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:57:37,979][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 03:57:37,980][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 03:57:38,696][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 03:57:39,159][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 03:57:39,669][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 03:57:40,177][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 03:57:40,681][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 03:57:41,185][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 03:57:41,689][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 03:57:42,195][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 03:57:42,698][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 03:57:43,200][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 03:57:43,703][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 03:57:55,316][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 03:57:55,828][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 03:57:56,331][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 03:57:56,836][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 03:57:57,345][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 03:57:57,849][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 03:57:58,367][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 03:57:58,868][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 03:57:59,370][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 03:57:59,869][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 03:58:00,369][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 03:58:00,870][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 03:58:01,370][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 03:58:01,874][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 03:58:02,382][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 03:58:02,884][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 03:58:03,390][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 03:58:03,893][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 03:58:04,394][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 03:58:04,898][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 03:58:05,402][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 03:58:05,904][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 03:58:06,407][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 03:58:06,912][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 03:58:07,416][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 03:58:07,919][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 03:58:08,419][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 03:58:08,922][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 03:58:09,423][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 03:58:09,925][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 03:58:10,427][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 03:58:10,929][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10879 tokens. [2025-11-13 03:58:11,650][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.22%, Current % of VRAM taken: 59.67%, Block Peak % of device VRAM: 62.32%, ΔTime: 00:00:32 [2025-11-13 03:58:12,400][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 03:58:12,402][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 03:58:12,403][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 03:58:13,418][__main__][INFO] - Iteration 402 took 52s (30.95% Gen, 67.12% Train). Generation: 16s, Training: 35s. Estimated remaining time: 38h 4m 12s. Estimated total time: 43h 52m 43s. Time estimates for 10 more iterations: 8m 46s, 100 more iterations: 1h 27m 45s, 500 more iterations: 7h 18m 47s. [2025-11-13 03:58:13,420][__main__][INFO] - Starting iteration 402. [2025-11-13 03:58:13,892][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 40 and human policies 1. [2025-11-13 03:58:13,893][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 03:58:28,603][__main__][INFO] - Number of regex retries in iteration 402: 0 [2025-11-13 03:58:28,604][__main__][INFO] - agents played in iteration 402 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 03:58:29,515][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:58:29,538][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:58:29,560][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:58:29,582][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:58:29,583][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 03:58:29,585][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 03:58:30,322][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 03:58:30,788][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 03:58:31,299][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 03:58:31,803][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 03:58:32,313][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 03:58:32,820][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 03:58:33,325][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 03:58:33,843][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 03:58:34,347][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 03:58:34,851][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 03:58:35,354][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 03:58:35,856][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 03:58:36,366][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 03:58:36,871][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 03:58:37,378][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 03:58:37,882][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 03:58:38,385][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 03:58:38,890][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 03:58:39,393][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 03:58:39,893][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 03:58:40,396][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 03:58:40,896][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 03:58:46,946][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 03:58:47,449][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 03:58:47,961][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 03:58:48,461][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 03:58:48,961][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 03:58:49,464][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 03:58:49,966][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 03:58:50,468][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 03:58:50,973][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 03:58:51,474][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 03:58:51,980][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 03:58:52,481][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 03:58:52,983][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 03:58:53,487][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 03:58:53,991][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 03:58:54,498][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 03:58:55,000][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 03:58:55,502][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 03:58:56,008][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 03:58:56,510][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 03:58:57,012][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 03:58:57,514][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 03:58:58,017][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 03:58:58,520][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 03:58:59,023][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 03:58:59,528][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 03:59:00,030][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 03:59:00,534][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 03:59:01,036][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 03:59:01,537][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 03:59:02,037][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 03:59:02,549][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10879 tokens. [2025-11-13 03:59:03,263][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.17%, Current % of VRAM taken: 59.62%, Block Peak % of device VRAM: 62.24%, ΔTime: 00:00:32 [2025-11-13 03:59:04,027][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 03:59:04,028][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 03:59:04,030][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 03:59:05,349][__main__][INFO] - Iteration 403 took 51s (28.59% Gen, 68.85% Train). Generation: 14s, Training: 35s. Estimated remaining time: 37h 3m 27s. Estimated total time: 42h 52m 50s. Time estimates for 10 more iterations: 8m 34s, 100 more iterations: 1h 25m 45s, 500 more iterations: 7h 8m 48s. [2025-11-13 03:59:05,351][__main__][INFO] - Starting iteration 403. [2025-11-13 03:59:05,826][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 40 and human policies 1. [2025-11-13 03:59:05,827][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 03:59:20,845][__main__][INFO] - Number of regex retries in iteration 403: 0 [2025-11-13 03:59:20,846][__main__][INFO] - agents played in iteration 403 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 03:59:21,699][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:59:21,725][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:59:21,747][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:59:21,769][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 03:59:21,770][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 03:59:21,771][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 03:59:22,504][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 03:59:22,964][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 03:59:23,475][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 03:59:23,978][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 03:59:24,483][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 03:59:24,985][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 03:59:25,489][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 03:59:25,994][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 03:59:26,497][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 03:59:27,006][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 03:59:27,513][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 03:59:39,135][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 03:59:39,638][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 03:59:40,142][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 03:59:40,657][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 03:59:41,164][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 03:59:41,666][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 03:59:42,172][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 03:59:42,679][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 03:59:43,184][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 03:59:43,688][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 03:59:44,190][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 03:59:44,692][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 03:59:45,193][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 03:59:45,696][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 03:59:46,197][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 03:59:46,705][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 03:59:47,208][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 03:59:47,713][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 03:59:48,217][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 03:59:48,724][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 03:59:49,233][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 03:59:49,746][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 03:59:50,250][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 03:59:50,776][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 03:59:51,277][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 03:59:51,780][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 03:59:52,282][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 03:59:52,787][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 03:59:53,296][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 03:59:53,798][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 03:59:54,300][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 03:59:54,808][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10878 tokens. [2025-11-13 03:59:55,547][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.21%, Current % of VRAM taken: 59.66%, Block Peak % of device VRAM: 62.31%, ΔTime: 00:00:33 [2025-11-13 03:59:56,324][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 03:59:56,326][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 03:59:56,328][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 03:59:57,487][__main__][INFO] - Iteration 404 took 51s (29.07% Gen, 68.68% Train). Generation: 15s, Training: 35s. Estimated remaining time: 37h 12m 49s. Estimated total time: 43h 3m 4s. Time estimates for 10 more iterations: 8m 36s, 100 more iterations: 1h 26m 6s, 500 more iterations: 7h 10m 30s. [2025-11-13 03:59:57,489][__main__][INFO] - Starting iteration 404. [2025-11-13 03:59:57,954][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 40 and human policies 1. [2025-11-13 03:59:57,955][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 04:00:04,078][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:00:12,988][__main__][INFO] - Number of regex retries in iteration 404: 1 [2025-11-13 04:00:12,989][__main__][INFO] - agents played in iteration 404 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 04:00:13,761][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:00:13,788][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:00:13,814][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:00:13,837][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:00:13,837][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 04:00:13,838][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 04:00:14,538][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 04:00:14,999][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 04:00:15,514][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 04:00:16,021][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 04:00:16,532][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 04:00:17,045][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 04:00:17,553][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 04:00:18,065][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 04:00:18,574][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 04:00:19,078][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 04:00:19,580][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 04:00:31,188][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 04:00:31,690][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 04:00:32,192][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 04:00:32,692][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 04:00:33,193][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 04:00:33,694][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 04:00:34,204][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 04:00:34,708][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 04:00:35,216][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 04:00:35,717][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 04:00:36,220][mllm.training.trainer_common][INFO] - 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Processing mini-batch 54 of 64 [2025-11-13 04:00:42,321][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 04:00:42,830][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 04:00:43,344][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 04:00:43,852][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 04:00:44,356][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 04:00:44,875][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 04:00:45,378][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 04:00:45,887][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 04:00:46,391][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 04:00:46,897][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10878 tokens. [2025-11-13 04:00:47,657][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.26%, Current % of VRAM taken: 59.71%, Block Peak % of device VRAM: 62.52%, ΔTime: 00:00:33 [2025-11-13 04:00:48,393][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 04:00:48,394][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 04:00:48,396][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 04:00:49,311][__main__][INFO] - Iteration 405 took 51s (29.27% Gen, 68.94% Train). Generation: 15s, Training: 35s. Estimated remaining time: 36h 56m 45s. Estimated total time: 42h 47m 53s. Time estimates for 10 more iterations: 8m 33s, 100 more iterations: 1h 25m 35s, 500 more iterations: 7h 7m 58s. [2025-11-13 04:00:49,313][__main__][INFO] - Starting iteration 405. [2025-11-13 04:00:49,823][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 40 and human policies 1. [2025-11-13 04:00:49,824][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 04:01:04,638][__main__][INFO] - Number of regex retries in iteration 405: 0 [2025-11-13 04:01:04,639][__main__][INFO] - agents played in iteration 405 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 04:01:05,421][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:01:05,443][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:01:05,466][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:01:05,488][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:01:05,489][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 04:01:05,490][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 04:01:06,159][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 04:01:06,616][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 04:01:07,127][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 04:01:07,628][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 04:01:08,134][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 04:01:08,636][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 04:01:09,138][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 04:01:09,642][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 04:01:10,145][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 04:01:10,651][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 04:01:11,155][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 04:01:11,658][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 04:01:12,163][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 04:01:12,664][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 04:01:13,173][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 04:01:13,682][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 04:01:14,184][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 04:01:14,699][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 04:01:15,204][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 04:01:15,711][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 04:01:16,217][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 04:01:16,721][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 04:01:17,228][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 04:01:17,732][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 04:01:18,235][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 04:01:18,741][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 04:01:19,245][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 04:01:19,748][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 04:01:20,252][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 04:01:20,755][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 04:01:21,259][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 04:01:21,761][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 04:01:22,266][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 04:01:22,780][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 04:01:23,283][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 04:01:23,786][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 04:01:24,289][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 04:01:24,791][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 04:01:25,294][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 04:01:25,796][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 04:01:26,299][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 04:01:26,807][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 04:01:27,311][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 04:01:27,817][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 04:01:28,322][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 04:01:28,826][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 04:01:29,333][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 04:01:29,835][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 04:01:30,343][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 04:01:30,847][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 04:01:31,353][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 04:01:31,859][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 04:01:32,360][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 04:01:32,860][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 04:01:33,374][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 04:01:33,878][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 04:01:34,394][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 04:01:34,896][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 04:01:35,401][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 04:01:35,906][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 04:01:36,411][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 04:01:36,919][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 04:01:37,422][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 04:01:37,925][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 04:01:38,434][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10877 tokens. [2025-11-13 04:01:39,183][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.20%, Current % of VRAM taken: 59.65%, Block Peak % of device VRAM: 62.28%, ΔTime: 00:00:33 [2025-11-13 04:01:39,922][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 04:01:39,925][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 04:01:39,926][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 04:01:40,844][__main__][INFO] - Iteration 406 took 51s (29.04% Gen, 69.16% Train). Generation: 14s, Training: 35s. Estimated remaining time: 36h 39m 5s. Estimated total time: 42h 31m 5s. Time estimates for 10 more iterations: 8m 30s, 100 more iterations: 1h 25m 2s, 500 more iterations: 7h 5m 10s. [2025-11-13 04:01:40,847][__main__][INFO] - Starting iteration 406. [2025-11-13 04:01:41,331][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 40 and human policies 1. [2025-11-13 04:01:41,332][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 04:01:46,026][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:01:46,728][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:01:50,723][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:01:56,020][__main__][INFO] - Number of regex retries in iteration 406: 3 [2025-11-13 04:01:56,020][__main__][INFO] - agents played in iteration 406 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 04:01:56,868][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:01:56,890][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:01:56,913][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:01:56,936][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:01:56,936][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 04:01:56,938][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 04:01:57,655][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 04:01:58,116][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 04:01:58,623][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 04:01:59,128][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 04:01:59,632][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 04:02:00,132][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 04:02:00,633][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 04:02:01,134][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 04:02:01,635][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 04:02:02,143][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 04:02:02,644][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 04:02:14,253][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 04:02:14,755][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 04:02:15,259][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 04:02:15,763][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 04:02:16,269][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 04:02:16,772][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 04:02:17,278][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 04:02:17,781][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 04:02:18,287][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 04:02:18,809][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 04:02:19,314][mllm.training.trainer_common][INFO] - 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Processing mini-batch 54 of 64 [2025-11-13 04:02:25,417][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 04:02:25,919][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 04:02:26,426][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 04:02:26,930][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 04:02:27,433][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 04:02:27,944][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 04:02:28,447][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 04:02:28,953][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 04:02:29,460][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 04:02:29,964][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10878 tokens. [2025-11-13 04:02:30,759][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.25%, Current % of VRAM taken: 59.70%, Block Peak % of device VRAM: 62.31%, ΔTime: 00:00:33 [2025-11-13 04:02:31,515][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 04:02:31,516][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 04:02:31,518][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 04:02:32,456][__main__][INFO] - Iteration 407 took 51s (28.73% Gen, 69.43% Train). Generation: 14s, Training: 35s. Estimated remaining time: 36h 43m 25s. Estimated total time: 42h 36m 16s. Time estimates for 10 more iterations: 8m 31s, 100 more iterations: 1h 25m 12s, 500 more iterations: 7h 6m 2s. [2025-11-13 04:02:32,458][__main__][INFO] - Starting iteration 407. [2025-11-13 04:02:32,962][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 40 and human policies 1. [2025-11-13 04:02:32,963][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 04:02:39,662][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:02:47,964][__main__][INFO] - Number of regex retries in iteration 407: 1 [2025-11-13 04:02:47,965][__main__][INFO] - agents played in iteration 407 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 04:02:48,805][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:02:48,828][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:02:48,850][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:02:48,873][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:02:48,873][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 04:02:48,874][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 04:02:49,559][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 04:02:50,020][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 04:02:50,528][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 04:02:51,034][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 04:02:51,546][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 04:02:52,049][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 04:02:52,550][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 04:02:53,057][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 04:02:53,557][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 04:02:54,059][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 04:02:54,560][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 04:03:06,206][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 04:03:06,714][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 04:03:07,227][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 04:03:07,731][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 04:03:08,237][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 04:03:08,746][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 04:03:09,251][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 04:03:09,755][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 04:03:10,257][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 04:03:10,758][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 04:03:11,263][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 04:03:11,766][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 04:03:12,269][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 04:03:12,772][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 04:03:13,274][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 04:03:13,776][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 04:03:14,279][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 04:03:14,782][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 04:03:15,290][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 04:03:15,793][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 04:03:16,295][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 04:03:16,810][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 04:03:17,315][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 04:03:17,836][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 04:03:18,343][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 04:03:18,847][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 04:03:19,353][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 04:03:19,855][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 04:03:20,356][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 04:03:20,859][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 04:03:21,359][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 04:03:21,864][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10879 tokens. [2025-11-13 04:03:22,646][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.23%, Current % of VRAM taken: 59.68%, Block Peak % of device VRAM: 62.32%, ΔTime: 00:00:33 [2025-11-13 04:03:23,398][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 04:03:23,399][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 04:03:23,401][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 04:03:24,505][__main__][INFO] - Iteration 408 took 51s (29.11% Gen, 68.75% Train). Generation: 15s, Training: 35s. Estimated remaining time: 37h 3m 27s. Estimated total time: 42h 57m 9s. Time estimates for 10 more iterations: 8m 35s, 100 more iterations: 1h 25m 54s, 500 more iterations: 7h 9m 31s. [2025-11-13 04:03:24,507][__main__][INFO] - Starting iteration 408. [2025-11-13 04:03:24,999][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 40 and human policies 1. [2025-11-13 04:03:24,999][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 04:03:30,730][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:03:39,778][__main__][INFO] - Number of regex retries in iteration 408: 1 [2025-11-13 04:03:39,778][__main__][INFO] - agents played in iteration 408 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 04:03:40,629][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:03:40,656][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:03:40,682][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:03:40,704][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:03:40,705][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 04:03:40,706][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 04:03:41,408][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 04:03:41,866][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 04:03:42,375][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 04:03:42,882][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 04:03:43,388][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 04:03:43,895][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 04:03:44,399][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 04:03:44,901][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 04:03:45,402][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 04:03:45,903][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 04:03:46,407][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 04:03:58,060][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 04:03:58,564][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 04:03:59,066][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 04:03:59,567][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 04:04:00,075][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 04:04:00,578][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 04:04:01,080][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 04:04:01,584][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 04:04:02,088][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 04:04:02,591][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 04:04:03,093][mllm.training.trainer_common][INFO] - 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Processing mini-batch 54 of 64 [2025-11-13 04:04:09,130][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 04:04:09,634][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 04:04:10,143][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 04:04:10,649][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 04:04:11,153][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 04:04:11,655][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 04:04:12,156][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 04:04:12,661][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 04:04:13,166][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 04:04:13,671][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10859 tokens. [2025-11-13 04:04:14,394][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.27%, Current % of VRAM taken: 59.72%, Block Peak % of device VRAM: 62.32%, ΔTime: 00:00:32 [2025-11-13 04:04:15,134][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 04:04:15,136][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 04:04:15,138][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 04:04:16,341][__main__][INFO] - Iteration 409 took 51s (28.79% Gen, 68.87% Train). Generation: 14s, Training: 35s. Estimated remaining time: 36h 52m 33s. Estimated total time: 42h 47m 8s. Time estimates for 10 more iterations: 8m 33s, 100 more iterations: 1h 25m 34s, 500 more iterations: 7h 7m 51s. [2025-11-13 04:04:16,343][__main__][INFO] - Starting iteration 409. [2025-11-13 04:04:16,822][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 40 and human policies 1. [2025-11-13 04:04:16,822][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 04:04:21,035][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:04:27,367][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:04:30,331][__main__][INFO] - Number of regex retries in iteration 409: 2 [2025-11-13 04:04:30,331][__main__][INFO] - agents played in iteration 409 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 04:04:31,104][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:04:31,131][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:04:31,156][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:04:31,178][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:04:31,179][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 04:04:31,179][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 04:04:31,878][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 04:04:32,334][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 04:04:32,841][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 04:04:33,343][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 04:04:33,844][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 04:04:34,347][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 04:04:34,849][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 04:04:35,353][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 04:04:35,859][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 04:04:36,364][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 04:04:36,872][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 04:04:48,451][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 04:04:48,953][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 04:04:49,458][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 04:04:50,030][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 04:04:50,544][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 04:04:51,052][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 04:04:51,558][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 04:04:52,063][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 04:04:52,572][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 04:04:53,075][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 04:04:53,584][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 04:04:54,090][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 04:04:54,599][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 04:04:55,104][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 04:04:55,612][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 04:04:56,125][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 04:04:56,633][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 04:04:57,135][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 04:04:57,652][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 04:04:58,156][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 04:04:58,661][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 04:04:59,162][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 04:04:59,670][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 04:05:00,177][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 04:05:00,680][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 04:05:01,184][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 04:05:01,689][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 04:05:02,195][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 04:05:02,697][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 04:05:03,198][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 04:05:03,701][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 04:05:04,206][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10878 tokens. [2025-11-13 04:05:04,995][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.18%, Current % of VRAM taken: 59.63%, Block Peak % of device VRAM: 62.28%, ΔTime: 00:00:33 [2025-11-13 04:05:05,768][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 04:05:05,770][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 04:05:05,772][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 04:05:06,749][__main__][INFO] - Iteration 410 took 49s (27.06% Gen, 70.98% Train). Generation: 13s, Training: 35s. Estimated remaining time: 35h 40m 59s. Estimated total time: 41h 36m 24s. Time estimates for 10 more iterations: 8m 19s, 100 more iterations: 1h 23m 12s, 500 more iterations: 6h 56m 4s. [2025-11-13 04:05:06,751][__main__][INFO] - Starting iteration 410. [2025-11-13 04:05:07,234][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 40 and human policies 1. [2025-11-13 04:05:07,235][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 04:05:14,705][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:05:14,785][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:05:19,122][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:05:25,818][__main__][INFO] - Number of regex retries in iteration 410: 3 [2025-11-13 04:05:25,819][__main__][INFO] - agents played in iteration 410 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 04:05:26,655][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:05:26,682][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:05:26,709][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:05:26,732][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:05:26,732][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 04:05:26,733][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 04:05:27,439][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 04:05:27,896][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 04:05:28,407][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 04:05:28,915][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 04:05:29,430][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 04:05:29,934][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 04:05:30,440][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 04:05:30,944][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 04:05:31,450][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 04:05:31,961][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 04:05:32,463][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 04:05:32,966][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 04:05:33,470][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 04:05:33,972][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 04:05:34,473][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 04:05:34,974][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 04:05:35,476][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 04:05:35,982][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 04:05:36,483][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 04:05:36,987][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 04:05:37,495][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 04:05:37,997][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 04:05:44,070][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 04:05:44,580][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 04:05:45,085][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 04:05:45,591][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 04:05:46,094][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 04:05:46,599][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 04:05:47,105][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 04:05:47,610][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 04:05:48,113][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 04:05:48,616][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 04:05:49,122][mllm.training.trainer_common][INFO] - 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Processing mini-batch 54 of 64 [2025-11-13 04:05:55,183][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 04:05:55,685][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 04:05:56,187][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 04:05:56,691][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 04:05:57,195][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 04:05:57,699][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 04:05:58,203][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 04:05:58,705][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 04:05:59,209][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 04:05:59,720][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10874 tokens. [2025-11-13 04:06:00,473][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.20%, Current % of VRAM taken: 59.65%, Block Peak % of device VRAM: 62.41%, ΔTime: 00:00:33 [2025-11-13 04:06:01,252][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 04:06:01,253][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 04:06:01,255][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 04:06:03,319][__main__][INFO] - Iteration 411 took 56s (33.14% Gen, 63.18% Train). Generation: 18s, Training: 35s. Estimated remaining time: 40h 47m 52s. Estimated total time: 46h 44m 14s. Time estimates for 10 more iterations: 9m 20s, 100 more iterations: 1h 33m 28s, 500 more iterations: 7h 47m 22s. [2025-11-13 04:06:03,321][__main__][INFO] - Starting iteration 411. [2025-11-13 04:06:03,807][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 41 and human policies 1. [2025-11-13 04:06:03,807][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 04:06:13,559][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:06:21,860][__main__][INFO] - Number of regex retries in iteration 411: 1 [2025-11-13 04:06:21,861][__main__][INFO] - agents played in iteration 411 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 04:06:22,901][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:06:22,924][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:06:22,947][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:06:22,969][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:06:22,970][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 04:06:22,970][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 04:06:23,741][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 04:06:24,201][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 04:06:24,708][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 04:06:25,209][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 04:06:25,710][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 04:06:26,212][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 04:06:26,713][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 04:06:27,216][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 04:06:27,717][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 04:06:28,216][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 04:06:28,718][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 04:06:40,365][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 04:06:40,873][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 04:06:41,379][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 04:06:41,890][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 04:06:42,398][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 04:06:42,905][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 04:06:43,413][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 04:06:43,918][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 04:06:44,426][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 04:06:44,939][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 04:06:45,449][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 04:06:45,963][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 04:06:46,475][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 04:06:46,991][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 04:06:47,503][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 04:06:48,011][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 04:06:48,526][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 04:06:49,037][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 04:06:49,544][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 04:06:50,054][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 04:06:50,558][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 04:06:51,077][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 04:06:51,587][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 04:06:52,093][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 04:06:52,600][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 04:06:53,116][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 04:06:53,628][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 04:06:54,131][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 04:06:54,634][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 04:06:55,145][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 04:06:55,649][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 04:06:56,161][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10879 tokens. [2025-11-13 04:06:56,975][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.26%, Current % of VRAM taken: 59.72%, Block Peak % of device VRAM: 62.32%, ΔTime: 00:00:33 [2025-11-13 04:06:57,754][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 04:06:57,755][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 04:06:57,757][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 04:06:58,705][__main__][INFO] - Iteration 412 took 54s (32.88% Gen, 65.39% Train). Generation: 18s, Training: 35s. Estimated remaining time: 39h 47m 40s. Estimated total time: 45h 44m 57s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 29s, 500 more iterations: 7h 37m 29s. [2025-11-13 04:06:58,707][__main__][INFO] - Starting iteration 412. [2025-11-13 04:06:59,194][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 41 and human policies 1. [2025-11-13 04:06:59,195][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 04:07:09,719][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:07:09,769][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:07:20,644][__main__][INFO] - Number of regex retries in iteration 412: 2 [2025-11-13 04:07:20,644][__main__][INFO] - agents played in iteration 412 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 04:07:21,487][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:07:21,515][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:07:21,541][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:07:21,563][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:07:21,564][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 04:07:21,564][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 04:07:22,249][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 04:07:22,708][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 04:07:23,218][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 04:07:23,721][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 04:07:24,239][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 04:07:24,744][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 04:07:25,259][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 04:07:25,761][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 04:07:26,264][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 04:07:26,775][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 04:07:27,277][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 04:07:38,877][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 04:07:39,387][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 04:07:39,899][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 04:07:40,410][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 04:07:40,928][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 04:07:41,441][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 04:07:41,947][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 04:07:42,457][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 04:07:42,969][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 04:07:43,478][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 04:07:43,988][mllm.training.trainer_common][INFO] - 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Processing mini-batch 54 of 64 [2025-11-13 04:07:50,151][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 04:07:50,661][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 04:07:51,168][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 04:07:51,690][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 04:07:52,202][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 04:07:52,717][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 04:07:53,230][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 04:07:53,735][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 04:07:54,240][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 04:07:54,745][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10874 tokens. [2025-11-13 04:07:55,518][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.25%, Current % of VRAM taken: 59.70%, Block Peak % of device VRAM: 62.35%, ΔTime: 00:00:33 [2025-11-13 04:07:56,277][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 04:07:56,279][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 04:07:56,281][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 04:07:57,319][__main__][INFO] - Iteration 413 took 58s (36.90% Gen, 61.31% Train). Generation: 21s, Training: 35s. Estimated remaining time: 42h 28m 0s. Estimated total time: 48h 26m 16s. Time estimates for 10 more iterations: 9m 41s, 100 more iterations: 1h 36m 52s, 500 more iterations: 8h 4m 22s. [2025-11-13 04:07:57,321][__main__][INFO] - Starting iteration 413. [2025-11-13 04:07:57,814][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 41 and human policies 1. [2025-11-13 04:07:57,815][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 04:08:05,649][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:08:15,741][__main__][INFO] - Number of regex retries in iteration 413: 1 [2025-11-13 04:08:15,742][__main__][INFO] - agents played in iteration 413 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 04:08:16,532][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:08:16,560][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:08:16,588][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:08:16,612][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:08:16,612][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 04:08:16,613][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 04:08:17,293][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 04:08:17,751][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 04:08:18,258][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 04:08:18,758][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 04:08:19,263][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 04:08:19,763][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 04:08:20,264][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 04:08:20,780][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 04:08:21,280][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 04:08:21,784][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 04:08:22,284][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 04:08:33,807][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 04:08:34,312][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 04:08:34,816][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 04:08:35,319][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 04:08:35,822][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 04:08:36,326][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 04:08:36,827][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 04:08:37,334][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 04:08:37,847][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 04:08:38,353][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 04:08:38,860][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 04:08:39,365][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 04:08:39,870][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 04:08:40,376][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 04:08:40,879][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 04:08:41,381][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 04:08:41,890][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 04:08:42,402][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 04:08:42,908][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 04:08:43,414][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 04:08:43,922][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 04:08:44,433][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 04:08:44,938][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 04:08:45,450][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 04:08:45,956][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 04:08:46,458][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 04:08:46,964][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 04:08:47,471][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 04:08:47,983][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 04:08:48,489][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 04:08:48,993][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 04:08:49,504][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10874 tokens. [2025-11-13 04:08:50,291][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.23%, Current % of VRAM taken: 59.68%, Block Peak % of device VRAM: 62.24%, ΔTime: 00:00:33 [2025-11-13 04:08:51,061][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 04:08:51,076][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 04:08:51,077][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 04:08:52,104][__main__][INFO] - Iteration 414 took 54s (33.02% Gen, 65.09% Train). Generation: 17s, Training: 35s. Estimated remaining time: 39h 15m 22s. Estimated total time: 45h 14m 33s. Time estimates for 10 more iterations: 9m 2s, 100 more iterations: 1h 30m 29s, 500 more iterations: 7h 32m 25s. [2025-11-13 04:08:52,107][__main__][INFO] - Starting iteration 414. [2025-11-13 04:08:52,581][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 41 and human policies 1. [2025-11-13 04:08:52,581][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 04:09:02,545][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:09:08,587][__main__][INFO] - Number of regex retries in iteration 414: 1 [2025-11-13 04:09:08,588][__main__][INFO] - agents played in iteration 414 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 04:09:09,383][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:09:09,406][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:09:09,428][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:09:09,450][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:09:09,451][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 04:09:09,452][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 04:09:10,144][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 04:09:10,605][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 04:09:11,112][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 04:09:11,618][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 04:09:12,118][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 04:09:12,621][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 04:09:13,120][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 04:09:13,620][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 04:09:14,118][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 04:09:14,618][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 04:09:15,118][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 04:09:26,618][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 04:09:27,118][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 04:09:27,619][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 04:09:28,119][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 04:09:28,622][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 04:09:29,125][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 04:09:29,631][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 04:09:30,137][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 04:09:30,645][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 04:09:31,150][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 04:09:31,661][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 04:09:32,166][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 04:09:32,684][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 04:09:33,188][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 04:09:33,696][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 04:09:34,212][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 04:09:34,719][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 04:09:35,226][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 04:09:35,731][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 04:09:36,235][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 04:09:36,740][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 04:09:37,245][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 04:09:37,755][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 04:09:38,263][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 04:09:38,769][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 04:09:39,278][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 04:09:39,787][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 04:09:40,290][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 04:09:40,796][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 04:09:41,304][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 04:09:41,814][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 04:09:42,318][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10878 tokens. [2025-11-13 04:09:43,133][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.22%, Current % of VRAM taken: 59.67%, Block Peak % of device VRAM: 62.29%, ΔTime: 00:00:32 [2025-11-13 04:09:43,865][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 04:09:43,866][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 04:09:43,868][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 04:09:44,781][__main__][INFO] - Iteration 415 took 52s (30.66% Gen, 67.59% Train). Generation: 16s, Training: 35s. Estimated remaining time: 37h 30m 0s. Estimated total time: 43h 30m 3s. Time estimates for 10 more iterations: 8m 42s, 100 more iterations: 1h 27m 0s, 500 more iterations: 7h 15m 0s. [2025-11-13 04:09:44,783][__main__][INFO] - Starting iteration 415. [2025-11-13 04:09:45,256][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 41 and human policies 1. [2025-11-13 04:09:45,257][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 04:09:50,139][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:09:52,672][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:09:58,547][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:10:00,021][__main__][INFO] - Number of regex retries in iteration 415: 3 [2025-11-13 04:10:00,022][__main__][INFO] - agents played in iteration 415 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 04:10:00,878][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:10:00,905][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:10:00,931][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:10:00,954][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:10:00,955][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 04:10:00,955][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 04:10:01,654][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 04:10:02,110][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 04:10:02,619][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 04:10:03,123][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 04:10:03,624][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 04:10:04,128][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 04:10:04,633][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 04:10:05,136][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 04:10:05,644][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 04:10:06,148][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 04:10:06,649][mllm.training.trainer_common][INFO] - 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Processing mini-batch 21 of 64 [2025-11-13 04:10:12,670][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 04:10:13,171][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 04:10:13,684][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 04:10:14,186][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 04:10:14,687][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 04:10:15,187][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 04:10:15,689][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 04:10:16,205][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 04:10:16,707][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 04:10:17,209][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 04:10:17,711][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 04:10:18,212][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 04:10:18,715][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 04:10:19,216][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 04:10:19,715][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 04:10:20,221][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 04:10:20,721][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 04:10:21,222][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 04:10:21,719][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 04:10:22,219][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 04:10:22,722][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 04:10:23,223][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 04:10:23,722][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 04:10:24,226][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 04:10:24,727][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 04:10:25,232][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 04:10:25,737][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 04:10:26,241][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 04:10:26,748][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 04:10:27,254][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 04:10:27,760][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 04:10:28,263][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 04:10:28,766][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 04:10:29,276][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 04:10:29,778][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 04:10:30,282][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 04:10:30,811][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 04:10:31,318][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 04:10:31,825][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 04:10:32,330][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 04:10:32,837][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 04:10:33,343][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 04:10:33,850][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10880 tokens. [2025-11-13 04:10:34,645][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.23%, Current % of VRAM taken: 59.68%, Block Peak % of device VRAM: 62.24%, ΔTime: 00:00:33 [2025-11-13 04:10:35,372][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 04:10:35,374][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 04:10:35,376][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 04:10:36,445][__main__][INFO] - Iteration 416 took 51s (28.84% Gen, 69.06% Train). Generation: 14s, Training: 35s. Estimated remaining time: 36h 38m 35s. Estimated total time: 42h 39m 30s. Time estimates for 10 more iterations: 8m 31s, 100 more iterations: 1h 25m 19s, 500 more iterations: 7h 6m 35s. [2025-11-13 04:10:36,447][__main__][INFO] - Starting iteration 416. [2025-11-13 04:10:36,929][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 41 and human policies 1. [2025-11-13 04:10:36,929][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 04:10:42,092][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:10:43,871][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:10:44,659][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:10:48,557][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:10:51,982][__main__][INFO] - Number of regex retries in iteration 416: 4 [2025-11-13 04:10:51,983][__main__][INFO] - agents played in iteration 416 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 04:10:52,758][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:10:52,790][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:10:52,817][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:10:52,842][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:10:52,843][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 04:10:52,843][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 04:10:53,530][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 04:10:53,989][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 04:10:54,516][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 04:10:55,019][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 04:10:55,523][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 04:10:56,027][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 04:10:56,532][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 04:10:57,037][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 04:10:57,547][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 04:10:58,054][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 04:10:58,561][mllm.training.trainer_common][INFO] - 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Processing mini-batch 21 of 64 [2025-11-13 04:11:04,624][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 04:11:05,126][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 04:11:05,627][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 04:11:06,130][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 04:11:06,633][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 04:11:07,134][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 04:11:07,638][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 04:11:08,139][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 04:11:08,640][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 04:11:09,144][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 04:11:09,645][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 04:11:10,145][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 04:11:10,645][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 04:11:11,145][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 04:11:11,646][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 04:11:12,146][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 04:11:12,645][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 04:11:13,144][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 04:11:13,646][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 04:11:14,148][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 04:11:14,649][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 04:11:15,149][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 04:11:15,653][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 04:11:16,152][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 04:11:16,652][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 04:11:17,152][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 04:11:17,651][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 04:11:18,154][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 04:11:18,654][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 04:11:19,155][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 04:11:19,661][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 04:11:20,164][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 04:11:20,666][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 04:11:21,172][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 04:11:21,674][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 04:11:22,179][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 04:11:22,681][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 04:11:23,186][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 04:11:23,697][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 04:11:24,200][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 04:11:24,721][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 04:11:25,225][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 04:11:25,729][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10880 tokens. [2025-11-13 04:11:26,513][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.25%, Current % of VRAM taken: 59.70%, Block Peak % of device VRAM: 62.42%, ΔTime: 00:00:32 [2025-11-13 04:11:27,294][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 04:11:27,296][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 04:11:27,300][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 04:11:28,350][__main__][INFO] - Iteration 417 took 51s (29.27% Gen, 68.68% Train). Generation: 15s, Training: 35s. Estimated remaining time: 36h 49m 19s. Estimated total time: 42h 51m 6s. Time estimates for 10 more iterations: 8m 34s, 100 more iterations: 1h 25m 42s, 500 more iterations: 7h 8m 31s. [2025-11-13 04:11:28,352][__main__][INFO] - Starting iteration 417. [2025-11-13 04:11:28,823][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 41 and human policies 1. [2025-11-13 04:11:28,824][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 04:11:39,630][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:11:45,381][__main__][INFO] - Number of regex retries in iteration 417: 1 [2025-11-13 04:11:45,382][__main__][INFO] - agents played in iteration 417 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 04:11:46,214][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:11:46,241][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:11:46,267][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:11:46,290][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:11:46,290][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 04:11:46,291][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 04:11:46,996][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 04:11:47,453][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 04:11:47,960][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 04:11:48,463][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 04:11:48,968][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 04:11:49,473][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 04:11:49,977][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 04:11:50,481][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 04:11:50,983][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 04:11:51,487][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 04:11:51,993][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 04:12:03,575][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 04:12:04,076][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 04:12:04,578][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 04:12:05,081][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 04:12:05,580][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 04:12:06,081][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 04:12:06,583][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 04:12:07,085][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 04:12:07,587][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 04:12:08,088][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 04:12:08,589][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 04:12:09,097][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 04:12:09,598][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 04:12:10,099][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 04:12:10,600][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 04:12:11,100][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 04:12:11,605][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 04:12:12,111][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 04:12:12,618][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 04:12:13,126][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 04:12:13,633][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 04:12:14,146][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 04:12:14,650][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 04:12:15,157][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 04:12:15,662][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 04:12:16,171][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 04:12:16,683][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 04:12:17,192][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 04:12:17,704][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 04:12:18,224][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 04:12:18,734][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 04:12:19,246][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10880 tokens. [2025-11-13 04:12:20,073][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.19%, Current % of VRAM taken: 59.65%, Block Peak % of device VRAM: 62.34%, ΔTime: 00:00:33 [2025-11-13 04:12:20,850][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 04:12:20,851][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 04:12:20,853][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 04:12:21,924][__main__][INFO] - Iteration 418 took 53s (31.18% Gen, 66.80% Train). Generation: 16s, Training: 35s. Estimated remaining time: 38h 12m 24s. Estimated total time: 44h 15m 4s. Time estimates for 10 more iterations: 8m 51s, 100 more iterations: 1h 28m 30s, 500 more iterations: 7h 22m 30s. [2025-11-13 04:12:21,927][__main__][INFO] - Starting iteration 418. [2025-11-13 04:12:22,423][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 41 and human policies 1. [2025-11-13 04:12:22,424][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 04:12:43,273][__main__][INFO] - Number of regex retries in iteration 418: 0 [2025-11-13 04:12:43,274][__main__][INFO] - agents played in iteration 418 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 04:12:44,072][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:12:44,100][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:12:44,127][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:12:44,150][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:12:44,151][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 04:12:44,153][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 04:12:44,921][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 04:12:45,381][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 04:12:45,890][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 04:12:46,392][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 04:12:46,895][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 04:12:47,408][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 04:12:47,910][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 04:12:48,416][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 04:12:48,923][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 04:12:49,422][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 04:12:49,945][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 04:13:01,564][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 04:13:02,066][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 04:13:02,568][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 04:13:03,073][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 04:13:03,575][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 04:13:04,078][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 04:13:04,580][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 04:13:05,081][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 04:13:05,583][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 04:13:06,084][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 04:13:06,584][mllm.training.trainer_common][INFO] - 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Processing mini-batch 54 of 64 [2025-11-13 04:13:12,643][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 04:13:13,147][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 04:13:13,651][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 04:13:14,155][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 04:13:14,661][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 04:13:15,163][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 04:13:15,663][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 04:13:16,167][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 04:13:16,668][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 04:13:17,179][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10879 tokens. [2025-11-13 04:13:17,982][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.18%, Current % of VRAM taken: 59.63%, Block Peak % of device VRAM: 62.28%, ΔTime: 00:00:33 [2025-11-13 04:13:18,748][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 04:13:18,750][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 04:13:18,751][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 04:13:19,798][__main__][INFO] - Iteration 419 took 57s (36.34% Gen, 61.83% Train). Generation: 20s, Training: 35s. Estimated remaining time: 41h 45m 9s. Estimated total time: 47h 48m 47s. Time estimates for 10 more iterations: 9m 33s, 100 more iterations: 1h 35m 37s, 500 more iterations: 7h 58m 7s. [2025-11-13 04:13:19,801][__main__][INFO] - Starting iteration 419. [2025-11-13 04:13:20,308][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 41 and human policies 1. [2025-11-13 04:13:20,309][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 04:13:26,822][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:13:30,776][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:13:35,780][__main__][INFO] - Number of regex retries in iteration 419: 2 [2025-11-13 04:13:35,781][__main__][INFO] - agents played in iteration 419 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 04:13:36,595][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:13:36,622][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:13:36,648][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:13:36,671][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:13:36,671][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 04:13:36,673][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 04:13:37,434][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 04:13:37,894][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 04:13:38,402][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 04:13:38,909][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 04:13:39,413][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 04:13:39,921][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 04:13:40,425][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 04:13:40,929][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 04:13:41,433][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 04:13:41,937][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 04:13:42,457][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 04:13:54,017][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 04:13:54,517][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 04:13:55,017][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 04:13:55,518][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 04:13:56,017][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 04:13:56,516][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 04:13:57,016][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 04:13:57,516][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 04:13:58,018][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 04:13:58,523][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 04:13:59,024][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 04:13:59,538][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 04:14:00,039][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 04:14:00,545][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 04:14:01,046][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 04:14:01,548][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 04:14:02,052][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 04:14:02,553][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 04:14:03,053][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 04:14:03,558][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 04:14:04,061][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 04:14:04,561][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 04:14:05,061][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 04:14:05,562][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 04:14:06,062][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 04:14:06,562][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 04:14:07,065][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 04:14:07,564][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 04:14:08,065][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 04:14:08,574][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 04:14:09,074][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 04:14:09,579][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10880 tokens. [2025-11-13 04:14:10,365][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.20%, Current % of VRAM taken: 59.65%, Block Peak % of device VRAM: 62.31%, ΔTime: 00:00:32 [2025-11-13 04:14:11,161][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 04:14:11,163][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 04:14:11,165][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 04:14:12,192][__main__][INFO] - Iteration 420 took 51s (29.82% Gen, 68.20% Train). Generation: 15s, Training: 35s. Estimated remaining time: 37h 9m 44s. Estimated total time: 43h 14m 14s. Time estimates for 10 more iterations: 8m 38s, 100 more iterations: 1h 26m 28s, 500 more iterations: 7h 12m 22s. [2025-11-13 04:14:12,195][__main__][INFO] - Starting iteration 420. [2025-11-13 04:14:12,695][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 41 and human policies 1. [2025-11-13 04:14:12,696][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 04:14:30,703][__main__][INFO] - Number of regex retries in iteration 420: 0 [2025-11-13 04:14:30,703][__main__][INFO] - agents played in iteration 420 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 04:14:31,664][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:14:31,724][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:14:31,764][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:14:31,788][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:14:31,789][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 04:14:31,789][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 04:14:32,591][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 04:14:33,141][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 04:14:33,663][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 04:14:34,171][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 04:14:34,680][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 04:14:35,186][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 04:14:35,696][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 04:14:36,202][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 04:14:36,711][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 04:14:37,223][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 04:14:37,732][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 04:14:49,446][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 04:14:49,950][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 04:14:50,457][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 04:14:50,960][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 04:14:51,465][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 04:14:51,970][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 04:14:52,473][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 04:14:52,977][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 04:14:53,500][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 04:14:54,007][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 04:14:54,516][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 04:14:55,020][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 04:14:55,522][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 04:14:56,025][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 04:14:56,532][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 04:14:57,038][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 04:14:57,544][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 04:14:58,048][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 04:14:58,554][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 04:14:59,055][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 04:14:59,555][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 04:15:00,056][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 04:15:00,556][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 04:15:01,069][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 04:15:01,573][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 04:15:02,075][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 04:15:02,579][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 04:15:03,082][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 04:15:03,584][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 04:15:04,086][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 04:15:04,589][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 04:15:05,103][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10875 tokens. [2025-11-13 04:15:05,885][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.32%, Current % of VRAM taken: 59.77%, Block Peak % of device VRAM: 62.49%, ΔTime: 00:00:33 [2025-11-13 04:15:06,682][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 04:15:06,684][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 04:15:06,685][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 04:15:08,857][__main__][INFO] - Iteration 421 took 56s (32.06% Gen, 64.07% Train). Generation: 18s, Training: 35s. Estimated remaining time: 40h 42m 40s. Estimated total time: 46h 48m 7s. Time estimates for 10 more iterations: 9m 21s, 100 more iterations: 1h 33m 36s, 500 more iterations: 7h 48m 1s. [2025-11-13 04:15:08,860][__main__][INFO] - Starting iteration 421. [2025-11-13 04:15:09,373][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 42 and human policies 1. [2025-11-13 04:15:09,374][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 04:15:23,482][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:15:23,531][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:15:28,821][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:15:31,733][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:15:32,569][__main__][INFO] - Number of regex retries in iteration 421: 4 [2025-11-13 04:15:32,569][__main__][INFO] - agents played in iteration 421 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 04:15:33,408][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.62%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:15:33,436][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.62%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:15:33,462][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.62%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:15:33,485][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.62%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:15:33,486][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 04:15:33,487][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 04:15:34,271][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 04:15:34,737][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 04:15:35,251][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 04:15:35,756][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 04:15:36,265][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 04:15:36,781][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 04:15:37,296][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 04:15:37,808][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 04:15:38,319][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 04:15:38,829][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 04:15:39,336][mllm.training.trainer_common][INFO] - 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Processing mini-batch 21 of 64 [2025-11-13 04:15:45,441][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 04:15:45,948][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 04:15:46,452][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 04:15:46,955][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 04:15:47,457][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 04:15:47,958][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 04:15:48,460][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 04:15:48,961][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 04:15:49,460][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 04:15:49,971][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 04:15:50,471][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 04:15:50,989][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 04:15:51,493][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 04:15:51,995][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 04:15:52,498][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 04:15:53,000][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 04:15:53,502][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 04:15:54,004][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 04:15:54,507][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 04:15:55,012][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 04:15:55,512][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 04:15:56,015][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 04:15:56,517][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 04:15:57,020][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 04:15:57,526][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 04:15:58,027][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 04:15:58,527][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 04:15:59,032][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 04:15:59,537][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 04:16:00,041][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 04:16:00,544][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 04:16:01,044][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 04:16:01,543][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 04:16:02,042][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 04:16:02,542][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 04:16:03,044][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 04:16:03,545][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 04:16:04,044][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 04:16:04,545][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 04:16:05,045][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 04:16:05,546][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 04:16:06,053][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 04:16:06,556][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10880 tokens. [2025-11-13 04:16:07,352][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.19%, Current % of VRAM taken: 59.65%, Block Peak % of device VRAM: 62.31%, ΔTime: 00:00:33 [2025-11-13 04:16:08,155][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 04:16:08,157][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 04:16:08,159][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 04:16:09,152][__main__][INFO] - Iteration 422 took 59s (38.80% Gen, 59.54% Train). Generation: 23s, Training: 35s. Estimated remaining time: 43h 42m 29s. Estimated total time: 49h 48m 57s. Time estimates for 10 more iterations: 9m 57s, 100 more iterations: 1h 39m 37s, 500 more iterations: 8h 18m 9s. [2025-11-13 04:16:09,154][__main__][INFO] - Starting iteration 422. [2025-11-13 04:16:09,625][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 42 and human policies 1. [2025-11-13 04:16:09,626][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 04:16:28,768][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:16:29,599][__main__][INFO] - Number of regex retries in iteration 422: 1 [2025-11-13 04:16:29,600][__main__][INFO] - agents played in iteration 422 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 04:16:30,468][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:16:30,520][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:16:30,558][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:16:30,587][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:16:30,588][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 04:16:30,589][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 04:16:31,307][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 04:16:31,775][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 04:16:32,282][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 04:16:32,785][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 04:16:33,296][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 04:16:33,801][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 04:16:34,317][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 04:16:34,823][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 04:16:35,330][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 04:16:35,838][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 04:16:36,343][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 04:16:48,094][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 04:16:48,601][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 04:16:49,111][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 04:16:49,621][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 04:16:50,130][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 04:16:50,639][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 04:16:51,147][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 04:16:51,657][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 04:16:52,166][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 04:16:52,677][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 04:16:53,199][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 04:16:53,705][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 04:16:54,232][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 04:16:54,743][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 04:16:55,252][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 04:16:55,763][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 04:16:56,265][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 04:16:56,767][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 04:16:57,266][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 04:16:57,768][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 04:16:58,282][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 04:16:58,783][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 04:16:59,283][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 04:16:59,794][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 04:17:00,295][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 04:17:00,813][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 04:17:01,315][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 04:17:01,816][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 04:17:02,318][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 04:17:02,819][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 04:17:03,326][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 04:17:03,826][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10877 tokens. [2025-11-13 04:17:04,560][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.24%, Current % of VRAM taken: 59.69%, Block Peak % of device VRAM: 62.49%, ΔTime: 00:00:33 [2025-11-13 04:17:05,196][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 04:17:05,198][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 04:17:05,200][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 04:17:06,185][__main__][INFO] - Iteration 423 took 56s (35.31% Gen, 62.94% Train). Generation: 19s, Training: 35s. Estimated remaining time: 41h 0m 35s. Estimated total time: 47h 8m 0s. Time estimates for 10 more iterations: 9m 25s, 100 more iterations: 1h 34m 16s, 500 more iterations: 7h 51m 20s. [2025-11-13 04:17:06,187][__main__][INFO] - Starting iteration 423. [2025-11-13 04:17:06,670][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 42 and human policies 1. [2025-11-13 04:17:06,670][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 04:17:23,620][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:17:25,623][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:17:34,445][__main__][INFO] - Number of regex retries in iteration 423: 2 [2025-11-13 04:17:34,445][__main__][INFO] - agents played in iteration 423 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 04:17:35,261][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:17:35,289][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:17:35,315][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:17:35,338][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:17:35,339][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 04:17:35,340][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 04:17:36,063][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 04:17:36,527][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 04:17:37,035][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 04:17:37,540][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 04:17:38,045][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 04:17:38,548][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 04:17:39,054][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 04:17:39,560][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 04:17:40,062][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 04:17:40,569][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 04:17:41,074][mllm.training.trainer_common][INFO] - 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Processing mini-batch 21 of 64 [2025-11-13 04:17:47,160][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 04:17:47,663][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 04:17:48,172][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 04:17:48,681][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 04:17:49,191][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 04:17:49,705][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 04:17:50,212][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 04:17:50,726][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 04:17:51,234][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 04:17:51,743][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 04:17:52,252][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 04:17:52,758][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 04:17:53,270][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 04:17:53,779][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 04:17:54,291][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 04:17:54,811][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 04:17:55,321][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 04:17:55,833][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 04:17:56,340][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 04:17:56,849][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 04:17:57,354][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 04:17:57,863][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 04:17:58,371][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 04:17:58,882][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 04:17:59,390][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 04:17:59,897][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 04:18:00,412][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 04:18:00,916][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 04:18:01,430][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 04:18:01,932][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 04:18:02,436][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 04:18:02,937][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 04:18:03,441][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 04:18:03,945][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 04:18:04,445][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 04:18:04,946][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 04:18:05,449][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 04:18:05,950][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 04:18:06,450][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 04:18:06,951][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 04:18:07,452][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 04:18:07,954][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 04:18:08,454][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10872 tokens. [2025-11-13 04:18:09,182][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.13%, Current % of VRAM taken: 59.58%, Block Peak % of device VRAM: 62.31%, ΔTime: 00:00:33 [2025-11-13 04:18:09,923][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 04:18:09,924][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 04:18:09,926][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 04:18:10,966][__main__][INFO] - Iteration 424 took 1m 4s (43.20% Gen, 55.18% Train). Generation: 27s, Training: 35s. Estimated remaining time: 47h 26m 22s. Estimated total time: 53h 34m 51s. Time estimates for 10 more iterations: 10m 42s, 100 more iterations: 1h 47m 9s, 500 more iterations: 8h 55m 48s. [2025-11-13 04:18:10,969][__main__][INFO] - Starting iteration 424. [2025-11-13 04:18:11,466][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 42 and human policies 1. [2025-11-13 04:18:11,466][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 04:18:24,403][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:18:24,571][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:18:34,603][__main__][INFO] - Number of regex retries in iteration 424: 2 [2025-11-13 04:18:34,604][__main__][INFO] - agents played in iteration 424 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 04:18:35,381][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:18:35,408][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:18:35,434][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:18:35,457][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:18:35,457][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 04:18:35,459][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 04:18:36,182][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 04:18:36,639][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 04:18:37,151][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 04:18:37,655][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 04:18:38,160][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 04:18:38,663][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 04:18:39,165][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 04:18:39,674][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 04:18:40,177][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 04:18:40,684][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 04:18:41,192][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 04:18:41,698][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 04:18:42,208][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 04:18:42,710][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 04:18:43,213][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 04:18:43,719][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 04:18:44,222][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 04:18:44,726][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 04:18:45,229][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 04:18:45,733][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 04:18:46,249][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 04:18:46,755][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 04:18:54,288][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 04:18:54,796][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 04:18:55,309][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 04:18:55,820][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 04:18:56,329][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 04:18:56,841][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 04:18:57,352][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 04:18:57,861][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 04:18:58,368][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 04:18:58,872][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 04:18:59,380][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 04:18:59,884][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 04:19:00,386][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 04:19:00,895][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 04:19:01,400][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 04:19:01,915][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 04:19:02,423][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 04:19:02,934][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 04:19:03,444][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 04:19:03,955][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 04:19:04,471][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 04:19:04,981][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 04:19:05,490][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 04:19:06,009][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 04:19:06,517][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 04:19:07,025][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 04:19:07,536][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 04:19:08,047][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 04:19:08,560][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 04:19:09,068][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 04:19:09,576][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 04:19:10,084][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10876 tokens. [2025-11-13 04:19:10,906][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.25%, Current % of VRAM taken: 59.71%, Block Peak % of device VRAM: 62.28%, ΔTime: 00:00:34 [2025-11-13 04:19:11,565][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 04:19:11,568][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 04:19:11,570][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 04:19:12,598][__main__][INFO] - Iteration 425 took 1m 1s (37.85% Gen, 60.47% Train). Generation: 23s, Training: 36s. Estimated remaining time: 44h 47m 8s. Estimated total time: 50h 56m 39s. Time estimates for 10 more iterations: 10m 11s, 100 more iterations: 1h 41m 53s, 500 more iterations: 8h 29m 26s. [2025-11-13 04:19:12,600][__main__][INFO] - Starting iteration 425. [2025-11-13 04:19:13,100][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 42 and human policies 1. [2025-11-13 04:19:13,100][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 04:19:30,920][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:19:30,924][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:19:34,050][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:19:41,444][__main__][INFO] - Number of regex retries in iteration 425: 3 [2025-11-13 04:19:41,445][__main__][INFO] - agents played in iteration 425 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 04:19:42,236][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:19:42,263][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:19:42,289][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:19:42,313][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:19:42,313][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 04:19:42,314][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 04:19:43,031][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 04:19:43,488][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 04:19:43,997][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 04:19:44,500][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 04:19:45,007][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 04:19:45,510][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 04:19:46,014][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 04:19:46,528][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 04:19:47,037][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 04:19:47,553][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 04:19:48,055][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 04:19:48,563][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 04:19:49,072][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 04:19:49,579][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 04:19:50,085][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 04:19:50,588][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 04:19:51,091][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 04:19:51,594][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 04:19:52,095][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 04:19:52,599][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 04:19:53,105][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 04:19:53,606][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 04:19:59,656][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 04:20:00,157][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 04:20:00,660][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 04:20:01,160][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 04:20:01,660][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 04:20:02,162][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 04:20:02,663][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 04:20:03,167][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 04:20:03,673][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 04:20:04,178][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 04:20:04,684][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 04:20:05,193][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 04:20:05,697][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 04:20:06,211][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 04:20:06,723][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 04:20:07,234][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 04:20:07,747][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 04:20:08,254][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 04:20:08,764][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 04:20:09,278][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 04:20:09,785][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 04:20:10,293][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 04:20:10,798][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 04:20:11,308][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 04:20:11,819][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 04:20:12,332][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 04:20:12,844][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 04:20:13,361][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 04:20:13,873][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 04:20:14,385][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 04:20:14,902][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 04:20:15,415][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10880 tokens. [2025-11-13 04:20:16,211][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.30%, Current % of VRAM taken: 59.75%, Block Peak % of device VRAM: 62.42%, ΔTime: 00:00:33 [2025-11-13 04:20:16,971][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 04:20:16,972][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 04:20:16,974][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 04:20:17,982][__main__][INFO] - Iteration 426 took 1m 4s (43.69% Gen, 54.76% Train). Generation: 28s, Training: 35s. Estimated remaining time: 47h 53m 33s. Estimated total time: 54h 4m 9s. Time estimates for 10 more iterations: 10m 48s, 100 more iterations: 1h 48m 8s, 500 more iterations: 9h 0m 41s. [2025-11-13 04:20:17,984][__main__][INFO] - Starting iteration 426. [2025-11-13 04:20:18,468][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 42 and human policies 1. [2025-11-13 04:20:18,468][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 04:20:31,750][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:20:37,280][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:20:41,247][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:20:42,204][__main__][INFO] - Number of regex retries in iteration 426: 3 [2025-11-13 04:20:42,205][__main__][INFO] - agents played in iteration 426 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 04:20:43,044][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:20:43,066][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:20:43,089][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:20:43,111][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:20:43,111][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 04:20:43,112][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 04:20:43,839][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 04:20:44,300][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 04:20:44,809][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 04:20:45,314][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 04:20:45,820][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 04:20:46,324][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 04:20:46,829][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 04:20:47,345][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 04:20:47,853][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 04:20:48,370][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 04:20:48,878][mllm.training.trainer_common][INFO] - 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Processing mini-batch 21 of 64 [2025-11-13 04:20:54,971][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 04:20:55,479][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 04:20:55,986][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 04:20:56,493][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 04:20:56,995][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 04:20:57,499][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 04:20:58,000][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 04:20:58,502][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 04:20:59,005][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 04:20:59,508][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 04:21:00,010][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 04:21:00,515][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 04:21:01,017][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 04:21:01,519][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 04:21:02,027][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 04:21:02,529][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 04:21:03,049][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 04:21:03,555][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 04:21:04,060][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 04:21:04,567][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 04:21:05,073][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 04:21:05,582][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 04:21:06,088][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 04:21:06,594][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 04:21:07,105][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 04:21:07,614][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 04:21:08,123][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 04:21:08,643][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 04:21:09,151][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 04:21:09,669][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 04:21:10,175][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 04:21:10,687][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 04:21:11,199][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 04:21:11,715][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 04:21:12,221][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 04:21:12,731][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 04:21:13,240][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 04:21:13,752][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 04:21:14,265][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 04:21:14,775][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 04:21:15,285][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 04:21:15,793][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 04:21:16,304][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10878 tokens. [2025-11-13 04:21:17,081][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.29%, Current % of VRAM taken: 59.74%, Block Peak % of device VRAM: 62.52%, ΔTime: 00:00:33 [2025-11-13 04:21:17,744][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 04:21:17,746][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 04:21:17,748][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 04:21:18,729][__main__][INFO] - Iteration 427 took 1m 0s (39.39% Gen, 58.98% Train). Generation: 23s, Training: 35s. Estimated remaining time: 44h 1m 28s. Estimated total time: 50h 13m 5s. Time estimates for 10 more iterations: 10m 2s, 100 more iterations: 1h 40m 26s, 500 more iterations: 8h 22m 10s. [2025-11-13 04:21:18,732][__main__][INFO] - Starting iteration 427. [2025-11-13 04:21:19,230][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 42 and human policies 1. [2025-11-13 04:21:19,231][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 04:21:43,625][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:21:44,813][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:21:49,000][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:21:53,364][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:21:54,205][__main__][INFO] - Number of regex retries in iteration 427: 4 [2025-11-13 04:21:54,205][__main__][INFO] - agents played in iteration 427 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 04:21:55,058][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.62%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:21:55,085][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.62%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:21:55,112][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.62%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:21:55,135][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.62%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:21:55,136][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 04:21:55,137][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 04:21:55,835][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 04:21:56,293][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 04:21:56,800][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 04:21:57,303][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 04:21:57,807][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 04:21:58,309][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 04:21:58,812][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 04:21:59,316][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 04:21:59,822][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 04:22:00,322][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 04:22:00,823][mllm.training.trainer_common][INFO] - 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Processing mini-batch 21 of 64 [2025-11-13 04:22:06,875][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 04:22:07,376][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 04:22:07,878][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 04:22:08,378][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 04:22:08,884][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 04:22:09,384][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 04:22:09,885][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 04:22:10,385][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 04:22:10,885][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 04:22:11,385][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 04:22:11,884][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 04:22:12,385][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 04:22:12,889][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 04:22:13,389][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 04:22:13,891][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 04:22:14,396][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 04:22:14,902][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 04:22:15,410][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 04:22:15,914][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 04:22:16,417][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 04:22:16,919][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 04:22:17,426][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 04:22:17,935][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 04:22:18,444][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 04:22:18,951][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 04:22:19,457][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 04:22:19,959][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 04:22:20,473][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 04:22:20,983][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 04:22:21,488][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 04:22:22,002][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 04:22:22,512][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 04:22:23,022][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 04:22:23,533][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 04:22:24,046][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 04:22:24,570][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 04:22:25,074][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 04:22:25,578][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 04:22:26,096][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 04:22:26,602][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 04:22:27,105][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 04:22:27,618][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 04:22:28,131][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10875 tokens. [2025-11-13 04:22:28,956][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.20%, Current % of VRAM taken: 59.65%, Block Peak % of device VRAM: 62.41%, ΔTime: 00:00:33 [2025-11-13 04:22:29,725][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 04:22:29,727][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 04:22:29,729][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 04:22:30,737][__main__][INFO] - Iteration 428 took 1m 11s (48.91% Gen, 49.68% Train). Generation: 34s, Training: 35s. Estimated remaining time: 53h 22m 37s. Estimated total time: 59h 35m 26s. Time estimates for 10 more iterations: 11m 55s, 100 more iterations: 1h 59m 10s, 500 more iterations: 9h 55m 54s. [2025-11-13 04:22:30,740][__main__][INFO] - Starting iteration 428. [2025-11-13 04:22:31,234][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 42 and human policies 1. [2025-11-13 04:22:31,234][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 04:22:43,387][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:22:50,645][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:22:53,890][__main__][INFO] - Number of regex retries in iteration 428: 2 [2025-11-13 04:22:53,890][__main__][INFO] - agents played in iteration 428 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 04:22:54,688][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:22:54,723][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:22:54,752][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:22:54,779][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:22:54,779][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 04:22:54,780][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 04:22:55,487][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 04:22:56,089][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 04:22:56,600][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 04:22:57,104][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 04:22:57,608][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 04:22:58,111][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 04:22:58,616][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 04:22:59,123][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 04:22:59,629][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 04:23:00,135][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 04:23:00,639][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 04:23:01,143][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 04:23:01,674][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 04:23:02,184][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 04:23:02,691][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 04:23:03,195][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 04:23:03,696][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 04:23:04,201][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 04:23:04,700][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 04:23:05,201][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 04:23:05,701][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 04:23:06,201][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 04:23:06,702][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 04:23:07,202][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 04:23:07,703][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 04:23:08,208][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 04:23:08,716][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 04:23:09,220][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 04:23:09,722][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 04:23:10,229][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 04:23:10,740][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 04:23:11,246][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 04:23:11,750][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 04:23:12,253][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 04:23:12,756][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 04:23:13,284][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 04:23:13,794][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 04:23:14,303][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 04:23:14,816][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 04:23:15,324][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 04:23:15,828][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 04:23:16,334][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 04:23:18,445][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 04:23:18,944][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 04:23:19,451][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 04:23:19,977][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 04:23:20,485][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 04:23:20,999][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 04:23:21,508][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 04:23:22,020][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 04:23:22,532][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 04:23:23,042][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 04:23:23,552][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 04:23:24,069][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 04:23:24,570][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 04:23:25,082][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 04:23:25,586][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 04:23:26,087][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 04:23:26,597][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 04:23:27,097][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 04:23:27,599][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 04:23:28,103][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 04:23:28,609][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 04:23:29,114][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 04:23:29,621][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10877 tokens. [2025-11-13 04:23:30,399][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.23%, Current % of VRAM taken: 59.68%, Block Peak % of device VRAM: 62.41%, ΔTime: 00:00:34 [2025-11-13 04:23:31,069][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 04:23:31,071][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 04:23:31,072][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 04:23:32,051][__main__][INFO] - Iteration 429 took 1m 0s (37.25% Gen, 61.14% Train). Generation: 22s, Training: 37s. Estimated remaining time: 44h 27m 3s. Estimated total time: 50h 40m 53s. Time estimates for 10 more iterations: 10m 8s, 100 more iterations: 1h 41m 21s, 500 more iterations: 8h 26m 48s. [2025-11-13 04:23:32,053][__main__][INFO] - Starting iteration 429. [2025-11-13 04:23:32,549][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 42 and human policies 1. [2025-11-13 04:23:32,549][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 04:24:01,861][__main__][INFO] - Number of regex retries in iteration 429: 0 [2025-11-13 04:24:01,862][__main__][INFO] - agents played in iteration 429 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 04:24:02,770][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:24:02,793][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:24:02,816][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:24:02,838][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:24:02,839][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 04:24:02,840][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 04:24:03,554][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 04:24:04,013][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 04:24:04,524][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 04:24:05,029][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 04:24:05,531][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 04:24:06,038][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 04:24:06,541][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 04:24:07,043][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 04:24:07,546][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 04:24:08,058][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 04:24:08,563][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 04:24:09,065][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 04:24:09,566][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 04:24:10,068][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 04:24:10,572][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 04:24:11,095][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 04:24:11,601][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 04:24:12,106][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 04:24:12,608][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 04:24:13,109][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 04:24:13,618][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 04:24:14,128][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 04:24:14,635][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 04:24:15,144][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 04:24:15,649][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 04:24:16,154][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 04:24:16,664][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 04:24:17,173][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 04:24:17,695][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 04:24:18,201][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 04:24:18,714][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 04:24:19,224][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 04:24:19,733][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 04:24:20,249][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 04:24:20,762][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 04:24:21,272][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 04:24:21,783][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 04:24:22,294][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 04:24:22,802][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 04:24:23,313][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 04:24:23,823][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 04:24:24,335][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 04:24:24,844][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 04:24:25,353][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 04:24:25,875][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 04:24:26,383][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 04:24:26,901][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 04:24:27,412][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 04:24:27,924][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 04:24:28,434][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 04:24:28,944][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 04:24:29,454][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 04:24:29,965][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 04:24:30,473][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 04:24:30,985][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 04:24:31,498][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 04:24:32,010][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 04:24:32,521][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 04:24:33,030][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 04:24:33,543][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 04:24:34,050][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 04:24:34,555][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 04:24:35,075][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 04:24:35,581][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 04:24:36,096][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10876 tokens. [2025-11-13 04:24:36,880][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.20%, Current % of VRAM taken: 59.65%, Block Peak % of device VRAM: 62.35%, ΔTime: 00:00:33 [2025-11-13 04:24:37,663][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 04:24:37,665][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 04:24:37,667][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 04:24:38,655][__main__][INFO] - Iteration 430 took 1m 6s (44.34% Gen, 54.16% Train). Generation: 29s, Training: 35s. Estimated remaining time: 48h 50m 24s. Estimated total time: 55h 5m 21s. Time estimates for 10 more iterations: 11m 1s, 100 more iterations: 1h 50m 10s, 500 more iterations: 9h 10m 53s. [2025-11-13 04:24:38,657][__main__][INFO] - Starting iteration 430. [2025-11-13 04:24:39,155][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 42 and human policies 1. [2025-11-13 04:24:39,155][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 04:24:51,756][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:24:54,433][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:25:00,909][__main__][INFO] - Number of regex retries in iteration 430: 2 [2025-11-13 04:25:00,911][__main__][INFO] - agents played in iteration 430 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 04:25:01,693][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:25:01,722][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:25:01,748][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:25:01,771][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:25:01,772][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 04:25:01,773][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 04:25:02,482][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 04:25:02,939][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 04:25:03,446][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 04:25:03,953][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 04:25:04,456][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 04:25:04,963][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 04:25:05,468][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 04:25:05,970][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 04:25:06,488][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 04:25:06,991][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 04:25:07,497][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 04:25:08,006][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 04:25:08,511][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 04:25:09,016][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 04:25:09,518][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 04:25:10,021][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 04:25:10,525][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 04:25:11,025][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 04:25:11,526][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 04:25:12,026][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 04:25:12,525][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 04:25:13,028][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 04:25:19,037][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 04:25:19,537][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 04:25:20,038][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 04:25:20,539][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 04:25:21,042][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 04:25:21,547][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 04:25:22,051][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 04:25:22,556][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 04:25:23,062][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 04:25:23,576][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 04:25:24,081][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 04:25:24,587][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 04:25:25,100][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 04:25:25,608][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 04:25:26,119][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 04:25:26,629][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 04:25:27,135][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 04:25:27,644][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 04:25:28,154][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 04:25:28,663][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 04:25:29,174][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 04:25:29,684][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 04:25:30,195][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 04:25:30,704][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 04:25:31,211][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 04:25:31,715][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 04:25:32,229][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 04:25:32,738][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 04:25:33,248][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 04:25:33,763][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 04:25:34,281][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 04:25:34,783][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10877 tokens. [2025-11-13 04:25:35,601][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.19%, Current % of VRAM taken: 59.64%, Block Peak % of device VRAM: 62.31%, ΔTime: 00:00:33 [2025-11-13 04:25:36,366][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 04:25:36,367][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 04:25:36,369][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 04:25:38,397][__main__][INFO] - Iteration 431 took 59s (36.72% Gen, 59.85% Train). Generation: 21s, Training: 35s. Estimated remaining time: 43h 6m 15s. Estimated total time: 49h 22m 11s. Time estimates for 10 more iterations: 9m 52s, 100 more iterations: 1h 38m 44s, 500 more iterations: 8h 13m 41s. [2025-11-13 04:25:38,400][__main__][INFO] - Starting iteration 431. [2025-11-13 04:25:38,889][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 43 and human policies 1. [2025-11-13 04:25:38,889][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 04:26:00,666][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:26:06,554][__main__][INFO] - Number of regex retries in iteration 431: 1 [2025-11-13 04:26:06,554][__main__][INFO] - agents played in iteration 431 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 04:26:07,392][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:26:07,414][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:26:07,436][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:26:07,458][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:26:07,459][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 04:26:07,460][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 04:26:08,177][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 04:26:08,643][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 04:26:09,149][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 04:26:09,651][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 04:26:10,154][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 04:26:10,658][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 04:26:11,161][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 04:26:11,662][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 04:26:12,164][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 04:26:12,671][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 04:26:13,174][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 04:26:24,806][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 04:26:25,322][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 04:26:25,829][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 04:26:26,344][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 04:26:26,859][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 04:26:27,370][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 04:26:27,890][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 04:26:28,399][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 04:26:28,910][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 04:26:29,425][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 04:26:29,935][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 04:26:30,445][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 04:26:30,953][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 04:26:31,462][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 04:26:31,974][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 04:26:32,488][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 04:26:33,000][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 04:26:33,513][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 04:26:34,026][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 04:26:34,539][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 04:26:35,051][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 04:26:35,563][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 04:26:36,074][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 04:26:36,584][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 04:26:37,108][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 04:26:37,619][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 04:26:38,134][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 04:26:38,651][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 04:26:39,162][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 04:26:39,670][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 04:26:40,182][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 04:26:40,695][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10879 tokens. [2025-11-13 04:26:41,513][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.21%, Current % of VRAM taken: 59.66%, Block Peak % of device VRAM: 62.24%, ΔTime: 00:00:33 [2025-11-13 04:26:42,169][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 04:26:42,171][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 04:26:42,173][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 04:26:43,100][__main__][INFO] - Iteration 432 took 1m 4s (43.08% Gen, 55.47% Train). Generation: 27s, Training: 35s. Estimated remaining time: 47h 13m 34s. Estimated total time: 53h 30m 35s. Time estimates for 10 more iterations: 10m 42s, 100 more iterations: 1h 47m 1s, 500 more iterations: 8h 55m 5s. [2025-11-13 04:26:43,103][__main__][INFO] - Starting iteration 432. [2025-11-13 04:26:43,621][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 43 and human policies 1. [2025-11-13 04:26:43,622][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 04:26:58,676][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:27:09,106][__main__][INFO] - Number of regex retries in iteration 432: 1 [2025-11-13 04:27:09,107][__main__][INFO] - agents played in iteration 432 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 04:27:09,935][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:27:09,965][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:27:09,995][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:27:10,021][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:27:10,022][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 04:27:10,023][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 04:27:10,750][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 04:27:11,209][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 04:27:11,716][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 04:27:12,219][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 04:27:12,724][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 04:27:13,226][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 04:27:13,731][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 04:27:14,236][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 04:27:14,741][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 04:27:15,243][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 04:27:15,744][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 04:27:16,249][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 04:27:16,755][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 04:27:17,258][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 04:27:17,761][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 04:27:18,264][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 04:27:18,766][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 04:27:19,272][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 04:27:19,776][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 04:27:20,276][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 04:27:20,779][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 04:27:21,279][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 04:27:27,323][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 04:27:27,825][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 04:27:28,325][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 04:27:28,827][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 04:27:29,327][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 04:27:29,826][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 04:27:30,327][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 04:27:30,828][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 04:27:31,334][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 04:27:31,839][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 04:27:32,342][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 04:27:32,844][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 04:27:33,348][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 04:27:33,851][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 04:27:34,356][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 04:27:34,865][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 04:27:35,375][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 04:27:35,885][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 04:27:36,394][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 04:27:36,907][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 04:27:37,416][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 04:27:37,927][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 04:27:38,437][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 04:27:38,949][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 04:27:39,465][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 04:27:39,976][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 04:27:40,484][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 04:27:40,996][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 04:27:41,507][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 04:27:42,021][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 04:27:42,531][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 04:27:43,044][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10877 tokens. [2025-11-13 04:27:43,882][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.23%, Current % of VRAM taken: 59.68%, Block Peak % of device VRAM: 62.24%, ΔTime: 00:00:33 [2025-11-13 04:27:44,619][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 04:27:44,621][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 04:27:44,623][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 04:27:45,655][__main__][INFO] - Iteration 433 took 1m 2s (41.08% Gen, 57.25% Train). Generation: 25s, Training: 35s. Estimated remaining time: 45h 23m 37s. Estimated total time: 51h 41m 41s. Time estimates for 10 more iterations: 10m 20s, 100 more iterations: 1h 43m 23s, 500 more iterations: 8h 36m 56s. [2025-11-13 04:27:45,657][__main__][INFO] - Starting iteration 433. [2025-11-13 04:27:46,148][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 43 and human policies 1. [2025-11-13 04:27:46,148][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 04:28:11,045][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:28:14,381][__main__][INFO] - Number of regex retries in iteration 433: 1 [2025-11-13 04:28:14,382][__main__][INFO] - agents played in iteration 433 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 04:28:15,223][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:28:15,250][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:28:15,275][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:28:15,298][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:28:15,299][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 04:28:15,299][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 04:28:16,011][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 04:28:16,470][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 04:28:16,980][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 04:28:17,484][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 04:28:17,989][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 04:28:18,496][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 04:28:18,999][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 04:28:19,502][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 04:28:20,012][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 04:28:20,516][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 04:28:21,031][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 04:28:21,535][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 04:28:22,037][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 04:28:22,543][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 04:28:23,045][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 04:28:23,546][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 04:28:24,047][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 04:28:24,552][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 04:28:25,059][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 04:28:25,576][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 04:28:26,081][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 04:28:26,599][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 04:28:27,101][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 04:28:27,620][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 04:28:28,121][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 04:28:28,623][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 04:28:29,133][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 04:28:29,636][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 04:28:30,139][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 04:28:30,642][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 04:28:31,148][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 04:28:31,652][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 04:28:32,155][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 04:28:32,656][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 04:28:33,159][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 04:28:33,660][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 04:28:34,161][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 04:28:34,662][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 04:28:35,165][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 04:28:35,670][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 04:28:36,178][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 04:28:36,687][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 04:28:37,192][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 04:28:37,696][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 04:28:38,222][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 04:28:38,732][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 04:28:39,245][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 04:28:39,757][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 04:28:40,269][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 04:28:40,780][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 04:28:41,291][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 04:28:41,805][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 04:28:42,317][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 04:28:42,829][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 04:28:43,341][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 04:28:43,851][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 04:28:44,363][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 04:28:44,876][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 04:28:45,389][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 04:28:45,905][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 04:28:46,415][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 04:28:46,924][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 04:28:47,438][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 04:28:47,950][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 04:28:48,461][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10880 tokens. [2025-11-13 04:28:49,272][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.26%, Current % of VRAM taken: 59.72%, Block Peak % of device VRAM: 62.31%, ΔTime: 00:00:33 [2025-11-13 04:28:49,946][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 04:28:49,948][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 04:28:49,950][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 04:28:50,910][__main__][INFO] - Iteration 434 took 1m 4s (43.59% Gen, 54.92% Train). Generation: 28s, Training: 35s. Estimated remaining time: 47h 38m 59s. Estimated total time: 53h 58m 8s. Time estimates for 10 more iterations: 10m 47s, 100 more iterations: 1h 47m 56s, 500 more iterations: 8h 59m 41s. [2025-11-13 04:28:50,912][__main__][INFO] - Starting iteration 434. [2025-11-13 04:28:51,429][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 43 and human policies 1. [2025-11-13 04:28:51,430][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 04:29:03,746][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:29:04,347][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:29:07,223][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:29:10,175][mllm.models.large_language_model_local][WARNING] - Response Given that both you and Alice value hats and books highly and balls less, a competitive and cooperative approach would be to propose a split that maximizes the value for both of you while ensuring neither agent perceives the other as getting an unfair advantage. Here is a balanced proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:29:12,849][__main__][INFO] - Number of regex retries in iteration 434: 4 [2025-11-13 04:29:12,850][__main__][INFO] - agents played in iteration 434 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 04:29:13,778][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:29:13,802][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:29:13,824][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:29:13,846][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:29:13,847][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 04:29:13,848][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 04:29:14,637][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 04:29:15,111][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 04:29:15,619][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 04:29:16,138][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 04:29:16,641][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 04:29:17,145][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 04:29:17,651][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 04:29:18,154][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 04:29:18,663][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 04:29:19,164][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 04:29:19,663][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 04:29:20,165][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 04:29:20,664][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 04:29:21,168][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 04:29:21,667][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 04:29:22,166][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 04:29:22,669][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 04:29:23,169][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 04:29:23,671][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 04:29:24,172][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 04:29:24,673][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 04:29:25,175][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 04:29:31,210][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 04:29:31,720][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 04:29:32,220][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 04:29:32,720][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 04:29:33,239][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 04:29:33,739][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 04:29:34,241][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 04:29:34,744][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 04:29:35,248][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 04:29:35,753][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 04:29:36,256][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 04:29:36,757][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 04:29:37,260][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 04:29:37,764][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 04:29:38,265][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 04:29:38,766][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 04:29:39,267][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 04:29:39,775][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 04:29:40,279][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 04:29:40,781][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 04:29:41,287][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 04:29:41,792][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 04:29:42,296][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 04:29:42,800][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 04:29:43,300][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 04:29:43,816][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 04:29:44,326][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 04:29:44,832][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 04:29:45,344][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 04:29:45,850][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 04:29:46,361][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 04:29:46,868][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10880 tokens. [2025-11-13 04:29:47,662][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.18%, Current % of VRAM taken: 59.64%, Block Peak % of device VRAM: 62.31%, ΔTime: 00:00:33 [2025-11-13 04:29:48,411][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 04:29:48,413][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 04:29:48,414][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 04:29:49,389][__main__][INFO] - Iteration 435 took 57s (36.96% Gen, 61.36% Train). Generation: 21s, Training: 35s. Estimated remaining time: 41h 57m 54s. Estimated total time: 48h 18m 2s. Time estimates for 10 more iterations: 9m 39s, 100 more iterations: 1h 36m 36s, 500 more iterations: 8h 3m 0s. [2025-11-13 04:29:49,391][__main__][INFO] - Starting iteration 435. [2025-11-13 04:29:49,872][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 43 and human policies 1. [2025-11-13 04:29:49,872][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 04:30:01,693][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:30:01,766][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:30:01,858][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:30:08,888][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:30:12,450][__main__][INFO] - Number of regex retries in iteration 435: 4 [2025-11-13 04:30:12,450][__main__][INFO] - agents played in iteration 435 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 04:30:13,345][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:30:13,372][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:30:13,399][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:30:13,422][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:30:13,423][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 04:30:13,424][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 04:30:14,190][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 04:30:14,654][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 04:30:15,171][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 04:30:15,680][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 04:30:16,190][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 04:30:16,702][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 04:30:17,212][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 04:30:17,724][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 04:30:18,239][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 04:30:18,752][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 04:30:19,266][mllm.training.trainer_common][INFO] - 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Processing mini-batch 21 of 64 [2025-11-13 04:30:25,417][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 04:30:25,931][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 04:30:26,444][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 04:30:26,956][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 04:30:27,466][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 04:30:27,982][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 04:30:28,498][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 04:30:29,005][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 04:30:29,513][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 04:30:30,028][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 04:30:30,529][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 04:30:31,049][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 04:30:31,555][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 04:30:32,058][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 04:30:32,567][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 04:30:33,071][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 04:30:33,574][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 04:30:34,079][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 04:30:34,581][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 04:30:35,085][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 04:30:35,588][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 04:30:36,093][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 04:30:36,603][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 04:30:37,107][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 04:30:37,609][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 04:30:38,115][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 04:30:38,620][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 04:30:39,135][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 04:30:39,641][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 04:30:40,144][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 04:30:40,660][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 04:30:41,162][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 04:30:41,666][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 04:30:42,169][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 04:30:42,673][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 04:30:43,188][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 04:30:43,694][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 04:30:44,201][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 04:30:44,708][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 04:30:45,215][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 04:30:45,731][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 04:30:46,240][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 04:30:46,751][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10879 tokens. [2025-11-13 04:30:47,526][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.32%, Current % of VRAM taken: 59.77%, Block Peak % of device VRAM: 62.52%, ΔTime: 00:00:33 [2025-11-13 04:30:48,152][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 04:30:48,154][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 04:30:48,156][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 04:30:49,143][__main__][INFO] - Iteration 436 took 59s (38.09% Gen, 60.24% Train). Generation: 22s, Training: 35s. Estimated remaining time: 43h 2m 30s. Estimated total time: 49h 23m 38s. Time estimates for 10 more iterations: 9m 52s, 100 more iterations: 1h 38m 47s, 500 more iterations: 8h 13m 56s. [2025-11-13 04:30:49,146][__main__][INFO] - Starting iteration 436. [2025-11-13 04:30:49,674][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 43 and human policies 1. [2025-11-13 04:30:49,674][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 04:31:18,627][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:31:19,397][__main__][INFO] - Number of regex retries in iteration 436: 1 [2025-11-13 04:31:19,398][__main__][INFO] - agents played in iteration 436 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 04:31:20,219][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.62%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:31:20,244][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.62%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:31:20,269][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.62%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:31:20,291][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.62%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:31:20,292][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 04:31:20,293][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 04:31:21,098][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 04:31:21,564][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 04:31:22,079][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 04:31:22,592][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 04:31:23,102][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 04:31:23,618][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 04:31:24,128][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 04:31:24,633][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 04:31:25,138][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 04:31:25,655][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 04:31:26,167][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 04:31:26,684][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 04:31:27,190][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 04:31:27,701][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 04:31:28,212][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 04:31:28,718][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 04:31:29,226][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 04:31:29,727][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 04:31:30,229][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 04:31:30,744][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 04:31:31,250][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 04:31:31,763][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 04:31:37,793][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 04:31:38,294][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 04:31:38,802][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 04:31:39,304][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 04:31:39,804][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 04:31:40,304][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 04:31:40,806][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 04:31:41,312][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 04:31:41,813][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 04:31:42,313][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 04:31:42,814][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 04:31:43,313][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 04:31:43,814][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 04:31:44,313][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 04:31:44,813][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 04:31:45,313][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 04:31:45,818][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 04:31:46,318][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 04:31:46,822][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 04:31:47,322][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 04:31:47,830][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 04:31:48,331][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 04:31:48,831][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 04:31:49,340][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 04:31:49,842][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 04:31:50,344][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 04:31:50,847][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 04:31:51,349][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 04:31:51,865][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 04:31:52,366][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 04:31:52,867][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 04:31:53,370][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10878 tokens. [2025-11-13 04:31:54,094][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.23%, Current % of VRAM taken: 59.68%, Block Peak % of device VRAM: 62.34%, ΔTime: 00:00:33 [2025-11-13 04:31:54,819][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 04:31:54,820][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 04:31:54,822][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 04:31:55,812][__main__][INFO] - Iteration 437 took 1m 6s (44.94% Gen, 53.56% Train). Generation: 29s, Training: 35s. Estimated remaining time: 48h 44m 45s. Estimated total time: 55h 6m 59s. Time estimates for 10 more iterations: 11m 1s, 100 more iterations: 1h 50m 13s, 500 more iterations: 9h 11m 9s. [2025-11-13 04:31:55,814][__main__][INFO] - Starting iteration 437. [2025-11-13 04:31:56,284][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 43 and human policies 1. [2025-11-13 04:31:56,284][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 04:32:09,592][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 20 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:32:16,934][__main__][INFO] - Number of regex retries in iteration 437: 1 [2025-11-13 04:32:16,936][__main__][INFO] - agents played in iteration 437 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 04:32:17,815][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:32:17,840][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:32:17,866][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:32:17,902][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:32:17,902][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 04:32:17,903][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 04:32:18,708][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 04:32:19,176][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 04:32:19,691][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 04:32:20,201][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 04:32:20,713][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 04:32:21,230][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 04:32:21,743][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 04:32:22,259][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 04:32:22,767][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 04:32:23,284][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 04:32:23,795][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 04:32:24,307][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 04:32:24,819][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 04:32:25,329][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 04:32:25,839][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 04:32:26,352][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 04:32:26,859][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 04:32:27,385][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 04:32:27,896][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 04:32:28,407][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 04:32:28,916][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 04:32:29,426][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 04:32:29,935][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 04:32:30,446][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 04:32:30,957][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 04:32:31,470][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 04:32:31,980][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 04:32:32,492][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 04:32:33,004][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 04:32:33,511][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 04:32:34,018][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 04:32:34,529][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 04:32:35,054][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 04:32:35,566][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 04:32:36,072][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 04:32:36,580][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 04:32:37,086][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 04:32:37,592][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 04:32:38,102][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 04:32:38,610][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 04:32:39,118][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 04:32:39,627][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 04:32:40,136][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 04:32:40,644][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 04:32:41,149][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 04:32:41,665][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 04:32:42,165][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 04:32:42,665][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 04:32:43,167][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 04:32:43,670][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 04:32:44,174][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 04:32:44,674][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 04:32:45,172][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 04:32:45,672][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 04:32:46,169][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 04:32:46,666][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 04:32:47,164][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 04:32:47,666][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 04:32:48,166][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 04:32:48,667][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 04:32:49,168][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 04:32:49,678][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 04:32:50,179][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 04:32:50,681][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 04:32:51,182][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10875 tokens. [2025-11-13 04:32:51,944][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.20%, Current % of VRAM taken: 59.65%, Block Peak % of device VRAM: 62.24%, ΔTime: 00:00:33 [2025-11-13 04:32:52,598][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 04:32:52,600][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 04:32:52,602][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 04:32:53,566][__main__][INFO] - Iteration 438 took 57s (36.05% Gen, 62.26% Train). Generation: 20s, Training: 35s. Estimated remaining time: 41h 20m 57s. Estimated total time: 47h 44m 9s. Time estimates for 10 more iterations: 9m 32s, 100 more iterations: 1h 35m 28s, 500 more iterations: 7h 57m 21s. [2025-11-13 04:32:53,568][__main__][INFO] - Starting iteration 438. [2025-11-13 04:32:54,062][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 43 and human policies 1. [2025-11-13 04:32:54,062][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 04:33:16,588][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:33:19,668][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:33:20,891][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:33:26,352][__main__][INFO] - Number of regex retries in iteration 438: 3 [2025-11-13 04:33:26,353][__main__][INFO] - agents played in iteration 438 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 04:33:27,151][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:33:27,174][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:33:27,209][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:33:27,231][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:33:27,232][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 04:33:27,233][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 04:33:27,981][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 04:33:28,443][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 04:33:28,954][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 04:33:29,460][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 04:33:29,967][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 04:33:30,473][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 04:33:30,980][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 04:33:31,491][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 04:33:32,005][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 04:33:32,515][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 04:33:33,027][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 04:33:33,536][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 04:33:34,071][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 04:33:34,581][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 04:33:35,093][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 04:33:35,607][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 04:33:36,115][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 04:33:36,621][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 04:33:37,131][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 04:33:37,641][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 04:33:38,154][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 04:33:38,666][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 04:33:39,178][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 04:33:39,685][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 04:33:40,199][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 04:33:40,713][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 04:33:41,224][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 04:33:41,730][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 04:33:42,240][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 04:33:42,739][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 04:33:43,246][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 04:33:43,752][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 04:33:44,254][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 04:33:44,757][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 04:33:45,258][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 04:33:45,758][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 04:33:46,260][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 04:33:46,762][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 04:33:47,274][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 04:33:47,776][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 04:33:48,281][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 04:33:48,793][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 04:33:49,296][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 04:33:49,799][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 04:33:50,301][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 04:33:50,802][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 04:33:51,305][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 04:33:51,806][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 04:33:52,312][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 04:33:52,817][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 04:33:53,321][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 04:33:53,822][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 04:33:54,324][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 04:33:54,826][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 04:33:55,327][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 04:33:55,833][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 04:33:56,336][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 04:33:56,844][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 04:33:57,344][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 04:33:57,845][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 04:33:58,345][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 04:33:58,846][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 04:33:59,359][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 04:33:59,859][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 04:34:00,359][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10876 tokens. [2025-11-13 04:34:01,064][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.20%, Current % of VRAM taken: 59.65%, Block Peak % of device VRAM: 62.32%, ΔTime: 00:00:33 [2025-11-13 04:34:01,819][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 04:34:01,821][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 04:34:01,822][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 04:34:02,830][__main__][INFO] - Iteration 439 took 1m 8s (46.95% Gen, 51.58% Train). Generation: 32s, Training: 35s. Estimated remaining time: 50h 54m 6s. Estimated total time: 57h 18m 27s. Time estimates for 10 more iterations: 11m 27s, 100 more iterations: 1h 54m 36s, 500 more iterations: 9h 33m 4s. [2025-11-13 04:34:02,832][__main__][INFO] - Starting iteration 439. [2025-11-13 04:34:03,306][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 43 and human policies 1. [2025-11-13 04:34:03,307][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 04:34:26,339][__main__][INFO] - Number of regex retries in iteration 439: 0 [2025-11-13 04:34:26,340][__main__][INFO] - agents played in iteration 439 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 04:34:27,213][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:34:27,258][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:34:27,294][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:34:27,323][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:34:27,324][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 04:34:27,325][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 04:34:28,127][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 04:34:28,590][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 04:34:29,102][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 04:34:29,610][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 04:34:30,117][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 04:34:30,629][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 04:34:31,146][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 04:34:31,657][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 04:34:32,174][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 04:34:32,686][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 04:34:33,195][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 04:34:33,704][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 04:34:34,213][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 04:34:34,725][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 04:34:35,235][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 04:34:35,747][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 04:34:36,258][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 04:34:36,769][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 04:34:37,277][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 04:34:37,786][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 04:34:38,299][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 04:34:38,811][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 04:34:39,319][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 04:34:39,829][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 04:34:40,342][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 04:34:40,856][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 04:34:41,387][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 04:34:41,896][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 04:34:42,406][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 04:34:42,918][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 04:34:43,427][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 04:34:43,938][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 04:34:44,453][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 04:34:44,964][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 04:34:45,475][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 04:34:45,987][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 04:34:46,501][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 04:34:47,008][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 04:34:47,517][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 04:34:48,030][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 04:34:48,540][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 04:34:49,046][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 04:34:49,548][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 04:34:50,050][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 04:34:50,555][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 04:34:51,056][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 04:34:51,556][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 04:34:52,070][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 04:34:52,570][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 04:34:53,072][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 04:34:53,575][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 04:34:54,077][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 04:34:54,587][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 04:34:55,089][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 04:34:55,591][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 04:34:56,102][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 04:34:56,604][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 04:34:57,105][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 04:34:57,605][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 04:34:58,107][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 04:34:58,608][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 04:34:59,109][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 04:34:59,610][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 04:35:00,115][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 04:35:00,618][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10878 tokens. [2025-11-13 04:35:01,336][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.27%, Current % of VRAM taken: 59.72%, Block Peak % of device VRAM: 62.52%, ΔTime: 00:00:33 [2025-11-13 04:35:01,964][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 04:35:01,966][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 04:35:01,967][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 04:35:02,926][__main__][INFO] - Iteration 440 took 59s (38.63% Gen, 59.76% Train). Generation: 23s, Training: 35s. Estimated remaining time: 43h 15m 40s. Estimated total time: 49h 41m 2s. Time estimates for 10 more iterations: 9m 56s, 100 more iterations: 1h 39m 22s, 500 more iterations: 8h 16m 50s. [2025-11-13 04:35:02,928][__main__][INFO] - Starting iteration 440. [2025-11-13 04:35:03,422][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 43 and human policies 1. [2025-11-13 04:35:03,423][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 04:35:24,385][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:35:32,829][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:35:35,513][__main__][INFO] - Number of regex retries in iteration 440: 2 [2025-11-13 04:35:35,513][__main__][INFO] - agents played in iteration 440 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 04:35:36,421][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:35:36,446][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:35:36,471][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:35:36,494][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:35:36,495][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 04:35:36,496][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 04:35:37,286][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 04:35:37,747][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 04:35:38,267][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 04:35:38,771][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 04:35:39,278][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 04:35:39,787][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 04:35:40,289][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 04:35:40,804][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 04:35:41,312][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 04:35:41,828][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 04:35:42,341][mllm.training.trainer_common][INFO] - 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Processing mini-batch 43 of 64 [2025-11-13 04:35:59,629][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 04:36:00,132][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 04:36:00,636][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 04:36:01,137][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 04:36:01,639][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 04:36:02,141][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 04:36:02,643][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 04:36:03,149][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 04:36:03,651][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 04:36:04,152][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 04:36:04,654][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 04:36:05,158][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 04:36:05,666][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 04:36:06,169][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 04:36:06,671][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 04:36:07,187][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 04:36:07,689][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 04:36:08,189][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 04:36:08,695][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 04:36:09,199][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 04:36:09,703][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10880 tokens. [2025-11-13 04:36:10,409][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.19%, Current % of VRAM taken: 59.64%, Block Peak % of device VRAM: 62.24%, ΔTime: 00:00:33 [2025-11-13 04:36:11,172][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 04:36:11,174][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 04:36:11,175][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 04:36:13,142][__main__][INFO] - Iteration 441 took 1m 9s (46.03% Gen, 51.15% Train). Generation: 32s, Training: 35s. Estimated remaining time: 51h 39m 30s. Estimated total time: 58h 6m 1s. Time estimates for 10 more iterations: 11m 37s, 100 more iterations: 1h 56m 12s, 500 more iterations: 9h 41m 0s. [2025-11-13 04:36:13,144][__main__][INFO] - Starting iteration 441. [2025-11-13 04:36:13,623][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 44 and human policies 1. [2025-11-13 04:36:13,624][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 04:36:27,387][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:36:31,283][__main__][INFO] - Number of regex retries in iteration 441: 1 [2025-11-13 04:36:31,284][__main__][INFO] - agents played in iteration 441 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 04:36:32,156][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:36:32,180][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:36:32,206][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:36:32,229][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:36:32,229][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 04:36:32,230][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 04:36:32,941][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 04:36:33,403][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 04:36:33,911][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 04:36:34,419][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 04:36:34,924][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 04:36:35,428][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 04:36:35,934][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 04:36:36,438][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 04:36:36,943][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 04:36:37,454][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 04:36:37,958][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 04:36:49,646][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 04:36:50,155][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 04:36:50,661][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 04:36:51,167][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 04:36:52,302][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 04:36:53,917][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 04:36:54,433][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 04:36:54,944][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 04:36:55,452][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 04:36:55,962][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 04:36:56,471][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 04:36:56,984][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 04:36:57,496][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 04:36:58,005][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 04:36:58,516][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 04:36:59,018][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 04:36:59,521][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 04:37:00,023][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 04:37:00,526][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 04:37:01,033][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 04:37:01,536][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 04:37:02,037][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 04:37:02,543][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 04:37:03,047][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 04:37:03,549][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 04:37:04,051][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 04:37:04,552][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 04:37:05,054][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 04:37:05,556][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 04:37:06,057][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 04:37:06,559][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 04:37:07,064][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10879 tokens. [2025-11-13 04:37:07,849][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.17%, Current % of VRAM taken: 59.63%, Block Peak % of device VRAM: 62.41%, ΔTime: 00:00:34 [2025-11-13 04:37:08,516][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 04:37:08,518][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 04:37:08,519][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 04:37:09,448][__main__][INFO] - Iteration 442 took 55s (31.63% Gen, 66.70% Train). Generation: 17s, Training: 37s. Estimated remaining time: 40h 3m 46s. Estimated total time: 46h 31m 14s. Time estimates for 10 more iterations: 9m 18s, 100 more iterations: 1h 33m 2s, 500 more iterations: 7h 45m 12s. [2025-11-13 04:37:09,452][__main__][INFO] - Starting iteration 442. [2025-11-13 04:37:09,945][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 44 and human policies 1. [2025-11-13 04:37:09,946][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 04:37:30,986][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:37:32,892][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:37:39,217][__main__][INFO] - Number of regex retries in iteration 442: 2 [2025-11-13 04:37:39,218][__main__][INFO] - agents played in iteration 442 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 04:37:39,999][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:37:40,027][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:37:40,054][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:37:40,078][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:37:40,078][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 04:37:40,079][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 04:37:40,812][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 04:37:41,268][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 04:37:41,786][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 04:37:42,287][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 04:37:42,789][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 04:37:43,297][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 04:37:43,800][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 04:37:44,311][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 04:37:44,812][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 04:37:45,316][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 04:37:45,822][mllm.training.trainer_common][INFO] - 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Processing mini-batch 43 of 64 [2025-11-13 04:38:03,165][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 04:38:03,677][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 04:38:04,185][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 04:38:04,694][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 04:38:05,205][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 04:38:05,728][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 04:38:06,229][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 04:38:06,733][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 04:38:07,239][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 04:38:07,746][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 04:38:08,249][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 04:38:08,751][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 04:38:09,256][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 04:38:09,767][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 04:38:10,271][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 04:38:10,777][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 04:38:11,283][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 04:38:11,790][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 04:38:12,293][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 04:38:12,796][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 04:38:13,300][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10879 tokens. [2025-11-13 04:38:14,038][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.30%, Current % of VRAM taken: 59.75%, Block Peak % of device VRAM: 62.41%, ΔTime: 00:00:33 [2025-11-13 04:38:14,826][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 04:38:14,827][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 04:38:14,829][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 04:38:15,914][__main__][INFO] - Iteration 443 took 1m 5s (44.37% Gen, 53.98% Train). Generation: 29s, Training: 35s. Estimated remaining time: 48h 29m 56s. Estimated total time: 54h 58m 30s. Time estimates for 10 more iterations: 10m 59s, 100 more iterations: 1h 49m 57s, 500 more iterations: 9h 9m 45s. [2025-11-13 04:38:15,916][__main__][INFO] - Starting iteration 443. [2025-11-13 04:38:16,389][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 44 and human policies 1. [2025-11-13 04:38:16,390][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 04:38:31,004][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:38:40,006][__main__][INFO] - Number of regex retries in iteration 443: 1 [2025-11-13 04:38:40,006][__main__][INFO] - agents played in iteration 443 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 04:38:40,782][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:38:40,811][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:38:40,837][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:38:40,861][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:38:40,861][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 04:38:40,862][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 04:38:41,596][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 04:38:42,053][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 04:38:42,565][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 04:38:43,068][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 04:38:43,571][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 04:38:44,078][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 04:38:44,580][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 04:38:45,083][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 04:38:45,585][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 04:38:46,088][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 04:38:46,597][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 04:38:58,260][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 04:38:58,770][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 04:38:59,273][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 04:38:59,782][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 04:39:00,291][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 04:39:02,068][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 04:39:02,651][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 04:39:03,167][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 04:39:03,680][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 04:39:04,188][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 04:39:04,696][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 04:39:05,214][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 04:39:05,723][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 04:39:06,238][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 04:39:06,748][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 04:39:07,258][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 04:39:07,767][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 04:39:08,274][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 04:39:08,783][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 04:39:09,293][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 04:39:09,802][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 04:39:10,323][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 04:39:10,831][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 04:39:11,345][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 04:39:11,857][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 04:39:12,368][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 04:39:12,875][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 04:39:13,384][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 04:39:13,895][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 04:39:14,407][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 04:39:14,914][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 04:39:15,418][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10876 tokens. [2025-11-13 04:39:16,157][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.20%, Current % of VRAM taken: 59.65%, Block Peak % of device VRAM: 62.34%, ΔTime: 00:00:34 [2025-11-13 04:39:16,823][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 04:39:16,825][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 04:39:16,827][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 04:39:17,809][__main__][INFO] - Iteration 444 took 1m 1s (38.45% Gen, 59.95% Train). Generation: 23s, Training: 36s. Estimated remaining time: 44h 41m 26s. Estimated total time: 51h 11m 2s. Time estimates for 10 more iterations: 10m 14s, 100 more iterations: 1h 42m 22s, 500 more iterations: 8h 31m 50s. [2025-11-13 04:39:17,812][__main__][INFO] - Starting iteration 444. [2025-11-13 04:39:18,308][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 44 and human policies 1. [2025-11-13 04:39:18,308][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 04:39:49,339][__main__][INFO] - Number of regex retries in iteration 444: 0 [2025-11-13 04:39:49,340][__main__][INFO] - agents played in iteration 444 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 04:39:50,192][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:39:50,217][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:39:50,242][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:39:50,264][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:39:50,265][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 04:39:50,266][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 04:39:51,052][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 04:39:51,518][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 04:39:52,025][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 04:39:52,539][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 04:39:53,039][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 04:39:53,539][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 04:39:54,055][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 04:39:54,562][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 04:39:55,064][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 04:39:55,571][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 04:39:56,075][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 04:39:56,579][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 04:39:57,081][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 04:39:57,582][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 04:39:58,083][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 04:39:58,586][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 04:39:59,086][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 04:39:59,586][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 04:40:00,087][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 04:40:00,588][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 04:40:01,088][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 04:40:01,589][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 04:40:07,693][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 04:40:08,200][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 04:40:08,708][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 04:40:09,215][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 04:40:09,746][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 04:40:10,254][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 04:40:10,761][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 04:40:11,266][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 04:40:11,778][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 04:40:12,288][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 04:40:12,803][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 04:40:13,313][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 04:40:13,824][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 04:40:14,331][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 04:40:14,839][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 04:40:15,350][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 04:40:15,862][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 04:40:16,371][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 04:40:16,883][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 04:40:17,392][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 04:40:17,903][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 04:40:18,414][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 04:40:18,934][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 04:40:19,444][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 04:40:19,949][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 04:40:20,450][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 04:40:20,957][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 04:40:21,460][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 04:40:21,961][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 04:40:22,459][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 04:40:22,963][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 04:40:23,462][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10878 tokens. [2025-11-13 04:40:24,266][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.16%, Current % of VRAM taken: 59.61%, Block Peak % of device VRAM: 62.39%, ΔTime: 00:00:33 [2025-11-13 04:40:25,031][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 04:40:25,032][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 04:40:25,034][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 04:40:26,082][__main__][INFO] - Iteration 445 took 1m 7s (45.79% Gen, 52.67% Train). Generation: 31s, Training: 35s. Estimated remaining time: 49h 58m 2s. Estimated total time: 56h 28m 46s. Time estimates for 10 more iterations: 11m 17s, 100 more iterations: 1h 52m 57s, 500 more iterations: 9h 24m 47s. [2025-11-13 04:40:26,084][__main__][INFO] - Starting iteration 445. [2025-11-13 04:40:26,586][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 44 and human policies 1. [2025-11-13 04:40:26,586][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 04:40:42,408][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:40:47,063][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:40:47,862][__main__][INFO] - Number of regex retries in iteration 445: 2 [2025-11-13 04:40:47,862][__main__][INFO] - agents played in iteration 445 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 04:40:48,648][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:40:48,675][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:40:48,702][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:40:48,725][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:40:48,725][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 04:40:48,726][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 04:40:49,437][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 04:40:49,896][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 04:40:50,406][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 04:40:50,912][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 04:40:51,416][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 04:40:51,923][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 04:40:52,426][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 04:40:52,929][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 04:40:53,445][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 04:40:53,949][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 04:40:54,452][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 04:40:54,958][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 04:40:55,465][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 04:40:55,971][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 04:40:56,473][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 04:40:56,974][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 04:40:57,476][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 04:40:57,979][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 04:40:58,482][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 04:40:58,981][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 04:40:59,480][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 04:40:59,984][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 04:41:00,487][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 04:41:00,988][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 04:41:01,492][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 04:41:01,994][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 04:41:02,495][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 04:41:02,997][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 04:41:03,500][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 04:41:04,003][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 04:41:04,504][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 04:41:05,005][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 04:41:05,507][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 04:41:06,008][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 04:41:06,509][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 04:41:07,009][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 04:41:07,509][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 04:41:08,015][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 04:41:08,522][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 04:41:09,028][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 04:41:09,537][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 04:41:10,052][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 04:41:10,571][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 04:41:11,078][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 04:41:12,228][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 04:41:13,264][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 04:41:13,789][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 04:41:14,301][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 04:41:14,810][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 04:41:15,322][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 04:41:15,843][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 04:41:16,355][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 04:41:16,869][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 04:41:17,381][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 04:41:17,892][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 04:41:18,410][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 04:41:18,920][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 04:41:19,425][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 04:41:19,932][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 04:41:20,441][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 04:41:20,964][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 04:41:21,475][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 04:41:21,983][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 04:41:22,494][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 04:41:23,000][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10875 tokens. [2025-11-13 04:41:23,814][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.08%, Current % of VRAM taken: 59.53%, Block Peak % of device VRAM: 62.31%, ΔTime: 00:00:34 [2025-11-13 04:41:24,470][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 04:41:24,472][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 04:41:24,474][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 04:41:25,462][__main__][INFO] - Iteration 446 took 58s (36.13% Gen, 62.18% Train). Generation: 21s, Training: 36s. Estimated remaining time: 42h 32m 9s. Estimated total time: 49h 3m 52s. Time estimates for 10 more iterations: 9m 48s, 100 more iterations: 1h 38m 7s, 500 more iterations: 8h 10m 38s. [2025-11-13 04:41:25,467][__main__][INFO] - Starting iteration 446. [2025-11-13 04:41:25,980][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 44 and human policies 1. [2025-11-13 04:41:25,980][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 04:41:45,527][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:41:47,342][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:41:47,503][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:41:49,456][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:41:53,410][__main__][INFO] - Number of regex retries in iteration 446: 4 [2025-11-13 04:41:53,411][__main__][INFO] - agents played in iteration 446 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 04:41:54,234][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:41:54,257][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:41:54,282][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:41:54,304][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:41:54,305][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 04:41:54,306][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 04:41:55,032][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 04:41:55,489][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 04:41:56,108][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 04:41:56,609][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 04:41:57,111][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 04:41:57,616][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 04:41:58,119][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 04:41:58,624][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 04:41:59,137][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 04:41:59,645][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 04:42:00,151][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 04:42:00,650][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 04:42:01,151][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 04:42:01,652][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 04:42:02,156][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 04:42:02,656][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 04:42:03,161][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 04:42:03,664][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 04:42:04,181][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 04:42:04,682][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 04:42:05,184][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 04:42:05,683][mllm.training.trainer_common][INFO] - 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Processing mini-batch 43 of 64 [2025-11-13 04:42:17,214][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 04:42:17,717][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 04:42:18,227][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 04:42:18,734][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 04:42:19,246][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 04:42:19,767][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 04:42:20,274][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 04:42:20,788][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 04:42:21,292][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 04:42:21,800][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 04:42:22,312][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 04:42:22,818][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 04:42:23,322][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 04:42:23,843][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 04:42:24,351][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 04:42:24,863][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 04:42:25,371][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 04:42:25,885][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 04:42:26,398][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 04:42:26,906][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 04:42:27,416][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10751 tokens. [2025-11-13 04:42:28,147][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.19%, Current % of VRAM taken: 59.65%, Block Peak % of device VRAM: 62.24%, ΔTime: 00:00:33 [2025-11-13 04:42:28,922][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 04:42:28,923][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 04:42:28,926][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 04:42:30,035][__main__][INFO] - Iteration 447 took 1m 4s (42.82% Gen, 55.44% Train). Generation: 27s, Training: 35s. Estimated remaining time: 46h 50m 0s. Estimated total time: 53h 22m 48s. Time estimates for 10 more iterations: 10m 40s, 100 more iterations: 1h 46m 45s, 500 more iterations: 8h 53m 48s. [2025-11-13 04:42:30,037][__main__][INFO] - Starting iteration 447. [2025-11-13 04:42:30,547][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 44 and human policies 1. [2025-11-13 04:42:30,547][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 04:42:41,663][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:42:41,803][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:42:43,965][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:42:44,230][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:42:52,852][__main__][INFO] - Number of regex retries in iteration 447: 4 [2025-11-13 04:42:52,852][__main__][INFO] - agents played in iteration 447 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 04:42:53,646][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:42:53,671][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:42:53,708][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:42:53,734][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:42:53,735][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 04:42:53,736][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 04:42:54,439][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 04:42:54,899][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 04:42:55,411][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 04:42:55,911][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 04:42:56,418][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 04:42:56,924][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 04:42:57,426][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 04:42:57,931][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 04:42:58,435][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 04:42:58,941][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 04:42:59,444][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 04:42:59,946][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 04:43:00,450][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 04:43:00,954][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 04:43:01,458][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 04:43:01,972][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 04:43:02,476][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 04:43:02,988][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 04:43:03,492][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 04:43:03,996][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 04:43:04,502][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 04:43:05,005][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 04:43:11,041][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 04:43:11,543][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 04:43:12,045][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 04:43:12,546][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 04:43:13,048][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 04:43:13,550][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 04:43:14,053][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 04:43:14,556][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 04:43:15,071][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 04:43:15,571][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 04:43:16,084][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 04:43:16,585][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 04:43:17,084][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 04:43:17,601][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 04:43:18,101][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 04:43:18,604][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 04:43:19,105][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 04:43:19,605][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 04:43:20,111][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 04:43:20,611][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 04:43:21,111][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 04:43:21,611][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 04:43:22,113][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 04:43:22,620][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 04:43:23,126][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 04:43:23,632][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 04:43:24,139][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 04:43:24,644][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 04:43:25,149][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 04:43:25,665][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 04:43:26,174][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 04:43:26,705][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10853 tokens. [2025-11-13 04:43:27,499][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.25%, Current % of VRAM taken: 59.70%, Block Peak % of device VRAM: 62.30%, ΔTime: 00:00:33 [2025-11-13 04:43:28,259][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 04:43:28,260][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 04:43:28,262][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 04:43:30,465][__main__][INFO] - Iteration 448 took 59s (37.22% Gen, 59.10% Train). Generation: 22s, Training: 35s. Estimated remaining time: 43h 22m 9s. Estimated total time: 49h 55m 58s. Time estimates for 10 more iterations: 9m 59s, 100 more iterations: 1h 39m 51s, 500 more iterations: 8h 19m 19s. [2025-11-13 04:43:30,473][__main__][INFO] - Starting iteration 448. [2025-11-13 04:43:30,974][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 44 and human policies 1. [2025-11-13 04:43:30,974][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 04:44:01,468][__main__][INFO] - Number of regex retries in iteration 448: 0 [2025-11-13 04:44:01,469][__main__][INFO] - agents played in iteration 448 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 04:44:02,314][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:44:02,340][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:44:02,366][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:44:02,389][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:44:02,389][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 04:44:02,391][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 04:44:03,089][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 04:44:03,548][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 04:44:04,056][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 04:44:04,559][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 04:44:05,074][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 04:44:05,576][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 04:44:06,080][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 04:44:06,598][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 04:44:07,101][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 04:44:07,606][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 04:44:08,110][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 04:44:08,616][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 04:44:09,127][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 04:44:09,631][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 04:44:10,136][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 04:44:10,642][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 04:44:11,149][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 04:44:11,657][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 04:44:12,162][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 04:44:12,665][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 04:44:13,174][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 04:44:13,677][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 04:44:19,765][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 04:44:20,265][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 04:44:20,767][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 04:44:21,270][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 04:44:21,772][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 04:44:22,283][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 04:44:22,784][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 04:44:23,288][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 04:44:23,814][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 04:44:24,319][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 04:44:24,824][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 04:44:25,327][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 04:44:25,828][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 04:44:26,334][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 04:44:26,845][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 04:44:27,353][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 04:44:27,863][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 04:44:28,372][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 04:44:28,893][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 04:44:29,401][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 04:44:29,909][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 04:44:30,417][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 04:44:30,926][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 04:44:31,437][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 04:44:31,947][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 04:44:32,456][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 04:44:32,966][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 04:44:33,476][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 04:44:33,993][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 04:44:34,502][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 04:44:35,011][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 04:44:35,530][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10880 tokens. [2025-11-13 04:44:36,318][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.29%, Current % of VRAM taken: 59.74%, Block Peak % of device VRAM: 62.52%, ΔTime: 00:00:33 [2025-11-13 04:44:36,975][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 04:44:36,977][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 04:44:36,980][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 04:44:38,165][__main__][INFO] - Iteration 449 took 1m 7s (45.38% Gen, 52.85% Train). Generation: 30s, Training: 35s. Estimated remaining time: 49h 24m 38s. Estimated total time: 55h 59m 34s. Time estimates for 10 more iterations: 11m 11s, 100 more iterations: 1h 51m 59s, 500 more iterations: 9h 19m 55s. [2025-11-13 04:44:38,167][__main__][INFO] - Starting iteration 449. [2025-11-13 04:44:38,673][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 44 and human policies 1. [2025-11-13 04:44:38,673][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 04:44:47,878][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:44:48,997][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:44:57,540][__main__][INFO] - Number of regex retries in iteration 449: 2 [2025-11-13 04:44:57,540][__main__][INFO] - agents played in iteration 449 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 04:44:58,463][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.62%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:44:58,486][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.62%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:44:58,509][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.62%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:44:58,532][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.62%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:44:58,533][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 04:44:58,534][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 04:44:59,299][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 04:44:59,766][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 04:45:00,277][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 04:45:00,781][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 04:45:01,285][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 04:45:01,798][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 04:45:02,300][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 04:45:02,818][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 04:45:03,324][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 04:45:03,828][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 04:45:04,333][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 04:45:15,931][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 04:45:16,433][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 04:45:16,931][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 04:45:17,442][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 04:45:17,943][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 04:45:18,448][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 04:45:18,949][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 04:45:19,450][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 04:45:19,952][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 04:45:20,452][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 04:45:20,953][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 04:45:21,454][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 04:45:21,955][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 04:45:22,456][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 04:45:22,956][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 04:45:23,457][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 04:45:23,961][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 04:45:24,464][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 04:45:24,966][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 04:45:25,469][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 04:45:25,970][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 04:45:26,473][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 04:45:26,974][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 04:45:27,476][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 04:45:27,981][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 04:45:28,487][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 04:45:28,989][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 04:45:29,494][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 04:45:29,999][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 04:45:30,526][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 04:45:31,030][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 04:45:31,542][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10878 tokens. [2025-11-13 04:45:32,314][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.20%, Current % of VRAM taken: 59.65%, Block Peak % of device VRAM: 62.35%, ΔTime: 00:00:33 [2025-11-13 04:45:33,058][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 04:45:33,060][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 04:45:33,062][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 04:45:34,080][__main__][INFO] - Iteration 450 took 55s (34.05% Gen, 64.11% Train). Generation: 18s, Training: 35s. Estimated remaining time: 39h 34m 31s. Estimated total time: 46h 10m 23s. Time estimates for 10 more iterations: 9m 14s, 100 more iterations: 1h 32m 20s, 500 more iterations: 7h 41m 43s. [2025-11-13 04:45:34,082][__main__][INFO] - Starting iteration 450. [2025-11-13 04:45:34,590][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 44 and human policies 1. [2025-11-13 04:45:34,591][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 04:45:49,859][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:45:54,793][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:45:59,668][__main__][INFO] - Number of regex retries in iteration 450: 2 [2025-11-13 04:45:59,669][__main__][INFO] - agents played in iteration 450 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 04:46:00,549][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:46:00,578][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:46:00,602][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:46:00,626][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:46:00,627][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 04:46:00,628][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 04:46:01,428][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 04:46:01,896][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 04:46:02,411][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 04:46:02,920][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 04:46:03,445][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 04:46:03,954][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 04:46:04,463][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 04:46:04,972][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 04:46:05,486][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 04:46:05,998][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 04:46:06,501][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 04:46:07,005][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 04:46:07,527][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 04:46:08,028][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 04:46:08,531][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 04:46:09,032][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 04:46:09,534][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 04:46:10,047][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 04:46:10,549][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 04:46:11,050][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 04:46:11,556][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 04:46:12,061][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 04:46:18,147][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 04:46:18,654][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 04:46:19,159][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 04:46:19,659][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 04:46:20,160][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 04:46:20,665][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 04:46:21,166][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 04:46:21,667][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 04:46:22,165][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 04:46:22,663][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 04:46:23,162][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 04:46:23,661][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 04:46:24,159][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 04:46:24,660][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 04:46:25,157][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 04:46:25,655][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 04:46:26,155][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 04:46:26,657][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 04:46:27,159][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 04:46:27,661][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 04:46:28,162][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 04:46:28,666][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 04:46:29,173][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 04:46:29,679][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 04:46:30,185][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 04:46:30,690][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 04:46:31,208][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 04:46:31,717][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 04:46:32,222][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 04:46:32,733][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 04:46:33,242][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 04:46:33,761][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10876 tokens. [2025-11-13 04:46:34,577][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.19%, Current % of VRAM taken: 59.64%, Block Peak % of device VRAM: 62.28%, ΔTime: 00:00:33 [2025-11-13 04:46:35,215][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 04:46:35,216][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 04:46:35,219][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 04:46:37,072][__main__][INFO] - Iteration 451 took 1m 2s (40.13% Gen, 56.90% Train). Generation: 25s, Training: 35s. Estimated remaining time: 45h 27m 10s. Estimated total time: 52h 4m 5s. Time estimates for 10 more iterations: 10m 24s, 100 more iterations: 1h 44m 8s, 500 more iterations: 8h 40m 40s. [2025-11-13 04:46:37,073][__main__][INFO] - Starting iteration 451. [2025-11-13 04:46:37,575][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 45 and human policies 1. [2025-11-13 04:46:37,576][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 04:47:00,438][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:47:03,705][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:47:07,139][__main__][INFO] - Number of regex retries in iteration 451: 2 [2025-11-13 04:47:07,139][__main__][INFO] - agents played in iteration 451 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 04:47:08,014][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:47:08,043][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:47:08,071][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:47:08,095][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:47:08,096][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 04:47:08,098][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 04:47:08,872][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 04:47:09,342][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 04:47:09,856][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 04:47:10,361][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 04:47:10,865][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 04:47:11,375][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 04:47:11,878][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 04:47:12,381][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 04:47:12,885][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 04:47:13,391][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 04:47:13,899][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 04:47:14,402][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 04:47:14,907][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 04:47:15,413][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 04:47:15,919][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 04:47:16,439][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 04:47:16,947][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 04:47:17,451][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 04:47:17,953][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 04:47:18,455][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 04:47:18,963][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 04:47:19,467][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 04:47:19,970][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 04:47:20,475][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 04:47:20,981][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 04:47:21,489][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 04:47:21,995][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 04:47:22,500][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 04:47:23,010][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 04:47:23,515][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 04:47:24,017][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 04:47:24,527][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 04:47:25,029][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 04:47:25,541][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 04:47:26,045][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 04:47:26,548][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 04:47:27,058][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 04:47:27,561][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 04:47:28,065][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 04:47:28,572][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 04:47:29,075][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 04:47:29,578][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 04:47:30,080][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 04:47:30,581][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 04:47:31,084][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 04:47:31,590][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 04:47:32,094][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 04:47:32,595][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 04:47:33,096][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 04:47:33,603][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 04:47:34,107][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 04:47:34,609][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 04:47:35,111][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 04:47:35,612][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 04:47:36,138][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 04:47:36,640][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 04:47:37,141][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 04:47:37,653][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 04:47:38,154][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 04:47:38,659][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 04:47:39,163][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 04:47:39,668][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 04:47:40,178][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 04:47:40,681][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 04:47:41,186][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10873 tokens. [2025-11-13 04:47:41,977][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.19%, Current % of VRAM taken: 59.64%, Block Peak % of device VRAM: 62.41%, ΔTime: 00:00:33 [2025-11-13 04:47:42,733][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 04:47:42,735][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 04:47:42,739][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 04:47:43,773][__main__][INFO] - Iteration 452 took 1m 6s (44.66% Gen, 53.78% Train). Generation: 29s, Training: 35s. Estimated remaining time: 48h 31m 54s. Estimated total time: 55h 9m 56s. Time estimates for 10 more iterations: 11m 1s, 100 more iterations: 1h 50m 19s, 500 more iterations: 9h 11m 39s. [2025-11-13 04:47:43,776][__main__][INFO] - Starting iteration 452. [2025-11-13 04:47:44,263][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 45 and human policies 1. [2025-11-13 04:47:44,264][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 04:48:00,814][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:48:05,457][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:48:11,183][__main__][INFO] - Number of regex retries in iteration 452: 2 [2025-11-13 04:48:11,184][__main__][INFO] - agents played in iteration 452 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 04:48:12,074][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:48:12,097][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:48:12,121][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:48:12,144][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:48:12,145][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 04:48:12,145][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 04:48:12,928][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 04:48:13,397][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 04:48:13,911][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 04:48:14,444][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 04:48:14,955][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 04:48:15,466][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 04:48:15,974][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 04:48:16,485][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 04:48:16,999][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 04:48:17,509][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 04:48:18,023][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 04:48:18,538][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 04:48:19,048][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 04:48:19,560][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 04:48:20,075][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 04:48:20,586][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 04:48:21,099][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 04:48:21,607][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 04:48:22,122][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 04:48:22,630][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 04:48:23,135][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 04:48:23,641][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 04:48:24,147][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 04:48:24,650][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 04:48:25,157][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 04:48:25,661][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 04:48:26,166][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 04:48:26,671][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 04:48:27,177][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 04:48:27,681][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 04:48:28,185][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 04:48:28,689][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 04:48:29,192][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 04:48:29,695][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 04:48:30,198][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 04:48:30,701][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 04:48:31,205][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 04:48:31,711][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 04:48:32,214][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 04:48:32,718][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 04:48:33,223][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 04:48:33,726][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 04:48:34,231][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 04:48:34,736][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 04:48:35,256][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 04:48:35,761][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 04:48:36,273][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 04:48:36,777][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 04:48:37,279][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 04:48:37,784][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 04:48:38,288][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 04:48:38,794][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 04:48:39,296][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 04:48:39,798][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 04:48:40,300][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 04:48:40,802][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 04:48:41,304][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 04:48:41,818][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 04:48:42,320][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 04:48:42,823][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 04:48:43,335][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 04:48:43,837][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 04:48:44,351][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 04:48:44,857][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 04:48:45,364][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10877 tokens. [2025-11-13 04:48:46,146][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.30%, Current % of VRAM taken: 59.75%, Block Peak % of device VRAM: 62.42%, ΔTime: 00:00:33 [2025-11-13 04:48:46,794][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 04:48:46,797][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 04:48:46,800][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 04:48:47,768][__main__][INFO] - Iteration 453 took 1m 3s (42.39% Gen, 56.08% Train). Generation: 26s, Training: 35s. Estimated remaining time: 46h 16m 13s. Estimated total time: 52h 55m 19s. Time estimates for 10 more iterations: 10m 35s, 100 more iterations: 1h 45m 50s, 500 more iterations: 8h 49m 13s. [2025-11-13 04:48:47,771][__main__][INFO] - Starting iteration 453. [2025-11-13 04:48:48,279][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 45 and human policies 1. [2025-11-13 04:48:48,279][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 04:49:15,511][__main__][INFO] - Number of regex retries in iteration 453: 0 [2025-11-13 04:49:15,512][__main__][INFO] - agents played in iteration 453 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 04:49:16,375][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:49:16,397][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:49:16,420][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:49:16,443][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:49:16,443][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 04:49:16,445][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 04:49:17,237][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 04:49:17,705][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 04:49:18,214][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 04:49:18,718][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 04:49:19,222][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 04:49:19,729][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 04:49:20,254][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 04:49:20,756][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 04:49:21,259][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 04:49:21,767][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 04:49:22,271][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 04:49:22,774][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 04:49:23,276][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 04:49:23,781][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 04:49:24,285][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 04:49:24,788][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 04:49:25,295][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 04:49:25,799][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 04:49:26,304][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 04:49:26,811][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 04:49:27,315][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 04:49:27,820][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 04:49:28,339][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 04:49:28,846][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 04:49:29,359][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 04:49:29,867][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 04:49:30,374][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 04:49:30,878][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 04:49:31,382][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 04:49:31,887][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 04:49:32,396][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 04:49:32,903][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 04:49:33,407][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 04:49:33,908][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 04:49:34,408][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 04:49:34,912][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 04:49:35,412][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 04:49:35,912][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 04:49:36,411][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 04:49:36,911][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 04:49:37,412][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 04:49:37,911][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 04:49:38,412][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 04:49:38,916][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 04:49:39,419][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 04:49:39,921][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 04:49:40,436][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 04:49:40,940][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 04:49:41,460][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 04:49:41,962][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 04:49:42,464][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 04:49:42,967][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 04:49:43,471][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 04:49:43,979][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 04:49:44,483][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 04:49:44,988][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 04:49:45,496][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 04:49:46,000][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 04:49:46,504][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 04:49:47,006][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 04:49:47,514][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 04:49:48,025][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 04:49:48,534][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 04:49:49,039][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 04:49:49,551][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10869 tokens. [2025-11-13 04:49:50,355][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.18%, Current % of VRAM taken: 59.63%, Block Peak % of device VRAM: 62.28%, ΔTime: 00:00:33 [2025-11-13 04:49:51,136][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 04:49:51,137][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 04:49:51,139][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 04:49:52,128][__main__][INFO] - Iteration 454 took 1m 3s (42.65% Gen, 55.80% Train). Generation: 27s, Training: 35s. Estimated remaining time: 46h 32m 21s. Estimated total time: 53h 12m 31s. Time estimates for 10 more iterations: 10m 38s, 100 more iterations: 1h 46m 25s, 500 more iterations: 8h 52m 5s. [2025-11-13 04:49:52,131][__main__][INFO] - Starting iteration 454. [2025-11-13 04:49:52,633][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 45 and human policies 1. [2025-11-13 04:49:52,633][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 04:50:06,158][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:50:07,316][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:50:08,524][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:50:13,480][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:50:17,450][__main__][INFO] - Number of regex retries in iteration 454: 4 [2025-11-13 04:50:17,451][__main__][INFO] - agents played in iteration 454 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 04:50:18,355][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:50:18,383][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:50:18,408][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:50:18,431][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:50:18,432][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 04:50:18,434][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 04:50:19,213][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 04:50:19,684][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 04:50:20,198][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 04:50:20,716][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 04:50:21,227][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 04:50:21,739][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 04:50:22,252][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 04:50:22,762][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 04:50:23,276][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 04:50:23,788][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 04:50:24,302][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 04:50:24,811][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 04:50:25,320][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 04:50:25,828][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 04:50:26,337][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 04:50:26,845][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 04:50:27,375][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 04:50:27,885][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 04:50:28,395][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 04:50:28,905][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 04:50:29,413][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 04:50:29,920][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 04:50:36,002][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 04:50:36,505][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 04:50:37,010][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 04:50:37,516][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 04:50:38,025][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 04:50:38,528][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 04:50:39,032][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 04:50:39,546][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 04:50:40,048][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 04:50:40,560][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 04:50:41,064][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 04:50:41,566][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 04:50:42,071][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 04:50:42,573][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 04:50:43,075][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 04:50:43,580][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 04:50:44,086][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 04:50:44,591][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 04:50:45,093][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 04:50:45,594][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 04:50:46,095][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 04:50:46,595][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 04:50:47,099][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 04:50:47,599][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 04:50:48,102][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 04:50:48,608][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 04:50:49,117][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 04:50:49,624][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 04:50:50,128][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 04:50:50,633][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 04:50:51,171][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 04:50:51,681][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10877 tokens. [2025-11-13 04:50:52,525][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.31%, Current % of VRAM taken: 59.76%, Block Peak % of device VRAM: 62.52%, ΔTime: 00:00:33 [2025-11-13 04:50:53,178][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 04:50:53,180][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 04:50:53,181][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 04:50:54,173][__main__][INFO] - Iteration 455 took 1m 1s (40.33% Gen, 58.06% Train). Generation: 24s, Training: 35s. Estimated remaining time: 44h 35m 50s. Estimated total time: 51h 17m 2s. Time estimates for 10 more iterations: 10m 15s, 100 more iterations: 1h 42m 34s, 500 more iterations: 8h 32m 50s. [2025-11-13 04:50:54,175][__main__][INFO] - Starting iteration 455. [2025-11-13 04:50:54,676][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 45 and human policies 1. [2025-11-13 04:50:54,676][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 04:51:17,537][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:51:24,270][__main__][INFO] - Number of regex retries in iteration 455: 1 [2025-11-13 04:51:24,271][__main__][INFO] - agents played in iteration 455 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 04:51:25,148][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.62%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:51:25,176][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.62%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:51:25,201][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.62%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:51:25,223][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.62%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:51:25,224][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 04:51:25,224][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 04:51:26,028][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 04:51:26,495][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 04:51:27,011][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 04:51:27,520][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 04:51:28,033][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 04:51:28,544][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 04:51:29,049][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 04:51:29,559][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 04:51:30,068][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 04:51:30,589][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 04:51:31,097][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 04:51:42,744][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 04:51:43,246][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 04:51:43,759][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 04:51:44,262][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 04:51:44,777][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 04:51:45,279][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 04:51:45,781][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 04:51:46,286][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 04:51:46,788][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 04:51:47,294][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 04:51:47,796][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 04:51:48,299][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 04:51:48,806][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 04:51:49,313][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 04:51:49,818][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 04:51:50,323][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 04:51:50,828][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 04:51:51,331][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 04:51:51,832][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 04:51:52,332][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 04:51:52,836][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 04:51:53,336][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 04:51:53,835][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 04:51:54,335][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 04:51:54,837][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 04:51:55,340][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 04:51:55,842][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 04:51:56,344][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 04:51:56,845][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 04:51:57,347][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 04:51:57,851][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 04:51:58,352][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10879 tokens. [2025-11-13 04:51:59,077][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.21%, Current % of VRAM taken: 59.67%, Block Peak % of device VRAM: 62.31%, ΔTime: 00:00:33 [2025-11-13 04:51:59,809][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 04:51:59,811][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 04:51:59,812][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 04:52:00,799][__main__][INFO] - Iteration 456 took 1m 6s (44.76% Gen, 53.75% Train). Generation: 29s, Training: 35s. Estimated remaining time: 48h 23m 52s. Estimated total time: 55h 6m 11s. Time estimates for 10 more iterations: 11m 1s, 100 more iterations: 1h 50m 12s, 500 more iterations: 9h 11m 1s. [2025-11-13 04:52:00,801][__main__][INFO] - Starting iteration 456. [2025-11-13 04:52:01,277][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 45 and human policies 1. [2025-11-13 04:52:01,278][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 04:52:13,842][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:52:16,308][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:52:25,707][__main__][INFO] - Number of regex retries in iteration 456: 2 [2025-11-13 04:52:25,708][__main__][INFO] - agents played in iteration 456 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 04:52:26,602][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:52:26,647][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:52:26,683][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:52:26,722][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:52:26,723][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 04:52:26,724][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 04:52:27,525][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 04:52:27,993][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 04:52:28,511][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 04:52:29,022][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 04:52:29,538][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 04:52:30,051][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 04:52:30,566][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 04:52:31,098][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 04:52:31,611][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 04:52:32,127][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 04:52:32,638][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 04:52:33,148][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 04:52:33,666][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 04:52:34,174][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 04:52:34,686][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 04:52:35,195][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 04:52:35,703][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 04:52:36,213][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 04:52:36,719][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 04:52:37,224][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 04:52:37,732][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 04:52:38,241][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 04:52:38,752][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 04:52:39,259][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 04:52:39,767][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 04:52:40,284][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 04:52:40,794][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 04:52:41,310][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 04:52:41,823][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 04:52:42,332][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 04:52:42,841][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 04:52:43,348][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 04:52:43,862][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 04:52:44,371][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 04:52:44,878][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 04:52:45,399][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 04:52:45,905][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 04:52:46,416][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 04:52:46,920][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 04:52:47,431][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 04:52:47,939][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 04:52:48,440][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 04:52:48,940][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 04:52:49,447][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 04:52:49,952][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 04:52:50,454][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 04:52:50,956][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 04:52:51,461][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 04:52:51,965][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 04:52:52,466][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 04:52:52,969][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 04:52:53,480][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 04:52:53,983][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 04:52:54,495][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 04:52:54,996][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 04:52:55,499][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 04:52:56,001][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 04:52:56,507][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 04:52:57,009][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 04:52:57,513][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 04:52:58,014][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 04:52:58,517][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 04:52:59,021][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 04:52:59,526][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 04:53:00,031][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10874 tokens. [2025-11-13 04:53:00,797][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.20%, Current % of VRAM taken: 59.65%, Block Peak % of device VRAM: 62.31%, ΔTime: 00:00:33 [2025-11-13 04:53:01,445][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 04:53:01,446][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 04:53:01,448][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 04:53:02,447][__main__][INFO] - Iteration 457 took 1m 1s (39.94% Gen, 58.43% Train). Generation: 24s, Training: 35s. Estimated remaining time: 44h 15m 11s. Estimated total time: 50h 58m 31s. Time estimates for 10 more iterations: 10m 11s, 100 more iterations: 1h 41m 57s, 500 more iterations: 8h 29m 45s. [2025-11-13 04:53:02,449][__main__][INFO] - Starting iteration 457. [2025-11-13 04:53:02,937][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 45 and human policies 1. [2025-11-13 04:53:02,938][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 04:53:29,976][__main__][INFO] - Number of regex retries in iteration 457: 0 [2025-11-13 04:53:29,977][__main__][INFO] - agents played in iteration 457 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 04:53:30,818][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:53:30,841][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:53:30,864][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:53:30,887][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:53:30,888][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 04:53:30,889][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 04:53:31,665][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 04:53:32,126][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 04:53:32,640][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 04:53:33,149][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 04:53:33,657][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 04:53:34,171][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 04:53:34,680][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 04:53:35,191][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 04:53:35,713][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 04:53:36,224][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 04:53:36,734][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 04:53:37,242][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 04:53:37,749][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 04:53:38,256][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 04:53:38,765][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 04:53:39,277][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 04:53:39,797][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 04:53:40,308][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 04:53:40,817][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 04:53:41,323][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 04:53:41,831][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 04:53:42,339][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 04:53:42,848][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 04:53:43,354][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 04:53:43,860][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 04:53:44,368][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 04:53:44,877][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 04:53:45,382][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 04:53:45,889][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 04:53:46,404][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 04:53:46,913][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 04:53:47,427][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 04:53:47,932][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 04:53:48,441][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 04:53:48,950][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 04:53:49,460][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 04:53:49,967][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 04:53:50,477][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 04:53:50,984][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 04:53:51,496][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 04:53:52,008][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 04:53:52,524][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 04:53:53,033][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 04:53:53,540][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 04:53:54,048][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 04:53:54,553][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 04:53:55,058][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 04:53:55,562][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 04:53:56,066][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 04:53:56,571][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 04:53:57,074][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 04:53:57,578][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 04:53:58,079][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 04:53:58,583][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 04:53:59,092][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 04:53:59,594][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 04:54:00,098][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 04:54:00,600][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 04:54:01,104][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 04:54:01,609][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 04:54:02,115][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 04:54:02,617][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 04:54:03,130][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 04:54:03,633][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 04:54:04,137][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10862 tokens. [2025-11-13 04:54:04,947][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.22%, Current % of VRAM taken: 59.68%, Block Peak % of device VRAM: 62.42%, ΔTime: 00:00:33 [2025-11-13 04:54:05,684][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 04:54:05,685][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 04:54:05,686][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 04:54:06,763][__main__][INFO] - Iteration 458 took 1m 3s (42.37% Gen, 55.95% Train). Generation: 27s, Training: 35s. Estimated remaining time: 46h 26m 53s. Estimated total time: 53h 11m 18s. Time estimates for 10 more iterations: 10m 38s, 100 more iterations: 1h 46m 22s, 500 more iterations: 8h 51m 53s. [2025-11-13 04:54:06,765][__main__][INFO] - Starting iteration 458. [2025-11-13 04:54:07,238][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 45 and human policies 1. [2025-11-13 04:54:07,238][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 04:54:24,209][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:54:25,239][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:54:31,617][mllm.models.large_language_model_local][WARNING] - Response Understanding that we both value different items, I will propose a split that captures the high value items while also considering the total item quantity. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:54:37,076][__main__][INFO] - Number of regex retries in iteration 458: 3 [2025-11-13 04:54:37,076][__main__][INFO] - agents played in iteration 458 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 04:54:37,974][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:54:38,014][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:54:38,045][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:54:38,071][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:54:38,072][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 04:54:38,073][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 04:54:38,857][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 04:54:39,326][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 04:54:39,850][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 04:54:40,360][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 04:54:40,868][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 04:54:41,379][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 04:54:41,890][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 04:54:42,413][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 04:54:42,922][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 04:54:43,434][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 04:54:43,948][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 04:54:44,457][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 04:54:44,970][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 04:54:45,478][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 04:54:45,987][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 04:54:46,503][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 04:54:47,017][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 04:54:47,529][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 04:54:48,045][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 04:54:48,558][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 04:54:49,071][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 04:54:49,583][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 04:54:55,769][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 04:54:56,277][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 04:54:56,789][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 04:54:57,296][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 04:54:57,808][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 04:54:58,317][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 04:54:58,826][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 04:54:59,341][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 04:54:59,851][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 04:55:00,359][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 04:55:00,869][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 04:55:01,378][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 04:55:01,889][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 04:55:02,402][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 04:55:02,908][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 04:55:03,417][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 04:55:03,923][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 04:55:04,433][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 04:55:04,952][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 04:55:05,464][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 04:55:05,967][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 04:55:06,473][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 04:55:06,975][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 04:55:07,482][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 04:55:07,986][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 04:55:08,488][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 04:55:08,989][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 04:55:09,494][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 04:55:10,008][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 04:55:10,512][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 04:55:11,015][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 04:55:11,520][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10876 tokens. [2025-11-13 04:55:12,276][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.19%, Current % of VRAM taken: 59.64%, Block Peak % of device VRAM: 62.28%, ΔTime: 00:00:33 [2025-11-13 04:55:12,931][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 04:55:12,933][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 04:55:12,934][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 04:55:13,839][__main__][INFO] - Iteration 459 took 1m 6s (44.80% Gen, 53.84% Train). Generation: 29s, Training: 35s. Estimated remaining time: 48h 44m 33s. Estimated total time: 55h 30m 5s. Time estimates for 10 more iterations: 11m 6s, 100 more iterations: 1h 51m 0s, 500 more iterations: 9h 15m 0s. [2025-11-13 04:55:13,841][__main__][INFO] - Starting iteration 459. [2025-11-13 04:55:14,322][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 45 and human policies 1. [2025-11-13 04:55:14,322][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 04:55:40,833][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:55:42,723][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:55:42,728][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:55:46,151][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:55:48,958][__main__][INFO] - Number of regex retries in iteration 459: 4 [2025-11-13 04:55:48,959][__main__][INFO] - agents played in iteration 459 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 04:55:49,815][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:55:49,838][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:55:49,863][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:55:49,885][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:55:49,886][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 04:55:49,886][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 04:55:50,612][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 04:55:51,076][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 04:55:51,590][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 04:55:52,099][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 04:55:52,614][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 04:55:53,121][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 04:55:53,626][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 04:55:54,136][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 04:55:54,657][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 04:55:55,170][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 04:55:55,676][mllm.training.trainer_common][INFO] - 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Processing mini-batch 21 of 64 [2025-11-13 04:56:01,800][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 04:56:02,311][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 04:56:02,822][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 04:56:03,337][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 04:56:03,851][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 04:56:04,362][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 04:56:04,878][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 04:56:05,390][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 04:56:05,902][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 04:56:06,413][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 04:56:06,926][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 04:56:07,435][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 04:56:07,950][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 04:56:08,456][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 04:56:08,968][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 04:56:09,471][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 04:56:09,973][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 04:56:10,475][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 04:56:10,975][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 04:56:11,473][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 04:56:11,973][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 04:56:12,473][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 04:56:12,975][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 04:56:13,475][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 04:56:13,975][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 04:56:14,475][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 04:56:14,976][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 04:56:15,480][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 04:56:15,982][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 04:56:16,483][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 04:56:16,986][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 04:56:17,489][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 04:56:17,997][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 04:56:18,502][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 04:56:19,002][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 04:56:19,507][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 04:56:20,011][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 04:56:20,518][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 04:56:21,032][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 04:56:21,533][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 04:56:22,051][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 04:56:22,555][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 04:56:23,057][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10877 tokens. [2025-11-13 04:56:23,839][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.23%, Current % of VRAM taken: 59.68%, Block Peak % of device VRAM: 62.28%, ΔTime: 00:00:33 [2025-11-13 04:56:24,582][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 04:56:24,584][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 04:56:24,586][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 04:56:25,646][__main__][INFO] - Iteration 460 took 1m 11s (48.56% Gen, 49.95% Train). Generation: 34s, Training: 35s. Estimated remaining time: 52h 39m 31s. Estimated total time: 59h 26m 15s. Time estimates for 10 more iterations: 11m 53s, 100 more iterations: 1h 58m 52s, 500 more iterations: 9h 54m 22s. [2025-11-13 04:56:25,648][__main__][INFO] - Starting iteration 460. [2025-11-13 04:56:26,160][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 45 and human policies 1. [2025-11-13 04:56:26,161][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 04:56:40,241][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:56:49,774][__main__][INFO] - Number of regex retries in iteration 460: 1 [2025-11-13 04:56:49,775][__main__][INFO] - agents played in iteration 460 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 04:56:50,609][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:56:50,636][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:56:50,660][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:56:50,683][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:56:50,684][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 04:56:50,684][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 04:56:51,465][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 04:56:51,931][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 04:56:52,460][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 04:56:52,971][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 04:56:53,479][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 04:56:53,993][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 04:56:54,502][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 04:56:55,013][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 04:56:55,524][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 04:56:56,036][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 04:56:56,549][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 04:56:57,060][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 04:56:57,567][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 04:56:58,079][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 04:56:58,591][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 04:56:59,104][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 04:56:59,616][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 04:57:00,130][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 04:57:00,645][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 04:57:01,156][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 04:57:01,673][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 04:57:02,187][mllm.training.trainer_common][INFO] - 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Processing mini-batch 43 of 64 [2025-11-13 04:57:13,953][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 04:57:14,467][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 04:57:14,973][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 04:57:15,475][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 04:57:15,985][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 04:57:16,485][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 04:57:16,987][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 04:57:17,490][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 04:57:17,990][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 04:57:18,491][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 04:57:18,992][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 04:57:19,492][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 04:57:19,994][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 04:57:20,494][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 04:57:20,995][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 04:57:21,497][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 04:57:21,998][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 04:57:22,502][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 04:57:23,007][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 04:57:23,508][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 04:57:24,012][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10877 tokens. [2025-11-13 04:57:24,797][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.21%, Current % of VRAM taken: 59.66%, Block Peak % of device VRAM: 62.50%, ΔTime: 00:00:33 [2025-11-13 04:57:25,444][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 04:57:25,446][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 04:57:25,448][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 04:57:27,378][__main__][INFO] - Iteration 461 took 1m 1s (38.57% Gen, 58.27% Train). Generation: 23s, Training: 35s. Estimated remaining time: 44h 13m 10s. Estimated total time: 51h 0m 55s. Time estimates for 10 more iterations: 10m 12s, 100 more iterations: 1h 42m 1s, 500 more iterations: 8h 30m 9s. [2025-11-13 04:57:27,380][__main__][INFO] - Starting iteration 461. [2025-11-13 04:57:27,874][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 46 and human policies 1. [2025-11-13 04:57:27,874][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 04:57:48,664][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:57:59,001][__main__][INFO] - Number of regex retries in iteration 461: 1 [2025-11-13 04:57:59,002][__main__][INFO] - agents played in iteration 461 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 04:57:59,836][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:57:59,859][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:57:59,883][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:57:59,906][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:57:59,907][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 04:57:59,908][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 04:58:00,641][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 04:58:01,109][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 04:58:01,621][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 04:58:02,125][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 04:58:02,632][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 04:58:03,137][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 04:58:03,647][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 04:58:04,157][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 04:58:04,668][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 04:58:05,182][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 04:58:05,693][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 04:58:17,402][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 04:58:17,904][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 04:58:18,405][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 04:58:18,907][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 04:58:19,412][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 04:58:19,916][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 04:58:20,416][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 04:58:20,918][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 04:58:21,418][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 04:58:21,918][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 04:58:22,418][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 04:58:22,918][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 04:58:23,422][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 04:58:23,924][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 04:58:24,426][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 04:58:24,927][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 04:58:25,430][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 04:58:25,934][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 04:58:26,438][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 04:58:26,941][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 04:58:27,441][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 04:58:27,942][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 04:58:28,444][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 04:58:28,949][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 04:58:29,454][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 04:58:29,971][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 04:58:30,476][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 04:58:30,977][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 04:58:31,481][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 04:58:31,983][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 04:58:32,493][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 04:58:32,994][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10880 tokens. [2025-11-13 04:58:33,793][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.18%, Current % of VRAM taken: 59.63%, Block Peak % of device VRAM: 62.28%, ΔTime: 00:00:33 [2025-11-13 04:58:34,543][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 04:58:34,544][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 04:58:34,546][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 04:58:35,731][__main__][INFO] - Iteration 462 took 1m 7s (45.87% Gen, 52.38% Train). Generation: 31s, Training: 35s. Estimated remaining time: 49h 43m 59s. Estimated total time: 56h 32m 53s. Time estimates for 10 more iterations: 11m 18s, 100 more iterations: 1h 53m 5s, 500 more iterations: 9h 25m 28s. [2025-11-13 04:58:35,733][__main__][INFO] - Starting iteration 462. [2025-11-13 04:58:36,227][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 46 and human policies 1. [2025-11-13 04:58:36,228][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 04:58:51,022][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:58:57,884][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 04:59:01,058][__main__][INFO] - Number of regex retries in iteration 462: 2 [2025-11-13 04:59:01,059][__main__][INFO] - agents played in iteration 462 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 04:59:01,930][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:59:01,953][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:59:01,977][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:59:02,000][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 04:59:02,001][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 04:59:02,001][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 04:59:02,759][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 04:59:03,226][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 04:59:03,739][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 04:59:04,254][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 04:59:04,765][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 04:59:05,275][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 04:59:05,791][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 04:59:06,297][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 04:59:06,803][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 04:59:07,308][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 04:59:07,819][mllm.training.trainer_common][INFO] - 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Processing mini-batch 54 of 64 [2025-11-13 04:59:30,758][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 04:59:31,260][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 04:59:31,766][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 04:59:32,268][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 04:59:32,771][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 04:59:33,277][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 04:59:33,781][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 04:59:34,285][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 04:59:34,788][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 04:59:35,293][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10861 tokens. [2025-11-13 04:59:36,044][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.26%, Current % of VRAM taken: 59.71%, Block Peak % of device VRAM: 62.49%, ΔTime: 00:00:33 [2025-11-13 04:59:36,659][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 04:59:36,661][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 04:59:36,663][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 04:59:37,573][__main__][INFO] - Iteration 463 took 1m 1s (40.48% Gen, 58.04% Train). Generation: 24s, Training: 35s. Estimated remaining time: 44h 17m 22s. Estimated total time: 51h 7m 18s. Time estimates for 10 more iterations: 10m 13s, 100 more iterations: 1h 42m 14s, 500 more iterations: 8h 31m 13s. [2025-11-13 04:59:37,575][__main__][INFO] - Starting iteration 463. [2025-11-13 04:59:38,070][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 46 and human policies 1. [2025-11-13 04:59:38,071][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 05:00:08,283][__main__][INFO] - Number of regex retries in iteration 463: 0 [2025-11-13 05:00:08,284][__main__][INFO] - agents played in iteration 463 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 05:00:09,101][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.62%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:00:09,129][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.62%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:00:09,155][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.62%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:00:09,178][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.62%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:00:09,179][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 05:00:09,179][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 05:00:09,876][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 05:00:10,332][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 05:00:10,839][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 05:00:11,341][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 05:00:11,843][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 05:00:12,345][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 05:00:12,857][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 05:00:13,359][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 05:00:13,862][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 05:00:14,369][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 05:00:14,872][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 05:00:26,602][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 05:00:27,115][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 05:00:27,625][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 05:00:28,136][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 05:00:28,648][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 05:00:29,160][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 05:00:29,673][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 05:00:30,185][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 05:00:30,691][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 05:00:31,202][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 05:00:31,705][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 05:00:32,209][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 05:00:32,709][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 05:00:33,211][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 05:00:33,714][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 05:00:34,214][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 05:00:34,716][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 05:00:35,219][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 05:00:35,724][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 05:00:36,227][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 05:00:36,729][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 05:00:37,232][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 05:00:37,739][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 05:00:38,241][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 05:00:38,743][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 05:00:39,247][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 05:00:39,750][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 05:00:40,253][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 05:00:40,759][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 05:00:41,263][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 05:00:41,780][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 05:00:42,283][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10868 tokens. [2025-11-13 05:00:43,054][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.20%, Current % of VRAM taken: 59.66%, Block Peak % of device VRAM: 62.34%, ΔTime: 00:00:33 [2025-11-13 05:00:43,796][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 05:00:43,798][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 05:00:43,803][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 05:00:44,893][__main__][INFO] - Iteration 464 took 1m 6s (45.21% Gen, 53.15% Train). Generation: 30s, Training: 35s. Estimated remaining time: 48h 50m 8s. Estimated total time: 55h 41m 12s. Time estimates for 10 more iterations: 11m 8s, 100 more iterations: 1h 51m 22s, 500 more iterations: 9h 16m 52s. [2025-11-13 05:00:44,895][__main__][INFO] - Starting iteration 464. [2025-11-13 05:00:45,382][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 46 and human policies 1. [2025-11-13 05:00:45,383][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 05:01:08,848][__main__][INFO] - Number of regex retries in iteration 464: 0 [2025-11-13 05:01:08,850][__main__][INFO] - agents played in iteration 464 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 05:01:09,731][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:01:09,754][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:01:09,777][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:01:09,800][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:01:09,800][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 05:01:09,801][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 05:01:10,575][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 05:01:11,043][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 05:01:11,555][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 05:01:12,067][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 05:01:12,575][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 05:01:13,082][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 05:01:13,597][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 05:01:14,104][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 05:01:14,617][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 05:01:15,132][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 05:01:15,639][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 05:01:16,147][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 05:01:16,655][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 05:01:17,163][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 05:01:17,673][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 05:01:18,180][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 05:01:18,687][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 05:01:19,197][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 05:01:19,706][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 05:01:20,219][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 05:01:20,732][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 05:01:21,250][mllm.training.trainer_common][INFO] - 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Processing mini-batch 43 of 64 [2025-11-13 05:01:32,993][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 05:01:33,505][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 05:01:34,013][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 05:01:34,522][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 05:01:35,032][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 05:01:35,538][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 05:01:36,053][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 05:01:36,562][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 05:01:37,074][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 05:01:37,584][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 05:01:38,093][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 05:01:38,620][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 05:01:39,133][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 05:01:39,641][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 05:01:40,153][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 05:01:40,663][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 05:01:41,173][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 05:01:41,683][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 05:01:42,194][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 05:01:42,722][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 05:01:43,232][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10873 tokens. [2025-11-13 05:01:44,049][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.25%, Current % of VRAM taken: 59.70%, Block Peak % of device VRAM: 62.31%, ΔTime: 00:00:33 [2025-11-13 05:01:44,698][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 05:01:44,700][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 05:01:44,701][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 05:01:45,590][__main__][INFO] - Iteration 465 took 1m 0s (38.98% Gen, 59.54% Train). Generation: 23s, Training: 35s. Estimated remaining time: 43h 18m 21s. Estimated total time: 50h 10m 25s. Time estimates for 10 more iterations: 10m 2s, 100 more iterations: 1h 40m 20s, 500 more iterations: 8h 21m 44s. [2025-11-13 05:01:45,593][__main__][INFO] - Starting iteration 465. [2025-11-13 05:01:46,118][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 46 and human policies 1. [2025-11-13 05:01:46,119][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 05:02:03,918][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:02:03,920][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:02:09,218][mllm.models.large_language_model_local][WARNING] - Response Given Alice's high value for books and balls, I should try to maximize my points by getting as many valuable items (books and balls) as possible. Here's my strategy: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:02:14,111][__main__][INFO] - Number of regex retries in iteration 465: 3 [2025-11-13 05:02:14,112][__main__][INFO] - agents played in iteration 465 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 05:02:14,949][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:02:14,975][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:02:15,000][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:02:15,023][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:02:15,024][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 05:02:15,024][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 05:02:15,813][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 05:02:16,276][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 05:02:16,800][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 05:02:17,307][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 05:02:17,814][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 05:02:18,325][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 05:02:18,831][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 05:02:19,344][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 05:02:19,853][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 05:02:20,360][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 05:02:20,872][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 05:02:21,380][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 05:02:21,889][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 05:02:22,396][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 05:02:22,905][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 05:02:23,430][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 05:02:23,936][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 05:02:24,445][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 05:02:24,955][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 05:02:25,458][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 05:02:25,959][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 05:02:26,460][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 05:02:32,498][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 05:02:33,008][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 05:02:33,511][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 05:02:34,022][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 05:02:34,526][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 05:02:35,029][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 05:02:35,535][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 05:02:36,039][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 05:02:36,549][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 05:02:37,058][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 05:02:37,567][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 05:02:38,078][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 05:02:38,591][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 05:02:39,099][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 05:02:39,610][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 05:02:40,114][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 05:02:40,635][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 05:02:41,142][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 05:02:41,649][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 05:02:42,159][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 05:02:42,664][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 05:02:43,170][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 05:02:43,680][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 05:02:44,189][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 05:02:44,730][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 05:02:45,245][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 05:02:45,757][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 05:02:46,268][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 05:02:46,778][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 05:02:47,284][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 05:02:47,787][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 05:02:48,289][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10874 tokens. [2025-11-13 05:02:49,054][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.29%, Current % of VRAM taken: 59.74%, Block Peak % of device VRAM: 62.39%, ΔTime: 00:00:33 [2025-11-13 05:02:49,817][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 05:02:49,818][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 05:02:49,820][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 05:02:50,913][__main__][INFO] - Iteration 466 took 1m 4s (43.20% Gen, 55.11% Train). Generation: 27s, Training: 35s. Estimated remaining time: 47h 6m 39s. Estimated total time: 53h 59m 49s. Time estimates for 10 more iterations: 10m 47s, 100 more iterations: 1h 47m 59s, 500 more iterations: 8h 59m 58s. [2025-11-13 05:02:50,915][__main__][INFO] - Starting iteration 466. [2025-11-13 05:02:51,406][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 46 and human policies 1. [2025-11-13 05:02:51,407][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 05:03:10,873][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:03:15,862][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:03:21,730][__main__][INFO] - Number of regex retries in iteration 466: 2 [2025-11-13 05:03:21,730][__main__][INFO] - agents played in iteration 466 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 05:03:22,593][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:03:22,620][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:03:22,647][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:03:22,671][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:03:22,672][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 05:03:22,673][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 05:03:23,497][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 05:03:23,969][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 05:03:24,485][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 05:03:24,994][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 05:03:25,503][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 05:03:26,011][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 05:03:26,543][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 05:03:27,052][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 05:03:27,562][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 05:03:28,075][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 05:03:28,585][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 05:03:29,094][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 05:03:29,603][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 05:03:30,112][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 05:03:30,620][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 05:03:31,125][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 05:03:31,636][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 05:03:32,154][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 05:03:32,667][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 05:03:33,177][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 05:03:33,687][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 05:03:34,198][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 05:03:34,715][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 05:03:35,225][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 05:03:35,736][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 05:03:36,246][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 05:03:36,754][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 05:03:37,264][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 05:03:37,774][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 05:03:38,283][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 05:03:38,793][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 05:03:39,306][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 05:03:39,820][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 05:03:40,328][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 05:03:40,835][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 05:03:41,346][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 05:03:41,855][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 05:03:42,368][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 05:03:42,872][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 05:03:43,381][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 05:03:43,890][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 05:03:44,399][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 05:03:44,907][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 05:03:45,419][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 05:03:45,929][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 05:03:46,446][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 05:03:46,956][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 05:03:47,465][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 05:03:47,972][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 05:03:48,482][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 05:03:48,991][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 05:03:49,498][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 05:03:50,006][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 05:03:50,515][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 05:03:51,024][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 05:03:51,532][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 05:03:52,040][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 05:03:52,543][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 05:03:53,058][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 05:03:53,561][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 05:03:54,066][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 05:03:54,571][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 05:03:55,077][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 05:03:55,585][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 05:03:56,092][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10873 tokens. [2025-11-13 05:03:56,890][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.14%, Current % of VRAM taken: 59.59%, Block Peak % of device VRAM: 62.24%, ΔTime: 00:00:33 [2025-11-13 05:03:57,535][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 05:03:57,537][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 05:03:57,540][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 05:03:58,489][__main__][INFO] - Iteration 467 took 1m 7s (45.20% Gen, 53.38% Train). Generation: 30s, Training: 35s. Estimated remaining time: 48h 59m 51s. Estimated total time: 55h 54m 8s. Time estimates for 10 more iterations: 11m 10s, 100 more iterations: 1h 51m 48s, 500 more iterations: 9h 19m 1s. [2025-11-13 05:03:58,491][__main__][INFO] - Starting iteration 467. [2025-11-13 05:03:58,996][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 46 and human policies 1. [2025-11-13 05:03:58,996][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 05:04:15,636][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:04:19,081][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:04:19,695][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:04:27,057][__main__][INFO] - Number of regex retries in iteration 467: 3 [2025-11-13 05:04:27,058][__main__][INFO] - agents played in iteration 467 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 05:04:27,902][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.54%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:04:27,931][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.54%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:04:27,957][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.54%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:04:27,982][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.54%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:04:27,982][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 05:04:27,983][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 05:04:28,777][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 05:04:29,242][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 05:04:29,762][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 05:04:30,271][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 05:04:30,783][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 05:04:31,296][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 05:04:31,804][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 05:04:32,315][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 05:04:32,827][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 05:04:33,340][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 05:04:33,864][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 05:04:34,373][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 05:04:34,882][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 05:04:35,391][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 05:04:35,900][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 05:04:36,410][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 05:04:36,921][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 05:04:37,432][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 05:04:37,948][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 05:04:38,456][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 05:04:38,967][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 05:04:39,482][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 05:04:39,987][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 05:04:40,494][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 05:04:40,997][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 05:04:41,498][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 05:04:41,998][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 05:04:42,499][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 05:04:43,003][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 05:04:43,505][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 05:04:44,008][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 05:04:44,510][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 05:04:45,018][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 05:04:45,544][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 05:04:46,052][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 05:04:46,557][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 05:04:47,064][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 05:04:47,568][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 05:04:48,081][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 05:04:48,591][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 05:04:49,104][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 05:04:49,617][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 05:04:50,127][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 05:04:50,641][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 05:04:51,152][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 05:04:51,665][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 05:04:52,177][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 05:04:52,687][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 05:04:53,198][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 05:04:53,708][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 05:04:54,214][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 05:04:54,741][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 05:04:55,249][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 05:04:55,762][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 05:04:56,268][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 05:04:56,774][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 05:04:57,286][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 05:04:57,795][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 05:04:58,304][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 05:04:58,816][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 05:04:59,318][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 05:04:59,823][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 05:05:00,336][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 05:05:00,837][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 05:05:01,354][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10877 tokens. [2025-11-13 05:05:02,093][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.21%, Current % of VRAM taken: 59.66%, Block Peak % of device VRAM: 62.35%, ΔTime: 00:00:33 [2025-11-13 05:05:02,841][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 05:05:02,843][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 05:05:02,845][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 05:05:03,910][__main__][INFO] - Iteration 468 took 1m 4s (43.23% Gen, 55.13% Train). Generation: 28s, Training: 35s. Estimated remaining time: 47h 10m 22s. Estimated total time: 54h 5m 45s. Time estimates for 10 more iterations: 10m 49s, 100 more iterations: 1h 48m 11s, 500 more iterations: 9h 0m 57s. [2025-11-13 05:05:03,912][__main__][INFO] - Starting iteration 468. [2025-11-13 05:05:04,396][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 46 and human policies 1. [2025-11-13 05:05:04,397][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 05:05:23,183][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:05:23,363][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:05:28,599][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:05:29,859][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:05:33,416][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:05:34,321][__main__][INFO] - Number of regex retries in iteration 468: 5 [2025-11-13 05:05:34,321][__main__][INFO] - agents played in iteration 468 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 05:05:35,172][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:05:35,195][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:05:35,219][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:05:35,242][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:05:35,243][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 05:05:35,244][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 05:05:36,049][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 05:05:36,516][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 05:05:37,032][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 05:05:37,542][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 05:05:38,061][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 05:05:38,574][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 05:05:39,083][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 05:05:39,597][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 05:05:40,108][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 05:05:40,616][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 05:05:41,124][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 05:05:41,633][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 05:05:42,157][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 05:05:42,665][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 05:05:43,176][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 05:05:43,686][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 05:05:44,196][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 05:05:44,701][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 05:05:45,214][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 05:05:45,725][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 05:05:46,238][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 05:05:46,749][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 05:05:52,899][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 05:05:53,414][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 05:05:53,921][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 05:05:54,434][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 05:05:54,945][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 05:05:55,458][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 05:05:55,968][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 05:05:56,489][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 05:05:56,999][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 05:05:57,514][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 05:05:58,029][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 05:05:58,541][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 05:05:59,052][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 05:05:59,563][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 05:06:00,071][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 05:06:00,592][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 05:06:01,100][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 05:06:01,612][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 05:06:02,121][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 05:06:02,631][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 05:06:03,145][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 05:06:03,658][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 05:06:04,168][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 05:06:04,682][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 05:06:05,189][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 05:06:05,697][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 05:06:06,206][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 05:06:06,714][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 05:06:07,224][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 05:06:07,734][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 05:06:08,240][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 05:06:08,745][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10876 tokens. [2025-11-13 05:06:09,556][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.20%, Current % of VRAM taken: 59.65%, Block Peak % of device VRAM: 62.41%, ΔTime: 00:00:33 [2025-11-13 05:06:10,181][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 05:06:10,183][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 05:06:10,185][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 05:06:11,078][__main__][INFO] - Iteration 469 took 1m 6s (44.88% Gen, 53.78% Train). Generation: 29s, Training: 35s. Estimated remaining time: 48h 37m 36s. Estimated total time: 55h 34m 5s. Time estimates for 10 more iterations: 11m 6s, 100 more iterations: 1h 51m 8s, 500 more iterations: 9h 15m 40s. [2025-11-13 05:06:11,080][__main__][INFO] - Starting iteration 469. [2025-11-13 05:06:11,560][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 46 and human policies 1. [2025-11-13 05:06:11,560][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 05:06:27,571][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:06:29,667][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:06:36,300][__main__][INFO] - Number of regex retries in iteration 469: 2 [2025-11-13 05:06:36,301][__main__][INFO] - agents played in iteration 469 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 05:06:37,133][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:06:37,155][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:06:37,178][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:06:37,200][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:06:37,200][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 05:06:37,201][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 05:06:37,900][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 05:06:38,358][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 05:06:38,865][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 05:06:39,368][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 05:06:39,891][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 05:06:40,395][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 05:06:40,899][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 05:06:41,403][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 05:06:41,910][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 05:06:42,419][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 05:06:42,923][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 05:06:43,427][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 05:06:43,933][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 05:06:44,437][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 05:06:44,945][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 05:06:45,455][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 05:06:45,960][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 05:06:46,482][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 05:06:46,990][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 05:06:47,499][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 05:06:48,010][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 05:06:48,521][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 05:06:49,038][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 05:06:49,545][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 05:06:50,058][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 05:06:50,576][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 05:06:51,083][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 05:06:51,595][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 05:06:52,105][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 05:06:52,614][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 05:06:53,128][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 05:06:53,637][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 05:06:54,151][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 05:06:54,661][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 05:06:55,164][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 05:06:55,671][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 05:06:56,178][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 05:06:56,686][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 05:06:57,191][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 05:06:57,696][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 05:06:58,206][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 05:06:58,713][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 05:06:59,218][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 05:06:59,732][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 05:07:00,244][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 05:07:00,754][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 05:07:01,260][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 05:07:01,770][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 05:07:02,284][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 05:07:02,794][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 05:07:03,300][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 05:07:03,811][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 05:07:04,324][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 05:07:04,839][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 05:07:05,355][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 05:07:05,866][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 05:07:06,374][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 05:07:06,882][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 05:07:07,406][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 05:07:07,913][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 05:07:08,426][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 05:07:08,937][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 05:07:09,451][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 05:07:09,963][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 05:07:10,465][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10879 tokens. [2025-11-13 05:07:11,194][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.19%, Current % of VRAM taken: 59.65%, Block Peak % of device VRAM: 62.28%, ΔTime: 00:00:33 [2025-11-13 05:07:11,928][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 05:07:11,929][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 05:07:11,931][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 05:07:13,010][__main__][INFO] - Iteration 470 took 1m 1s (40.26% Gen, 57.98% Train). Generation: 24s, Training: 35s. Estimated remaining time: 44h 15m 2s. Estimated total time: 51h 12m 33s. Time estimates for 10 more iterations: 10m 14s, 100 more iterations: 1h 42m 25s, 500 more iterations: 8h 32m 5s. [2025-11-13 05:07:13,013][__main__][INFO] - Starting iteration 470. [2025-11-13 05:07:13,483][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 46 and human policies 1. [2025-11-13 05:07:13,484][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 05:07:28,233][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:07:31,083][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:07:31,389][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:07:36,925][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:07:39,263][__main__][INFO] - Number of regex retries in iteration 470: 4 [2025-11-13 05:07:39,263][__main__][INFO] - agents played in iteration 470 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 05:07:40,136][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:07:40,163][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:07:40,189][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:07:40,212][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:07:40,213][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 05:07:40,214][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 05:07:40,973][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 05:07:41,439][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 05:07:41,950][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 05:07:42,461][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 05:07:42,970][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 05:07:43,472][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 05:07:43,974][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 05:07:44,477][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 05:07:44,982][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 05:07:45,496][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 05:07:46,002][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 05:07:46,511][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 05:07:47,020][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 05:07:47,527][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 05:07:48,034][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 05:07:48,543][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 05:07:49,052][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 05:07:49,563][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 05:07:50,068][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 05:07:50,578][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 05:07:51,086][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 05:07:51,594][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 05:07:52,114][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 05:07:52,622][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 05:07:53,132][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 05:07:53,643][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 05:07:54,157][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 05:07:54,671][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 05:07:55,179][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 05:07:55,692][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 05:07:56,203][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 05:07:56,714][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 05:07:57,223][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 05:07:57,734][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 05:07:58,241][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 05:07:58,754][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 05:07:59,262][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 05:07:59,771][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 05:08:00,288][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 05:08:00,799][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 05:08:01,312][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 05:08:01,828][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 05:08:02,342][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 05:08:02,852][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 05:08:03,364][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 05:08:03,887][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 05:08:04,402][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 05:08:04,913][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 05:08:05,422][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 05:08:05,928][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 05:08:06,440][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 05:08:06,949][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 05:08:07,461][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 05:08:07,980][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 05:08:08,489][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 05:08:09,004][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 05:08:09,519][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 05:08:10,032][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 05:08:10,540][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 05:08:11,046][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 05:08:11,555][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 05:08:12,063][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 05:08:12,571][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 05:08:13,093][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 05:08:13,600][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10853 tokens. [2025-11-13 05:08:14,419][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.11%, Current % of VRAM taken: 59.56%, Block Peak % of device VRAM: 62.26%, ΔTime: 00:00:33 [2025-11-13 05:08:15,057][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 05:08:15,058][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 05:08:15,060][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 05:08:17,146][__main__][INFO] - Iteration 471 took 1m 3s (40.49% Gen, 56.23% Train). Generation: 25s, Training: 35s. Estimated remaining time: 46h 4m 35s. Estimated total time: 53h 3m 10s. Time estimates for 10 more iterations: 10m 36s, 100 more iterations: 1h 46m 6s, 500 more iterations: 8h 50m 31s. [2025-11-13 05:08:17,148][__main__][INFO] - Starting iteration 471. [2025-11-13 05:08:17,659][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 47 and human policies 1. [2025-11-13 05:08:17,660][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 05:08:46,745][__main__][INFO] - Number of regex retries in iteration 471: 0 [2025-11-13 05:08:46,745][__main__][INFO] - agents played in iteration 471 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 05:08:47,571][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.53%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:08:47,596][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.53%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:08:47,620][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.53%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:08:47,642][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.53%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:08:47,643][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 05:08:47,644][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 05:08:48,335][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 05:08:48,797][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 05:08:49,307][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 05:08:49,811][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 05:08:50,313][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 05:08:50,814][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 05:08:51,316][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 05:08:51,820][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 05:08:52,324][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 05:08:52,829][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 05:08:53,333][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 05:08:53,838][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 05:08:54,341][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 05:08:54,850][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 05:08:55,360][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 05:08:55,869][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 05:08:56,378][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 05:08:56,893][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 05:08:57,403][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 05:08:57,910][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 05:08:58,420][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 05:08:58,930][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 05:08:59,439][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 05:08:59,952][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 05:09:00,463][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 05:09:00,972][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 05:09:01,478][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 05:09:02,006][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 05:09:02,515][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 05:09:03,024][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 05:09:03,531][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 05:09:04,042][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 05:09:04,549][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 05:09:05,055][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 05:09:05,566][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 05:09:06,085][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 05:09:06,589][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 05:09:07,102][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 05:09:07,606][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 05:09:08,108][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 05:09:08,615][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 05:09:09,128][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 05:09:09,633][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 05:09:10,143][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 05:09:10,651][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 05:09:11,162][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 05:09:11,667][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 05:09:12,177][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 05:09:12,692][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 05:09:13,203][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 05:09:13,717][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 05:09:14,226][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 05:09:14,735][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 05:09:15,243][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 05:09:15,747][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 05:09:16,257][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 05:09:16,765][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 05:09:17,269][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 05:09:17,790][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 05:09:18,298][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 05:09:18,816][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 05:09:19,328][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 05:09:19,846][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 05:09:20,358][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 05:09:20,867][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10836 tokens. [2025-11-13 05:09:21,662][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.20%, Current % of VRAM taken: 59.65%, Block Peak % of device VRAM: 62.25%, ΔTime: 00:00:33 [2025-11-13 05:09:22,409][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 05:09:22,411][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 05:09:22,413][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 05:09:23,401][__main__][INFO] - Iteration 472 took 1m 5s (44.24% Gen, 54.25% Train). Generation: 29s, Training: 35s. Estimated remaining time: 47h 47m 24s. Estimated total time: 54h 47m 5s. Time estimates for 10 more iterations: 10m 57s, 100 more iterations: 1h 49m 34s, 500 more iterations: 9h 7m 50s. [2025-11-13 05:09:23,403][__main__][INFO] - Starting iteration 472. [2025-11-13 05:09:23,872][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 47 and human policies 1. [2025-11-13 05:09:23,872][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 05:09:43,799][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:09:49,856][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:09:50,052][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:09:53,738][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:09:54,725][__main__][INFO] - Number of regex retries in iteration 472: 4 [2025-11-13 05:09:54,726][__main__][INFO] - agents played in iteration 472 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 05:09:55,691][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:09:55,720][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:09:55,745][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:09:55,768][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:09:55,769][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 05:09:55,770][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 05:09:56,563][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 05:09:57,031][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 05:09:57,550][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 05:09:58,066][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 05:09:58,579][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 05:09:59,089][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 05:09:59,604][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 05:10:00,116][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 05:10:00,630][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 05:10:01,138][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 05:10:01,646][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 05:10:02,155][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 05:10:02,666][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 05:10:03,175][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 05:10:03,683][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 05:10:04,192][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 05:10:04,704][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 05:10:05,215][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 05:10:05,723][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 05:10:06,234][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 05:10:06,740][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 05:10:07,251][mllm.training.trainer_common][INFO] - 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Processing mini-batch 43 of 64 [2025-11-13 05:10:19,036][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 05:10:19,546][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 05:10:20,056][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 05:10:20,567][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 05:10:21,075][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 05:10:21,585][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 05:10:22,093][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 05:10:22,602][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 05:10:23,126][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 05:10:23,637][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 05:10:24,147][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 05:10:24,660][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 05:10:25,169][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 05:10:25,681][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 05:10:26,190][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 05:10:26,705][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 05:10:27,219][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 05:10:27,730][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 05:10:28,243][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 05:10:28,755][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 05:10:29,265][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10879 tokens. [2025-11-13 05:10:30,078][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.25%, Current % of VRAM taken: 59.71%, Block Peak % of device VRAM: 62.31%, ΔTime: 00:00:33 [2025-11-13 05:10:30,746][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 05:10:30,748][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 05:10:30,750][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 05:10:31,783][__main__][INFO] - Iteration 473 took 1m 7s (45.43% Gen, 53.05% Train). Generation: 30s, Training: 36s. Estimated remaining time: 49h 34m 46s. Estimated total time: 56h 35m 36s. Time estimates for 10 more iterations: 11m 19s, 100 more iterations: 1h 53m 11s, 500 more iterations: 9h 25m 56s. [2025-11-13 05:10:31,786][__main__][INFO] - Starting iteration 473. [2025-11-13 05:10:32,277][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 47 and human policies 1. [2025-11-13 05:10:32,277][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 05:10:45,001][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:10:53,678][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:10:54,618][__main__][INFO] - Number of regex retries in iteration 473: 2 [2025-11-13 05:10:54,619][__main__][INFO] - agents played in iteration 473 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 05:10:55,440][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:10:55,465][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:10:55,490][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:10:55,513][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:10:55,513][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 05:10:55,514][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 05:10:56,293][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 05:10:56,759][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 05:10:57,276][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 05:10:57,783][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 05:10:58,295][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 05:10:58,805][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 05:10:59,313][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 05:10:59,827][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 05:11:00,338][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 05:11:00,852][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 05:11:01,356][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 05:11:13,034][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 05:11:13,545][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 05:11:14,054][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 05:11:14,564][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 05:11:15,070][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 05:11:15,575][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 05:11:16,083][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 05:11:16,595][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 05:11:17,102][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 05:11:17,610][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 05:11:18,123][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 05:11:18,629][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 05:11:19,137][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 05:11:19,646][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 05:11:20,158][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 05:11:20,666][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 05:11:21,169][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 05:11:21,672][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 05:11:22,173][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 05:11:22,676][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 05:11:23,183][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 05:11:23,686][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 05:11:24,189][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 05:11:24,696][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 05:11:25,200][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 05:11:25,706][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 05:11:26,214][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 05:11:26,723][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 05:11:27,234][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 05:11:27,741][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 05:11:28,255][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 05:11:28,768][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10878 tokens. [2025-11-13 05:11:29,587][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.20%, Current % of VRAM taken: 59.65%, Block Peak % of device VRAM: 62.10%, ΔTime: 00:00:33 [2025-11-13 05:11:30,307][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 05:11:30,308][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 05:11:30,311][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 05:11:31,293][__main__][INFO] - Iteration 474 took 59s (37.86% Gen, 60.48% Train). Generation: 22s, Training: 35s. Estimated remaining time: 42h 9m 0s. Estimated total time: 49h 10m 49s. Time estimates for 10 more iterations: 9m 50s, 100 more iterations: 1h 38m 21s, 500 more iterations: 8h 11m 48s. [2025-11-13 05:11:31,295][__main__][INFO] - Starting iteration 474. [2025-11-13 05:11:31,787][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 47 and human policies 1. [2025-11-13 05:11:31,788][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 05:11:57,855][__main__][INFO] - Number of regex retries in iteration 474: 0 [2025-11-13 05:11:57,857][__main__][INFO] - agents played in iteration 474 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 05:11:58,729][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:11:58,756][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:11:58,783][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:11:58,807][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:11:58,807][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 05:11:58,808][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 05:11:59,572][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 05:12:00,039][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 05:12:00,553][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 05:12:01,062][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 05:12:01,578][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 05:12:02,085][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 05:12:02,595][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 05:12:03,104][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 05:12:03,610][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 05:12:04,125][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 05:12:04,637][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 05:12:05,143][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 05:12:05,658][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 05:12:06,163][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 05:12:06,669][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 05:12:07,180][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 05:12:07,690][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 05:12:08,205][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 05:12:08,716][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 05:12:09,239][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 05:12:09,750][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 05:12:10,259][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 05:12:16,390][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 05:12:16,897][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 05:12:17,415][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 05:12:17,923][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 05:12:18,439][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 05:12:18,946][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 05:12:19,455][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 05:12:19,968][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 05:12:20,480][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 05:12:20,989][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 05:12:21,496][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 05:12:22,004][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 05:12:22,516][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 05:12:23,022][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 05:12:23,531][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 05:12:24,042][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 05:12:24,552][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 05:12:25,064][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 05:12:25,573][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 05:12:26,081][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 05:12:26,593][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 05:12:27,101][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 05:12:27,617][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 05:12:28,127][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 05:12:28,640][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 05:12:29,159][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 05:12:29,671][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 05:12:30,185][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 05:12:30,697][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 05:12:31,208][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 05:12:31,738][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 05:12:32,253][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10864 tokens. [2025-11-13 05:12:33,081][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.30%, Current % of VRAM taken: 59.75%, Block Peak % of device VRAM: 62.41%, ΔTime: 00:00:33 [2025-11-13 05:12:33,724][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 05:12:33,726][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 05:12:33,728][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 05:12:34,771][__main__][INFO] - Iteration 475 took 1m 2s (41.39% Gen, 56.95% Train). Generation: 26s, Training: 35s. Estimated remaining time: 45h 26m 22s. Estimated total time: 52h 29m 15s. Time estimates for 10 more iterations: 10m 29s, 100 more iterations: 1h 44m 58s, 500 more iterations: 8h 44m 52s. [2025-11-13 05:12:34,774][__main__][INFO] - Starting iteration 475. [2025-11-13 05:12:35,286][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 47 and human policies 1. [2025-11-13 05:12:35,286][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 05:12:54,549][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:12:54,551][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:12:54,555][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:12:54,557][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:12:57,839][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:13:02,093][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:13:05,597][__main__][INFO] - Number of regex retries in iteration 475: 6 [2025-11-13 05:13:05,598][__main__][INFO] - agents played in iteration 475 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 05:13:06,407][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:13:06,436][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:13:06,462][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:13:06,485][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:13:06,486][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 05:13:06,487][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 05:13:07,280][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 05:13:07,745][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 05:13:08,260][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 05:13:08,775][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 05:13:09,288][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 05:13:09,797][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 05:13:10,301][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 05:13:10,809][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 05:13:11,329][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 05:13:11,837][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 05:13:12,346][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 05:13:12,859][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 05:13:13,366][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 05:13:13,877][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 05:13:14,386][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 05:13:14,889][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 05:13:15,393][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 05:13:15,896][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 05:13:16,396][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 05:13:16,898][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 05:13:17,399][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 05:13:17,901][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 05:13:18,400][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 05:13:18,899][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 05:13:19,416][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 05:13:19,923][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 05:13:20,438][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 05:13:20,941][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 05:13:21,444][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 05:13:21,949][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 05:13:22,461][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 05:13:22,973][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 05:13:23,483][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 05:13:23,994][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 05:13:24,513][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 05:13:25,025][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 05:13:25,537][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 05:13:26,048][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 05:13:26,557][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 05:13:27,069][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 05:13:27,579][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 05:13:28,084][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 05:13:28,590][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 05:13:29,100][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 05:13:29,607][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 05:13:30,117][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 05:13:30,623][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 05:13:31,144][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 05:13:31,652][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 05:13:32,164][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 05:13:32,673][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 05:13:33,185][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 05:13:33,698][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 05:13:34,208][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 05:13:34,718][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 05:13:35,239][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 05:13:35,752][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 05:13:36,259][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 05:13:36,766][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 05:13:37,272][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 05:13:37,778][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 05:13:38,281][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 05:13:38,785][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 05:13:39,287][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 05:13:39,795][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10878 tokens. [2025-11-13 05:13:40,613][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.16%, Current % of VRAM taken: 59.61%, Block Peak % of device VRAM: 62.31%, ΔTime: 00:00:33 [2025-11-13 05:13:41,357][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 05:13:41,359][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 05:13:41,361][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 05:13:42,288][__main__][INFO] - Iteration 476 took 1m 7s (45.24% Gen, 53.38% Train). Generation: 30s, Training: 35s. Estimated remaining time: 48h 46m 7s. Estimated total time: 55h 50m 7s. Time estimates for 10 more iterations: 11m 10s, 100 more iterations: 1h 51m 40s, 500 more iterations: 9h 18m 21s. [2025-11-13 05:13:42,290][__main__][INFO] - Starting iteration 476. [2025-11-13 05:13:42,786][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 47 and human policies 1. [2025-11-13 05:13:42,786][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 05:14:06,259][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:14:07,031][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:14:15,278][__main__][INFO] - Number of regex retries in iteration 476: 2 [2025-11-13 05:14:15,278][__main__][INFO] - agents played in iteration 476 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 05:14:16,135][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:14:16,163][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:14:16,189][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:14:16,212][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:14:16,212][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 05:14:16,213][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 05:14:16,992][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 05:14:17,456][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 05:14:17,970][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 05:14:18,484][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 05:14:18,994][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 05:14:19,504][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 05:14:20,013][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 05:14:20,522][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 05:14:21,032][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 05:14:21,543][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 05:14:22,052][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 05:14:22,565][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 05:14:23,076][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 05:14:23,588][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 05:14:24,099][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 05:14:24,608][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 05:14:25,118][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 05:14:25,626][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 05:14:26,136][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 05:14:26,650][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 05:14:27,158][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 05:14:27,677][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 05:14:28,185][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 05:14:28,691][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 05:14:29,199][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 05:14:29,710][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 05:14:30,224][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 05:14:30,736][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 05:14:31,244][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 05:14:31,756][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 05:14:32,263][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 05:14:32,776][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 05:14:33,284][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 05:14:33,793][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 05:14:34,302][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 05:14:34,814][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 05:14:35,325][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 05:14:35,832][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 05:14:36,342][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 05:14:36,861][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 05:14:37,371][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 05:14:37,881][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 05:14:38,392][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 05:14:38,899][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 05:14:39,408][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 05:14:39,924][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 05:14:40,436][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 05:14:40,950][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 05:14:41,459][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 05:14:41,968][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 05:14:42,481][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 05:14:42,987][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 05:14:43,499][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 05:14:44,011][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 05:14:44,522][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 05:14:45,038][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 05:14:45,549][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 05:14:46,074][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 05:14:46,587][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 05:14:47,102][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 05:14:47,619][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 05:14:48,132][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 05:14:48,668][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 05:14:49,182][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 05:14:49,692][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10878 tokens. [2025-11-13 05:14:50,507][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.25%, Current % of VRAM taken: 59.70%, Block Peak % of device VRAM: 62.31%, ΔTime: 00:00:33 [2025-11-13 05:14:51,157][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 05:14:51,159][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 05:14:51,161][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 05:14:52,058][__main__][INFO] - Iteration 477 took 1m 9s (46.90% Gen, 51.80% Train). Generation: 32s, Training: 35s. Estimated remaining time: 50h 38m 30s. Estimated total time: 57h 43m 41s. Time estimates for 10 more iterations: 11m 32s, 100 more iterations: 1h 55m 27s, 500 more iterations: 9h 37m 16s. [2025-11-13 05:14:52,060][__main__][INFO] - Starting iteration 477. [2025-11-13 05:14:52,563][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 47 and human policies 1. [2025-11-13 05:14:52,563][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 05:15:08,647][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:15:08,786][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:15:09,680][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:15:19,334][__main__][INFO] - Number of regex retries in iteration 477: 3 [2025-11-13 05:15:19,335][__main__][INFO] - agents played in iteration 477 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 05:15:20,142][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:15:20,166][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:15:20,189][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:15:20,211][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:15:20,212][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 05:15:20,213][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 05:15:20,990][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 05:15:21,459][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 05:15:21,973][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 05:15:22,482][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 05:15:22,994][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 05:15:23,500][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 05:15:24,010][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 05:15:24,519][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 05:15:25,030][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 05:15:25,538][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 05:15:26,047][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 05:15:26,551][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 05:15:27,059][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 05:15:27,567][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 05:15:28,078][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 05:15:28,589][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 05:15:29,093][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 05:15:29,592][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 05:15:30,094][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 05:15:30,597][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 05:15:31,097][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 05:15:31,598][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 05:15:32,099][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 05:15:32,599][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 05:15:33,099][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 05:15:33,600][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 05:15:34,101][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 05:15:34,605][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 05:15:35,105][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 05:15:35,609][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 05:15:36,114][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 05:15:36,617][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 05:15:37,120][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 05:15:37,635][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 05:15:38,140][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 05:15:38,660][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 05:15:39,167][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 05:15:39,678][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 05:15:40,192][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 05:15:40,703][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 05:15:41,214][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 05:15:41,722][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 05:15:42,227][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 05:15:42,764][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 05:15:43,272][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 05:15:43,780][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 05:15:44,292][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 05:15:44,799][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 05:15:45,313][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 05:15:45,821][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 05:15:47,839][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 05:15:48,508][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 05:15:49,024][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 05:15:49,545][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 05:15:50,053][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 05:15:50,576][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 05:15:51,087][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 05:15:51,596][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 05:15:52,106][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 05:15:52,613][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 05:15:53,125][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 05:15:53,639][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 05:15:54,150][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 05:15:54,666][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 05:15:55,179][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10877 tokens. [2025-11-13 05:15:56,052][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.21%, Current % of VRAM taken: 59.66%, Block Peak % of device VRAM: 62.24%, ΔTime: 00:00:35 [2025-11-13 05:15:56,726][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 05:15:56,728][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 05:15:56,730][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 05:15:57,642][__main__][INFO] - Iteration 478 took 1m 5s (41.14% Gen, 57.46% Train). Generation: 26s, Training: 37s. Estimated remaining time: 47h 7m 42s. Estimated total time: 54h 13m 58s. Time estimates for 10 more iterations: 10m 50s, 100 more iterations: 1h 48m 27s, 500 more iterations: 9h 2m 19s. [2025-11-13 05:15:57,644][__main__][INFO] - Starting iteration 478. [2025-11-13 05:15:58,155][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 47 and human policies 1. [2025-11-13 05:15:58,156][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 05:16:17,736][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:16:17,835][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:16:20,386][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:16:21,475][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:16:23,574][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:16:28,506][__main__][INFO] - Number of regex retries in iteration 478: 5 [2025-11-13 05:16:28,506][__main__][INFO] - agents played in iteration 478 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 05:16:29,396][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:16:29,420][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:16:29,442][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:16:29,465][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:16:29,466][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 05:16:29,467][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 05:16:30,236][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 05:16:30,701][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 05:16:31,216][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 05:16:31,727][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 05:16:32,238][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 05:16:32,752][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 05:16:33,264][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 05:16:33,777][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 05:16:34,291][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 05:16:34,806][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 05:16:35,321][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 05:16:35,833][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 05:16:36,345][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 05:16:36,865][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 05:16:37,369][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 05:16:37,877][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 05:16:38,382][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 05:16:38,881][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 05:16:39,388][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 05:16:39,892][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 05:16:40,392][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 05:16:40,894][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 05:16:41,395][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 05:16:41,898][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 05:16:42,399][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 05:16:42,902][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 05:16:43,405][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 05:16:43,909][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 05:16:44,411][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 05:16:44,915][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 05:16:45,418][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 05:16:45,921][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 05:16:46,423][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 05:16:46,924][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 05:16:47,430][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 05:16:47,936][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 05:16:48,466][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 05:16:48,969][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 05:16:49,472][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 05:16:49,974][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 05:16:50,481][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 05:16:50,995][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 05:16:51,506][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 05:16:52,019][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 05:16:52,529][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 05:16:53,039][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 05:16:53,564][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 05:16:54,072][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 05:16:54,581][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 05:16:55,099][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 05:16:55,613][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 05:16:56,124][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 05:16:56,635][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 05:16:57,145][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 05:16:57,672][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 05:16:58,180][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 05:16:58,694][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 05:16:59,200][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 05:16:59,708][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 05:17:00,219][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 05:17:00,731][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 05:17:01,239][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 05:17:01,759][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 05:17:02,269][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 05:17:02,787][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10878 tokens. [2025-11-13 05:17:03,612][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.23%, Current % of VRAM taken: 59.68%, Block Peak % of device VRAM: 62.42%, ΔTime: 00:00:33 [2025-11-13 05:17:04,399][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 05:17:04,401][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 05:17:04,403][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 05:17:05,504][__main__][INFO] - Iteration 479 took 1m 7s (45.06% Gen, 53.30% Train). Generation: 30s, Training: 35s. Estimated remaining time: 49h 0m 4s. Estimated total time: 56h 7m 28s. Time estimates for 10 more iterations: 11m 13s, 100 more iterations: 1h 52m 14s, 500 more iterations: 9h 21m 14s. [2025-11-13 05:17:05,507][__main__][INFO] - Starting iteration 479. [2025-11-13 05:17:06,001][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 47 and human policies 1. [2025-11-13 05:17:06,002][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 05:17:19,834][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:17:20,673][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:17:21,413][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:17:21,513][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:17:23,188][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 0 balls Explanation: Given the values, you have a clear advantage in hats while Bob has an advantage in books and balls. By fully claiming the hats, you maximize your points from this round. Since your value for books and balls is much lower compared to Bob's, proposing to keep any of these items would likely lead to receiving a smaller amount, resulting in fewer points. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:17:26,408][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:17:30,779][__main__][INFO] - Number of regex retries in iteration 479: 6 [2025-11-13 05:17:30,779][__main__][INFO] - agents played in iteration 479 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 05:17:31,595][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:17:31,623][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:17:31,647][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:17:31,670][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:17:31,671][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 05:17:31,672][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 05:17:32,455][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 05:17:32,921][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 05:17:33,437][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 05:17:33,948][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 05:17:34,454][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 05:17:34,957][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 05:17:35,460][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 05:17:35,962][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 05:17:36,466][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 05:17:36,983][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 05:17:37,486][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 05:17:37,990][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 05:17:38,495][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 05:17:38,999][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 05:17:39,503][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 05:17:40,005][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 05:17:40,509][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 05:17:41,013][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 05:17:41,517][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 05:17:42,018][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 05:17:42,521][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 05:17:43,022][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 05:17:43,525][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 05:17:44,027][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 05:17:44,529][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 05:17:45,035][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 05:17:45,540][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 05:17:46,042][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 05:17:46,543][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 05:17:47,044][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 05:17:47,560][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 05:17:48,061][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 05:17:48,562][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 05:17:49,069][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 05:17:49,573][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 05:17:50,082][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 05:17:50,582][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 05:17:51,084][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 05:17:51,589][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 05:17:52,091][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 05:17:52,597][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 05:17:53,107][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 05:17:53,617][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 05:17:54,123][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 05:17:54,627][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 05:17:55,137][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 05:17:55,662][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 05:17:56,813][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 05:17:58,109][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 05:17:58,618][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 05:17:59,126][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 05:17:59,637][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 05:18:00,150][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 05:18:00,674][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 05:18:01,184][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 05:18:01,695][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 05:18:02,205][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 05:18:02,715][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 05:18:03,228][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 05:18:03,738][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 05:18:04,248][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 05:18:04,758][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 05:18:05,268][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 05:18:05,782][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 05:18:06,292][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10878 tokens. [2025-11-13 05:18:07,113][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.22%, Current % of VRAM taken: 59.67%, Block Peak % of device VRAM: 62.32%, ΔTime: 00:00:34 [2025-11-13 05:18:07,777][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 05:18:07,779][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 05:18:07,781][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 05:18:08,834][__main__][INFO] - Iteration 480 took 1m 2s (39.43% Gen, 58.89% Train). Generation: 24s, Training: 37s. Estimated remaining time: 45h 13m 13s. Estimated total time: 52h 21m 40s. Time estimates for 10 more iterations: 10m 28s, 100 more iterations: 1h 44m 43s, 500 more iterations: 8h 43m 36s. [2025-11-13 05:18:08,836][__main__][INFO] - Starting iteration 480. [2025-11-13 05:18:09,334][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 47 and human policies 1. [2025-11-13 05:18:09,335][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 05:18:35,435][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:18:37,348][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:18:39,538][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:18:40,749][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:18:46,857][__main__][INFO] - Number of regex retries in iteration 480: 4 [2025-11-13 05:18:46,857][__main__][INFO] - agents played in iteration 480 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 05:18:47,635][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:18:47,663][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:18:47,689][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:18:47,712][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:18:47,712][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 05:18:47,713][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 05:18:48,429][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 05:18:48,887][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 05:18:49,397][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 05:18:49,899][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 05:18:50,401][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 05:18:50,905][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 05:18:51,407][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 05:18:51,909][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 05:18:52,408][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 05:18:52,914][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 05:18:53,418][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 05:18:53,922][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 05:18:54,430][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 05:18:54,935][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 05:18:55,437][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 05:18:55,939][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 05:18:56,445][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 05:18:56,953][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 05:18:57,465][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 05:18:57,969][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 05:18:58,471][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 05:18:58,980][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 05:18:59,490][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 05:19:00,001][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 05:19:00,513][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 05:19:01,020][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 05:19:01,527][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 05:19:02,036][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 05:19:02,545][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 05:19:03,056][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 05:19:03,564][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 05:19:04,079][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 05:19:04,587][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 05:19:05,097][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 05:19:05,615][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 05:19:06,125][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 05:19:06,635][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 05:19:07,144][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 05:19:07,650][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 05:19:08,162][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 05:19:08,672][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 05:19:09,181][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 05:19:09,690][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 05:19:10,201][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 05:19:10,715][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 05:19:11,225][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 05:19:11,732][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 05:19:12,237][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 05:19:12,748][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 05:19:13,257][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 05:19:13,764][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 05:19:14,267][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 05:19:14,786][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 05:19:15,297][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 05:19:15,809][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 05:19:16,317][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 05:19:16,824][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 05:19:17,336][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 05:19:17,847][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 05:19:18,355][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 05:19:18,865][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 05:19:19,371][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 05:19:19,884][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 05:19:20,392][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 05:19:20,904][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10874 tokens. [2025-11-13 05:19:21,731][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.13%, Current % of VRAM taken: 59.59%, Block Peak % of device VRAM: 62.23%, ΔTime: 00:00:33 [2025-11-13 05:19:22,498][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 05:19:22,500][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 05:19:22,501][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 05:19:24,600][__main__][INFO] - Iteration 481 took 1m 15s (49.85% Gen, 47.36% Train). Generation: 37s, Training: 35s. Estimated remaining time: 55h 33m 36s. Estimated total time: 62h 43m 19s. Time estimates for 10 more iterations: 12m 32s, 100 more iterations: 2h 5m 26s, 500 more iterations: 10h 27m 13s. [2025-11-13 05:19:24,602][__main__][INFO] - Starting iteration 481. [2025-11-13 05:19:25,099][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 48 and human policies 1. [2025-11-13 05:19:25,099][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 05:19:35,111][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:19:38,852][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:19:40,280][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:19:44,231][__main__][INFO] - Number of regex retries in iteration 481: 3 [2025-11-13 05:19:44,232][__main__][INFO] - agents played in iteration 481 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 05:19:45,013][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.54%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:19:45,037][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.54%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:19:45,062][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.54%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:19:45,084][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.54%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:19:45,085][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 05:19:45,086][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 05:19:45,801][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 05:19:46,259][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 05:19:46,765][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 05:19:47,269][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 05:19:47,773][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 05:19:48,277][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 05:19:48,779][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 05:19:49,282][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 05:19:49,804][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 05:19:50,308][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 05:19:50,809][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 05:19:51,314][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 05:19:51,817][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 05:19:52,322][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 05:19:52,826][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 05:19:53,329][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 05:19:53,831][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 05:19:54,333][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 05:19:54,833][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 05:19:55,335][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 05:19:55,837][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 05:19:56,343][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 05:19:56,844][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 05:19:57,345][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 05:19:57,845][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 05:19:58,350][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 05:19:58,852][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 05:19:59,357][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 05:19:59,864][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 05:20:00,375][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 05:20:00,880][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 05:20:01,383][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 05:20:01,891][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 05:20:02,396][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 05:20:02,953][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 05:20:04,703][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 05:20:05,317][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 05:20:05,828][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 05:20:06,338][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 05:20:06,849][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 05:20:07,359][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 05:20:07,866][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 05:20:08,384][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 05:20:08,889][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 05:20:09,407][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 05:20:09,916][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 05:20:10,424][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 05:20:10,934][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 05:20:11,443][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 05:20:11,951][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 05:20:12,457][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 05:20:12,963][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 05:20:13,472][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 05:20:13,980][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 05:20:14,489][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 05:20:15,003][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 05:20:15,510][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 05:20:16,027][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 05:20:16,539][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 05:20:17,050][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 05:20:17,564][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 05:20:18,079][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 05:20:18,591][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 05:20:19,103][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 05:20:19,614][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10878 tokens. [2025-11-13 05:20:20,443][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.24%, Current % of VRAM taken: 59.70%, Block Peak % of device VRAM: 62.41%, ΔTime: 00:00:34 [2025-11-13 05:20:21,106][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 05:20:21,109][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 05:20:21,111][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 05:20:22,033][__main__][INFO] - Iteration 482 took 56s (33.60% Gen, 64.77% Train). Generation: 19s, Training: 36s. Estimated remaining time: 40h 16m 4s. Estimated total time: 47h 26m 44s. Time estimates for 10 more iterations: 9m 29s, 100 more iterations: 1h 34m 53s, 500 more iterations: 7h 54m 27s. [2025-11-13 05:20:22,035][__main__][INFO] - Starting iteration 482. [2025-11-13 05:20:22,553][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 48 and human policies 1. [2025-11-13 05:20:22,554][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 05:20:42,449][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:20:42,798][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:20:44,384][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:20:51,362][__main__][INFO] - Number of regex retries in iteration 482: 3 [2025-11-13 05:20:51,362][__main__][INFO] - agents played in iteration 482 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 05:20:52,169][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:20:52,198][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:20:52,225][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:20:52,249][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:20:52,249][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 05:20:52,250][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 05:20:52,965][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 05:20:53,422][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 05:20:53,932][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 05:20:54,434][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 05:20:54,939][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 05:20:55,446][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 05:20:55,950][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 05:20:56,453][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 05:20:56,960][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 05:20:57,465][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 05:20:57,971][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 05:20:58,474][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 05:20:58,975][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 05:20:59,477][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 05:20:59,978][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 05:21:00,478][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 05:21:00,988][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 05:21:01,491][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 05:21:01,998][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 05:21:02,499][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 05:21:02,999][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 05:21:03,503][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 05:21:04,004][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 05:21:04,504][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 05:21:05,010][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 05:21:05,510][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 05:21:06,026][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 05:21:06,524][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 05:21:07,024][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 05:21:07,524][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 05:21:08,024][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 05:21:08,529][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 05:21:09,030][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 05:21:09,530][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 05:21:10,034][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 05:21:10,541][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 05:21:11,045][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 05:21:11,546][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 05:21:12,047][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 05:21:12,550][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 05:21:13,055][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 05:21:13,565][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 05:21:14,074][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 05:21:14,583][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 05:21:15,093][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 05:21:15,604][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 05:21:16,111][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 05:21:16,641][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 05:21:17,147][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 05:21:17,660][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 05:21:18,170][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 05:21:18,682][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 05:21:19,193][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 05:21:19,704][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 05:21:20,214][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 05:21:20,724][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 05:21:21,238][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 05:21:21,752][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 05:21:22,263][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 05:21:22,777][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 05:21:23,288][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 05:21:23,801][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 05:21:24,327][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 05:21:24,835][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 05:21:25,343][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10876 tokens. [2025-11-13 05:21:26,172][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.26%, Current % of VRAM taken: 59.71%, Block Peak % of device VRAM: 62.35%, ΔTime: 00:00:33 [2025-11-13 05:21:26,908][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 05:21:26,911][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 05:21:26,912][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 05:21:27,893][__main__][INFO] - Iteration 483 took 1m 5s (44.09% Gen, 54.41% Train). Generation: 28s, Training: 35s. Estimated remaining time: 47h 15m 15s. Estimated total time: 54h 27m 1s. Time estimates for 10 more iterations: 10m 53s, 100 more iterations: 1h 48m 54s, 500 more iterations: 9h 4m 30s. [2025-11-13 05:21:27,896][__main__][INFO] - Starting iteration 483. [2025-11-13 05:21:28,395][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 48 and human policies 1. [2025-11-13 05:21:28,396][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 05:21:45,008][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:21:45,600][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:21:45,679][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:21:53,944][__main__][INFO] - Number of regex retries in iteration 483: 3 [2025-11-13 05:21:53,945][__main__][INFO] - agents played in iteration 483 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 05:21:54,805][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:21:54,832][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:21:54,858][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:21:54,882][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:21:54,882][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 05:21:54,883][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 05:21:55,611][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 05:21:56,154][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 05:21:56,666][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 05:21:57,175][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 05:21:57,679][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 05:21:58,181][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 05:21:58,683][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 05:21:59,185][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 05:21:59,687][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 05:22:00,187][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 05:22:00,688][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 05:22:01,189][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 05:22:01,690][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 05:22:02,195][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 05:22:02,707][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 05:22:03,209][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 05:22:03,725][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 05:22:04,228][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 05:22:04,729][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 05:22:05,234][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 05:22:05,737][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 05:22:06,240][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 05:22:06,742][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 05:22:07,244][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 05:22:07,748][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 05:22:08,252][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 05:22:08,758][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 05:22:09,260][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 05:22:09,765][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 05:22:10,272][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 05:22:10,773][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 05:22:11,276][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 05:22:11,780][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 05:22:12,287][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 05:22:12,790][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 05:22:13,293][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 05:22:13,800][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 05:22:14,317][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 05:22:14,822][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 05:22:15,328][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 05:22:15,836][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 05:22:16,340][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 05:22:16,853][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 05:22:17,365][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 05:22:17,875][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 05:22:18,390][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 05:22:18,902][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 05:22:19,412][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 05:22:19,926][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 05:22:20,439][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 05:22:20,958][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 05:22:21,471][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 05:22:21,982][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 05:22:22,491][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 05:22:23,001][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 05:22:23,526][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 05:22:24,038][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 05:22:24,553][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 05:22:25,060][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 05:22:25,568][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 05:22:26,079][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 05:22:26,586][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 05:22:27,093][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 05:22:27,609][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 05:22:28,122][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10875 tokens. [2025-11-13 05:22:28,958][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.16%, Current % of VRAM taken: 59.61%, Block Peak % of device VRAM: 62.41%, ΔTime: 00:00:33 [2025-11-13 05:22:29,606][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 05:22:29,608][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 05:22:29,610][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 05:22:30,565][__main__][INFO] - Iteration 484 took 1m 2s (41.09% Gen, 57.37% Train). Generation: 25s, Training: 35s. Estimated remaining time: 44h 35m 43s. Estimated total time: 51h 48m 32s. Time estimates for 10 more iterations: 10m 21s, 100 more iterations: 1h 43m 37s, 500 more iterations: 8h 38m 5s. [2025-11-13 05:22:30,567][__main__][INFO] - Starting iteration 484. [2025-11-13 05:22:31,094][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 48 and human policies 1. [2025-11-13 05:22:31,095][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 05:22:53,555][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:22:57,311][__main__][INFO] - Number of regex retries in iteration 484: 1 [2025-11-13 05:22:57,311][__main__][INFO] - agents played in iteration 484 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 05:22:58,152][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:22:58,178][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:22:58,203][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:22:58,226][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:22:58,227][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 05:22:58,227][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 05:22:58,993][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 05:22:59,453][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 05:22:59,962][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 05:23:00,471][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 05:23:00,976][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 05:23:01,481][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 05:23:01,985][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 05:23:02,488][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 05:23:02,990][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 05:23:03,489][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 05:23:03,988][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 05:23:04,488][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 05:23:04,988][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 05:23:05,485][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 05:23:05,986][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 05:23:06,483][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 05:23:06,982][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 05:23:07,483][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 05:23:07,984][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 05:23:08,484][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 05:23:08,984][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 05:23:09,493][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 05:23:09,993][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 05:23:10,497][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 05:23:11,009][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 05:23:11,514][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 05:23:12,016][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 05:23:12,517][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 05:23:13,020][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 05:23:13,539][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 05:23:14,041][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 05:23:14,545][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 05:23:15,044][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 05:23:15,545][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 05:23:16,049][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 05:23:16,550][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 05:23:17,050][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 05:23:17,557][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 05:23:18,058][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 05:23:18,560][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 05:23:19,060][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 05:23:19,562][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 05:23:20,065][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 05:23:20,567][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 05:23:21,069][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 05:23:21,571][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 05:23:22,070][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 05:23:22,572][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 05:23:23,073][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 05:23:23,573][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 05:23:24,078][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 05:23:24,583][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 05:23:25,087][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 05:23:25,591][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 05:23:26,096][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 05:23:26,599][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 05:23:27,104][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 05:23:27,613][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 05:23:28,125][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 05:23:28,631][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 05:23:29,154][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 05:23:29,664][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 05:23:30,173][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 05:23:30,686][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 05:23:31,196][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10846 tokens. [2025-11-13 05:23:32,014][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.30%, Current % of VRAM taken: 59.75%, Block Peak % of device VRAM: 62.42%, ΔTime: 00:00:33 [2025-11-13 05:23:32,759][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 05:23:32,761][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 05:23:32,763][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 05:23:33,780][__main__][INFO] - Iteration 485 took 1m 2s (41.82% Gen, 56.55% Train). Generation: 26s, Training: 35s. Estimated remaining time: 45h 0m 27s. Estimated total time: 52h 14m 19s. Time estimates for 10 more iterations: 10m 26s, 100 more iterations: 1h 44m 28s, 500 more iterations: 8h 42m 23s. [2025-11-13 05:23:33,782][__main__][INFO] - Starting iteration 485. [2025-11-13 05:23:34,327][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 48 and human policies 1. [2025-11-13 05:23:34,327][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 05:23:47,525][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:23:50,482][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:23:58,298][__main__][INFO] - Number of regex retries in iteration 485: 2 [2025-11-13 05:23:58,299][__main__][INFO] - agents played in iteration 485 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 05:23:59,115][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:23:59,144][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:23:59,181][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:23:59,205][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:23:59,206][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 05:23:59,207][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 05:23:59,950][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 05:24:00,408][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 05:24:00,943][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 05:24:01,447][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 05:24:01,955][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 05:24:02,459][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 05:24:02,962][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 05:24:03,467][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 05:24:03,971][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 05:24:04,478][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 05:24:04,984][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 05:24:05,484][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 05:24:05,986][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 05:24:06,487][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 05:24:06,988][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 05:24:07,490][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 05:24:07,991][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 05:24:08,492][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 05:24:08,993][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 05:24:09,495][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 05:24:09,996][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 05:24:10,495][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 05:24:10,996][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 05:24:11,498][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 05:24:11,997][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 05:24:12,498][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 05:24:12,999][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 05:24:13,499][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 05:24:14,004][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 05:24:14,506][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 05:24:15,006][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 05:24:15,519][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 05:24:16,020][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 05:24:16,531][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 05:24:17,032][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 05:24:17,533][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 05:24:18,050][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 05:24:18,552][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 05:24:19,057][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 05:24:19,558][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 05:24:20,059][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 05:24:20,565][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 05:24:21,068][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 05:24:21,570][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 05:24:22,070][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 05:24:22,570][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 05:24:23,072][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 05:24:23,573][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 05:24:24,077][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 05:24:24,579][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 05:24:25,081][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 05:24:25,581][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 05:24:26,082][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 05:24:26,582][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 05:24:27,083][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 05:24:27,585][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 05:24:28,089][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 05:24:28,592][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 05:24:29,098][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 05:24:29,605][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 05:24:30,106][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 05:24:30,613][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 05:24:31,134][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 05:24:31,644][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 05:24:32,157][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10877 tokens. [2025-11-13 05:24:32,956][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.20%, Current % of VRAM taken: 59.65%, Block Peak % of device VRAM: 62.24%, ΔTime: 00:00:33 [2025-11-13 05:24:33,616][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 05:24:33,617][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 05:24:33,619][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 05:24:34,674][__main__][INFO] - Iteration 486 took 1m 0s (39.72% Gen, 58.53% Train). Generation: 23s, Training: 35s. Estimated remaining time: 43h 2m 31s. Estimated total time: 50h 17m 24s. Time estimates for 10 more iterations: 10m 3s, 100 more iterations: 1h 40m 34s, 500 more iterations: 8h 22m 54s. [2025-11-13 05:24:34,676][__main__][INFO] - Starting iteration 486. [2025-11-13 05:24:35,165][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 48 and human policies 1. [2025-11-13 05:24:35,165][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 05:24:46,267][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:24:55,323][__main__][INFO] - Number of regex retries in iteration 486: 1 [2025-11-13 05:24:55,324][__main__][INFO] - agents played in iteration 486 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 05:24:56,270][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:24:56,295][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:24:56,320][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:24:56,343][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:24:56,344][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 05:24:56,345][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 05:24:57,085][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 05:24:57,542][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 05:24:58,050][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 05:24:58,554][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 05:24:59,058][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 05:24:59,560][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 05:25:00,067][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 05:25:00,577][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 05:25:01,082][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 05:25:01,583][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 05:25:02,087][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 05:25:02,593][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 05:25:03,096][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 05:25:03,600][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 05:25:04,107][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 05:25:04,613][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 05:25:05,120][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 05:25:05,625][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 05:25:06,127][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 05:25:06,639][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 05:25:07,142][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 05:25:07,658][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 05:25:08,162][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 05:25:08,663][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 05:25:09,165][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 05:25:09,668][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 05:25:10,173][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 05:25:10,674][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 05:25:11,177][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 05:25:11,682][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 05:25:12,183][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 05:25:12,685][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 05:25:13,185][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 05:25:13,691][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 05:25:14,195][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 05:25:14,697][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 05:25:15,200][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 05:25:15,701][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 05:25:16,202][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 05:25:16,703][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 05:25:17,205][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 05:25:17,706][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 05:25:18,208][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 05:25:18,708][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 05:25:19,210][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 05:25:19,711][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 05:25:20,213][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 05:25:20,712][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 05:25:21,212][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 05:25:21,713][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 05:25:22,216][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 05:25:22,719][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 05:25:23,220][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 05:25:23,734][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 05:25:24,236][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 05:25:24,747][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 05:25:25,249][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 05:25:25,750][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 05:25:26,254][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 05:25:26,755][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 05:25:27,259][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 05:25:27,758][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 05:25:28,262][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 05:25:28,776][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 05:25:29,280][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10865 tokens. [2025-11-13 05:25:30,065][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.16%, Current % of VRAM taken: 59.61%, Block Peak % of device VRAM: 62.24%, ΔTime: 00:00:32 [2025-11-13 05:25:30,839][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 05:25:30,841][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 05:25:30,843][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 05:25:31,841][__main__][INFO] - Iteration 487 took 56s (35.57% Gen, 62.67% Train). Generation: 20s, Training: 35s. Estimated remaining time: 39h 58m 2s. Estimated total time: 47h 13m 52s. Time estimates for 10 more iterations: 9m 26s, 100 more iterations: 1h 34m 27s, 500 more iterations: 7h 52m 18s. [2025-11-13 05:25:31,844][__main__][INFO] - Starting iteration 487. [2025-11-13 05:25:32,355][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 48 and human policies 1. [2025-11-13 05:25:32,356][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 05:25:46,445][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:25:46,537][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:25:46,539][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:25:48,398][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:25:50,720][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:25:53,860][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:25:56,344][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:25:57,303][__main__][INFO] - Number of regex retries in iteration 487: 7 [2025-11-13 05:25:57,304][__main__][INFO] - agents played in iteration 487 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 05:25:58,253][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:25:58,309][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:25:58,345][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:25:58,377][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:25:58,378][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 05:25:58,379][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 05:25:59,257][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 05:25:59,804][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 05:26:00,325][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 05:26:00,828][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 05:26:01,334][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 05:26:01,840][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 05:26:02,344][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 05:26:02,850][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 05:26:03,354][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 05:26:03,864][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 05:26:04,370][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 05:26:04,875][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 05:26:05,381][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 05:26:05,886][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 05:26:06,390][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 05:26:06,900][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 05:26:07,405][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 05:26:07,909][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 05:26:08,415][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 05:26:08,919][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 05:26:09,423][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 05:26:09,925][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 05:26:10,429][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 05:26:10,940][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 05:26:11,446][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 05:26:11,956][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 05:26:12,460][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 05:26:12,961][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 05:26:13,478][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 05:26:13,980][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 05:26:14,480][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 05:26:14,982][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 05:26:15,483][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 05:26:15,988][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 05:26:16,495][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 05:26:17,000][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 05:26:17,504][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 05:26:18,005][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 05:26:18,506][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 05:26:19,010][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 05:26:19,512][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 05:26:20,014][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 05:26:20,516][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 05:26:21,017][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 05:26:21,522][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 05:26:22,023][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 05:26:22,524][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 05:26:23,026][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 05:26:23,526][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 05:26:24,028][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 05:26:24,528][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 05:26:25,029][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 05:26:25,529][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 05:26:26,030][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 05:26:26,532][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 05:26:27,032][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 05:26:27,533][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 05:26:28,037][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 05:26:28,541][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 05:26:29,043][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 05:26:29,544][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 05:26:30,046][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 05:26:30,549][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 05:26:31,051][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 05:26:31,554][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10864 tokens. [2025-11-13 05:26:32,300][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.25%, Current % of VRAM taken: 59.70%, Block Peak % of device VRAM: 62.41%, ΔTime: 00:00:33 [2025-11-13 05:26:32,955][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 05:26:32,956][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 05:26:32,958][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 05:26:33,943][__main__][INFO] - Iteration 488 took 1m 1s (40.51% Gen, 57.89% Train). Generation: 24s, Training: 35s. Estimated remaining time: 44h 2m 34s. Estimated total time: 51h 19m 26s. Time estimates for 10 more iterations: 10m 15s, 100 more iterations: 1h 42m 38s, 500 more iterations: 8h 33m 14s. [2025-11-13 05:26:33,946][__main__][INFO] - Starting iteration 488. [2025-11-13 05:26:34,435][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 48 and human policies 1. [2025-11-13 05:26:34,436][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 05:27:00,826][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:27:01,296][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:27:02,201][__main__][INFO] - Number of regex retries in iteration 488: 2 [2025-11-13 05:27:02,202][__main__][INFO] - agents played in iteration 488 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 05:27:03,032][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:27:03,061][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:27:03,090][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:27:03,126][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:27:03,127][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 05:27:03,128][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 05:27:03,866][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 05:27:04,325][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 05:27:04,836][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 05:27:05,341][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 05:27:05,851][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 05:27:06,356][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 05:27:06,864][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 05:27:07,391][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 05:27:07,895][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 05:27:08,405][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 05:27:08,910][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 05:27:09,416][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 05:27:09,925][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 05:27:10,435][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 05:27:10,940][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 05:27:11,449][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 05:27:11,953][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 05:27:12,482][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 05:27:12,983][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 05:27:13,485][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 05:27:13,989][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 05:27:14,491][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 05:27:14,995][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 05:27:15,496][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 05:27:16,001][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 05:27:16,509][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 05:27:17,009][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 05:27:17,511][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 05:27:18,010][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 05:27:18,509][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 05:27:19,014][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 05:27:19,515][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 05:27:20,016][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 05:27:20,516][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 05:27:21,017][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 05:27:21,521][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 05:27:22,023][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 05:27:22,522][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 05:27:23,024][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 05:27:23,524][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 05:27:24,024][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 05:27:24,523][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 05:27:25,024][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 05:27:25,527][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 05:27:26,028][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 05:27:26,529][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 05:27:27,029][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 05:27:27,529][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 05:27:28,032][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 05:27:28,533][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 05:27:29,035][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 05:27:29,537][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 05:27:30,039][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 05:27:30,541][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 05:27:31,043][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 05:27:31,545][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 05:27:32,051][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 05:27:32,552][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 05:27:33,053][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 05:27:33,563][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 05:27:34,066][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 05:27:34,568][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 05:27:35,069][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 05:27:35,569][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 05:27:36,073][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10876 tokens. [2025-11-13 05:27:36,846][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.26%, Current % of VRAM taken: 59.71%, Block Peak % of device VRAM: 62.34%, ΔTime: 00:00:32 [2025-11-13 05:27:37,608][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 05:27:37,610][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 05:27:37,611][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 05:27:38,634][__main__][INFO] - Iteration 489 took 1m 4s (43.25% Gen, 55.16% Train). Generation: 27s, Training: 35s. Estimated remaining time: 46h 12m 2s. Estimated total time: 53h 29m 59s. Time estimates for 10 more iterations: 10m 41s, 100 more iterations: 1h 46m 59s, 500 more iterations: 8h 54m 59s. [2025-11-13 05:27:38,636][__main__][INFO] - Starting iteration 489. [2025-11-13 05:27:39,120][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 48 and human policies 1. [2025-11-13 05:27:39,120][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 05:27:58,696][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:27:59,636][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:28:05,048][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:28:09,595][__main__][INFO] - Number of regex retries in iteration 489: 3 [2025-11-13 05:28:09,596][__main__][INFO] - agents played in iteration 489 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 05:28:10,442][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:28:10,469][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:28:10,496][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:28:10,519][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:28:10,520][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 05:28:10,521][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 05:28:11,298][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 05:28:11,757][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 05:28:12,266][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 05:28:12,774][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 05:28:13,279][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 05:28:13,785][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 05:28:14,291][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 05:28:14,794][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 05:28:15,298][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 05:28:15,803][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 05:28:16,310][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 05:28:16,819][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 05:28:17,322][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 05:28:17,827][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 05:28:18,333][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 05:28:18,836][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 05:28:19,342][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 05:28:19,843][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 05:28:20,343][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 05:28:20,844][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 05:28:21,344][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 05:28:21,843][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 05:28:22,342][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 05:28:22,843][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 05:28:23,344][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 05:28:23,843][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 05:28:24,341][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 05:28:24,845][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 05:28:25,346][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 05:28:25,846][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 05:28:26,345][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 05:28:26,845][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 05:28:27,348][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 05:28:27,849][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 05:28:28,349][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 05:28:28,849][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 05:28:29,351][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 05:28:29,852][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 05:28:30,353][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 05:28:30,854][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 05:28:31,356][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 05:28:31,857][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 05:28:32,360][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 05:28:32,861][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 05:28:33,362][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 05:28:33,863][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 05:28:34,363][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 05:28:34,864][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 05:28:35,365][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 05:28:35,866][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 05:28:36,372][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 05:28:36,872][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 05:28:37,371][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 05:28:37,871][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 05:28:38,371][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 05:28:38,872][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 05:28:39,372][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 05:28:39,873][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 05:28:40,376][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 05:28:40,878][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 05:28:41,382][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 05:28:41,909][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 05:28:42,410][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 05:28:42,914][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 05:28:43,424][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10879 tokens. [2025-11-13 05:28:44,208][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.18%, Current % of VRAM taken: 59.64%, Block Peak % of device VRAM: 62.31%, ΔTime: 00:00:32 [2025-11-13 05:28:44,849][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 05:28:44,850][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 05:28:44,852][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 05:28:45,767][__main__][INFO] - Iteration 490 took 1m 6s (45.72% Gen, 52.90% Train). Generation: 30s, Training: 35s. Estimated remaining time: 48h 13m 19s. Estimated total time: 55h 32m 23s. Time estimates for 10 more iterations: 11m 6s, 100 more iterations: 1h 51m 4s, 500 more iterations: 9h 15m 23s. [2025-11-13 05:28:45,769][__main__][INFO] - Starting iteration 490. [2025-11-13 05:28:46,281][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 48 and human policies 1. [2025-11-13 05:28:46,283][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 05:29:02,242][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:29:04,315][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:29:04,410][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:29:06,164][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:29:10,011][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:29:13,218][__main__][INFO] - Number of regex retries in iteration 490: 5 [2025-11-13 05:29:13,219][__main__][INFO] - agents played in iteration 490 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 05:29:14,084][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:29:14,112][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:29:14,140][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:29:14,164][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:29:14,165][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 05:29:14,165][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 05:29:14,946][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 05:29:15,404][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 05:29:15,918][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 05:29:16,422][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 05:29:16,925][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 05:29:17,435][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 05:29:17,937][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 05:29:18,440][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 05:29:18,946][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 05:29:19,447][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 05:29:19,951][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 05:29:20,452][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 05:29:20,955][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 05:29:21,457][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 05:29:21,957][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 05:29:22,459][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 05:29:22,960][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 05:29:23,465][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 05:29:23,968][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 05:29:24,474][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 05:29:24,975][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 05:29:25,477][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 05:29:25,982][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 05:29:26,484][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 05:29:26,986][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 05:29:27,484][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 05:29:27,984][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 05:29:28,483][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 05:29:28,982][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 05:29:29,482][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 05:29:29,982][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 05:29:30,483][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 05:29:30,987][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 05:29:31,488][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 05:29:31,989][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 05:29:32,491][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 05:29:32,995][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 05:29:33,496][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 05:29:33,998][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 05:29:34,498][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 05:29:35,000][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 05:29:35,501][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 05:29:36,017][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 05:29:36,518][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 05:29:37,020][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 05:29:37,523][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 05:29:38,024][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 05:29:38,524][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 05:29:39,024][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 05:29:39,523][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 05:29:40,026][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 05:29:40,528][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 05:29:41,029][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 05:29:41,529][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 05:29:42,031][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 05:29:42,532][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 05:29:43,033][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 05:29:43,537][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 05:29:44,038][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 05:29:44,540][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 05:29:45,042][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 05:29:45,543][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 05:29:46,044][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 05:29:46,550][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 05:29:47,056][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10878 tokens. [2025-11-13 05:29:47,830][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.27%, Current % of VRAM taken: 59.72%, Block Peak % of device VRAM: 62.41%, ΔTime: 00:00:32 [2025-11-13 05:29:48,573][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 05:29:48,575][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 05:29:48,577][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 05:29:50,857][__main__][INFO] - Iteration 491 took 1m 4s (41.71% Gen, 54.76% Train). Generation: 26s, Training: 35s. Estimated remaining time: 46h 28m 39s. Estimated total time: 53h 48m 48s. Time estimates for 10 more iterations: 10m 45s, 100 more iterations: 1h 47m 37s, 500 more iterations: 8h 58m 8s. [2025-11-13 05:29:50,859][__main__][INFO] - Starting iteration 491. [2025-11-13 05:29:51,395][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 49 and human policies 1. [2025-11-13 05:29:51,396][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 05:30:08,411][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:30:11,745][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:30:12,191][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 0 balls Given Bob's per-item values, he assigns much higher value to books and balls compared to hats. Since I value hats less and Bob values hats more, I should take all hats to maximize my points from this item. For books and balls, since Bob values them more highly, I propose to keep none of these items. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:30:13,878][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:30:19,060][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:30:20,861][__main__][INFO] - Number of regex retries in iteration 491: 5 [2025-11-13 05:30:20,861][__main__][INFO] - agents played in iteration 491 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 05:30:21,747][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:30:21,772][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:30:21,797][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:30:21,820][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:30:21,820][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 05:30:21,822][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 05:30:22,519][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 05:30:22,978][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 05:30:23,483][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 05:30:23,982][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 05:30:24,489][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 05:30:24,988][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 05:30:25,488][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 05:30:26,000][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 05:30:26,501][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 05:30:27,006][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 05:30:27,506][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 05:30:28,009][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 05:30:28,520][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 05:30:29,021][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 05:30:29,523][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 05:30:30,023][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 05:30:30,525][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 05:30:31,034][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 05:30:31,551][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 05:30:32,057][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 05:30:32,563][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 05:30:33,067][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 05:30:33,570][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 05:30:34,072][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 05:30:34,576][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 05:30:35,079][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 05:30:35,583][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 05:30:36,086][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 05:30:36,599][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 05:30:37,102][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 05:30:37,604][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 05:30:38,107][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 05:30:38,609][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 05:30:39,120][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 05:30:39,622][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 05:30:40,121][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 05:30:40,621][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 05:30:41,121][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 05:30:41,631][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 05:30:42,130][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 05:30:42,628][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 05:30:43,132][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 05:30:43,634][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 05:30:44,135][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 05:30:44,638][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 05:30:45,140][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 05:30:45,643][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 05:30:46,143][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 05:30:46,644][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 05:30:47,150][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 05:30:47,650][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 05:30:48,153][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 05:30:48,658][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 05:30:49,166][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 05:30:49,673][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 05:30:50,177][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 05:30:50,682][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 05:30:51,190][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 05:30:51,696][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 05:30:52,212][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 05:30:52,724][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 05:30:53,238][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 05:30:53,756][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 05:30:54,268][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 05:30:54,783][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10875 tokens. [2025-11-13 05:30:55,618][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.28%, Current % of VRAM taken: 59.74%, Block Peak % of device VRAM: 62.51%, ΔTime: 00:00:33 [2025-11-13 05:30:56,281][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 05:30:56,283][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 05:30:56,285][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 05:30:57,227][__main__][INFO] - Iteration 492 took 1m 5s (44.76% Gen, 53.81% Train). Generation: 29s, Training: 35s. Estimated remaining time: 47h 30m 22s. Estimated total time: 54h 51m 37s. Time estimates for 10 more iterations: 10m 58s, 100 more iterations: 1h 49m 43s, 500 more iterations: 9h 8m 36s. [2025-11-13 05:30:57,229][__main__][INFO] - Starting iteration 492. [2025-11-13 05:30:57,760][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 49 and human policies 1. [2025-11-13 05:30:57,761][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 05:31:07,823][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:31:14,805][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:31:18,131][__main__][INFO] - Number of regex retries in iteration 492: 2 [2025-11-13 05:31:18,132][__main__][INFO] - agents played in iteration 492 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 05:31:19,100][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:31:19,123][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:31:19,146][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:31:19,168][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:31:19,168][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 05:31:19,169][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 05:31:19,967][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 05:31:20,425][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 05:31:20,934][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 05:31:21,439][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 05:31:21,942][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 05:31:22,442][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 05:31:22,943][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 05:31:23,444][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 05:31:23,957][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 05:31:24,458][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 05:31:24,960][mllm.training.trainer_common][INFO] - 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Processing mini-batch 21 of 64 [2025-11-13 05:31:30,988][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 05:31:31,491][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 05:31:31,995][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 05:31:32,498][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 05:31:33,001][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 05:31:33,501][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 05:31:34,002][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 05:31:34,504][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 05:31:35,004][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 05:31:35,506][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 05:31:36,009][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 05:31:36,513][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 05:31:37,023][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 05:31:37,526][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 05:31:38,030][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 05:31:38,535][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 05:31:39,039][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 05:31:39,551][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 05:31:40,055][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 05:31:40,559][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 05:31:41,061][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 05:31:41,563][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 05:31:42,064][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 05:31:42,565][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 05:31:43,067][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 05:31:43,573][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 05:31:44,076][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 05:31:44,578][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 05:31:45,080][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 05:31:45,582][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 05:31:46,084][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 05:31:46,585][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 05:31:47,085][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 05:31:47,588][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 05:31:48,088][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 05:31:48,587][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 05:31:49,087][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 05:31:49,587][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 05:31:50,093][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 05:31:50,596][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 05:31:51,100][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 05:31:51,603][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 05:31:52,109][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10877 tokens. [2025-11-13 05:31:52,950][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.19%, Current % of VRAM taken: 59.64%, Block Peak % of device VRAM: 62.52%, ΔTime: 00:00:32 [2025-11-13 05:31:53,716][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 05:31:53,717][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 05:31:53,719][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 05:31:54,752][__main__][INFO] - Iteration 493 took 56s (35.74% Gen, 62.44% Train). Generation: 20s, Training: 35s. Estimated remaining time: 40h 7m 25s. Estimated total time: 47h 29m 38s. Time estimates for 10 more iterations: 9m 29s, 100 more iterations: 1h 34m 59s, 500 more iterations: 7h 54m 56s. [2025-11-13 05:31:54,754][__main__][INFO] - Starting iteration 493. [2025-11-13 05:31:55,262][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 49 and human policies 1. [2025-11-13 05:31:55,263][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 05:32:15,794][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:32:17,315][__main__][INFO] - Number of regex retries in iteration 493: 1 [2025-11-13 05:32:17,315][__main__][INFO] - agents played in iteration 493 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 05:32:18,253][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:32:18,280][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:32:18,305][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:32:18,327][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:32:18,328][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 05:32:18,329][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 05:32:19,057][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 05:32:19,514][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 05:32:20,022][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 05:32:20,529][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 05:32:21,033][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 05:32:21,538][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 05:32:22,044][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 05:32:22,545][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 05:32:23,065][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 05:32:23,569][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 05:32:24,071][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 05:32:24,572][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 05:32:25,074][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 05:32:25,580][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 05:32:26,085][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 05:32:26,587][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 05:32:27,090][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 05:32:27,594][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 05:32:28,097][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 05:32:28,605][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 05:32:29,110][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 05:32:29,613][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 05:32:35,669][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 05:32:36,175][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 05:32:36,678][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 05:32:37,179][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 05:32:37,683][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 05:32:38,184][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 05:32:38,687][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 05:32:39,190][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 05:32:39,691][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 05:32:40,193][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 05:32:40,697][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 05:32:41,198][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 05:32:41,701][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 05:32:42,201][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 05:32:42,701][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 05:32:43,202][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 05:32:43,704][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 05:32:44,209][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 05:32:44,710][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 05:32:45,212][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 05:32:45,716][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 05:32:46,218][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 05:32:46,719][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 05:32:47,234][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 05:32:47,734][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 05:32:48,235][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 05:32:48,735][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 05:32:49,235][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 05:32:49,750][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 05:32:50,253][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 05:32:50,759][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 05:32:51,266][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10879 tokens. [2025-11-13 05:32:52,038][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.20%, Current % of VRAM taken: 59.65%, Block Peak % of device VRAM: 62.41%, ΔTime: 00:00:32 [2025-11-13 05:32:52,689][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 05:32:52,690][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 05:32:52,692][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 05:32:53,732][__main__][INFO] - Iteration 494 took 58s (37.71% Gen, 60.50% Train). Generation: 22s, Training: 35s. Estimated remaining time: 41h 20m 18s. Estimated total time: 48h 43m 30s. Time estimates for 10 more iterations: 9m 44s, 100 more iterations: 1h 37m 27s, 500 more iterations: 8h 7m 15s. [2025-11-13 05:32:53,734][__main__][INFO] - Starting iteration 494. [2025-11-13 05:32:54,242][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 49 and human policies 1. [2025-11-13 05:32:54,242][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 05:33:08,216][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:33:11,890][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:33:17,228][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and Bob's values, it's clear that both of us value books and balls the most, while hats are of lower value for both of us. However, since the values for hats are the same for both of us, we can't expect to get an advantage in that category. To maximize our points, we should ensure we get as many books and balls as possible while being mindful of not overproposing, as that could lead to a proportional allocation which might result in us getting less than our fair share. Let's propose to take all 10 books and 10 balls, and leave 10 hats for Bob since we both value hats the least. This proposal should maximize our points for books and balls while not overproposing and risking a proportional allocation. Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:33:20,147][__main__][INFO] - Number of regex retries in iteration 494: 3 [2025-11-13 05:33:20,148][__main__][INFO] - agents played in iteration 494 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 05:33:20,981][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:33:21,009][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:33:21,037][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:33:21,062][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:33:21,063][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 05:33:21,064][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. 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Processing mini-batch 43 of 64 [2025-11-13 05:33:44,009][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 05:33:44,509][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 05:33:45,010][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 05:33:45,511][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 05:33:46,011][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 05:33:46,515][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 05:33:47,016][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 05:33:47,517][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 05:33:48,017][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 05:33:48,517][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 05:33:49,018][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 05:33:49,521][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 05:33:50,020][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 05:33:50,522][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 05:33:51,023][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 05:33:51,524][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 05:33:52,025][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 05:33:52,526][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 05:33:53,026][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 05:33:53,533][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 05:33:54,035][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10875 tokens. [2025-11-13 05:33:54,772][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.27%, Current % of VRAM taken: 59.72%, Block Peak % of device VRAM: 62.34%, ΔTime: 00:00:32 [2025-11-13 05:33:55,522][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 05:33:55,523][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 05:33:55,525][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 05:33:56,537][__main__][INFO] - Iteration 495 took 1m 2s (41.58% Gen, 56.79% Train). Generation: 25s, Training: 35s. Estimated remaining time: 44h 30m 35s. Estimated total time: 51h 54m 50s. Time estimates for 10 more iterations: 10m 22s, 100 more iterations: 1h 43m 49s, 500 more iterations: 8h 39m 8s. [2025-11-13 05:33:56,539][__main__][INFO] - Starting iteration 495. [2025-11-13 05:33:57,022][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 49 and human policies 1. [2025-11-13 05:33:57,022][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 05:34:18,390][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:34:22,920][__main__][INFO] - Number of regex retries in iteration 495: 1 [2025-11-13 05:34:22,921][__main__][INFO] - agents played in iteration 495 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 05:34:23,796][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:34:23,824][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:34:23,848][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:34:23,872][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:34:23,872][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 05:34:23,873][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 05:34:24,671][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 05:34:25,133][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 05:34:25,645][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 05:34:26,161][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 05:34:26,663][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 05:34:27,165][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 05:34:27,667][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 05:34:28,168][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 05:34:28,689][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 05:34:29,198][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 05:34:29,702][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 05:34:41,307][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 05:34:41,811][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 05:34:42,314][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 05:34:42,818][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 05:34:43,320][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 05:34:43,823][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 05:34:44,326][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 05:34:44,827][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 05:34:45,327][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 05:34:45,834][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 05:34:46,335][mllm.training.trainer_common][INFO] - 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Processing mini-batch 54 of 64 [2025-11-13 05:34:52,414][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 05:34:52,917][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 05:34:53,419][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 05:34:53,920][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 05:34:54,424][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 05:34:54,935][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 05:34:55,440][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 05:34:55,944][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 05:34:56,453][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 05:34:56,959][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10876 tokens. [2025-11-13 05:34:57,785][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.22%, Current % of VRAM taken: 59.67%, Block Peak % of device VRAM: 62.41%, ΔTime: 00:00:33 [2025-11-13 05:34:58,417][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 05:34:58,419][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 05:34:58,420][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 05:34:59,398][__main__][INFO] - Iteration 496 took 1m 2s (41.52% Gen, 56.91% Train). Generation: 25s, Training: 35s. Estimated remaining time: 44h 33m 32s. Estimated total time: 51h 58m 50s. Time estimates for 10 more iterations: 10m 23s, 100 more iterations: 1h 43m 57s, 500 more iterations: 8h 39m 48s. [2025-11-13 05:34:59,400][__main__][INFO] - Starting iteration 496. [2025-11-13 05:34:59,896][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 49 and human policies 1. [2025-11-13 05:34:59,899][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 05:35:19,567][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:35:22,903][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:35:22,997][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:35:28,431][__main__][INFO] - Number of regex retries in iteration 496: 3 [2025-11-13 05:35:28,431][__main__][INFO] - agents played in iteration 496 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 05:35:29,299][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:35:29,324][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:35:29,348][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:35:29,370][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:35:29,371][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 05:35:29,371][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 05:35:30,154][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 05:35:30,614][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 05:35:31,122][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 05:35:31,629][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 05:35:32,131][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 05:35:32,635][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 05:35:33,138][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 05:35:33,640][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 05:35:34,147][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 05:35:34,652][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 05:35:35,156][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 05:35:35,661][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 05:35:36,165][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 05:35:36,669][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 05:35:37,172][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 05:35:37,674][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 05:35:38,183][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 05:35:38,685][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 05:35:39,186][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 05:35:39,697][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 05:35:40,201][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 05:35:40,714][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 05:35:41,218][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 05:35:41,722][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 05:35:42,230][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 05:35:42,731][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 05:35:43,233][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 05:35:43,733][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 05:35:44,234][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 05:35:44,736][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 05:35:45,238][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 05:35:45,742][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 05:35:46,248][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 05:35:46,749][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 05:35:47,249][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 05:35:47,750][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 05:35:48,250][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 05:35:48,751][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 05:35:49,251][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 05:35:49,750][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 05:35:50,252][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 05:35:50,755][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 05:35:51,257][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 05:35:51,759][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 05:35:52,261][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 05:35:52,766][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 05:35:53,268][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 05:35:53,769][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 05:35:54,272][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 05:35:54,774][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 05:35:55,276][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 05:35:55,789][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 05:35:56,290][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 05:35:56,796][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 05:35:57,298][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 05:35:57,799][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 05:35:58,302][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 05:35:58,803][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 05:35:59,303][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 05:35:59,804][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 05:36:00,306][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 05:36:00,821][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 05:36:01,322][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 05:36:01,825][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 05:36:02,330][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10877 tokens. [2025-11-13 05:36:03,141][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.19%, Current % of VRAM taken: 59.65%, Block Peak % of device VRAM: 62.34%, ΔTime: 00:00:32 [2025-11-13 05:36:03,890][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 05:36:03,892][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 05:36:03,894][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 05:36:04,890][__main__][INFO] - Iteration 497 took 1m 4s (43.90% Gen, 54.56% Train). Generation: 28s, Training: 35s. Estimated remaining time: 46h 43m 22s. Estimated total time: 54h 9m 45s. Time estimates for 10 more iterations: 10m 49s, 100 more iterations: 1h 48m 19s, 500 more iterations: 9h 1m 37s. [2025-11-13 05:36:04,892][__main__][INFO] - Starting iteration 497. [2025-11-13 05:36:05,388][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 49 and human policies 1. [2025-11-13 05:36:05,389][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 05:36:19,469][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:36:25,206][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:36:29,121][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:36:30,015][__main__][INFO] - Number of regex retries in iteration 497: 3 [2025-11-13 05:36:30,016][__main__][INFO] - agents played in iteration 497 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 05:36:30,879][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:36:30,903][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:36:30,933][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:36:30,962][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:36:30,963][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 05:36:30,965][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 05:36:31,760][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 05:36:32,228][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 05:36:32,747][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 05:36:33,253][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 05:36:33,768][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 05:36:34,277][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 05:36:34,780][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 05:36:35,311][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 05:36:35,812][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 05:36:36,316][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 05:36:36,816][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 05:36:37,318][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 05:36:37,830][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 05:36:38,332][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 05:36:38,834][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 05:36:39,338][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 05:36:39,841][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 05:36:40,349][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 05:36:40,853][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 05:36:41,354][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 05:36:41,861][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 05:36:42,365][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 05:36:42,868][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 05:36:43,371][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 05:36:43,874][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 05:36:44,376][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 05:36:44,878][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 05:36:45,380][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 05:36:45,893][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 05:36:46,394][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 05:36:46,906][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 05:36:47,407][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 05:36:47,910][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 05:36:48,426][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 05:36:48,927][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 05:36:49,429][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 05:36:49,926][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 05:36:50,425][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 05:36:50,928][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 05:36:51,429][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 05:36:51,928][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 05:36:52,427][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 05:36:52,929][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 05:36:53,433][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 05:36:53,933][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 05:36:54,433][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 05:36:54,940][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 05:36:55,441][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 05:36:55,943][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 05:36:56,444][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 05:36:56,944][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 05:36:57,448][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 05:36:57,948][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 05:36:58,449][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 05:36:58,951][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 05:36:59,451][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 05:36:59,951][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 05:37:00,453][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 05:37:00,954][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 05:37:01,455][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 05:37:01,956][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 05:37:02,460][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 05:37:02,963][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 05:37:03,464][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 05:37:03,965][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10879 tokens. [2025-11-13 05:37:04,730][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.20%, Current % of VRAM taken: 59.66%, Block Peak % of device VRAM: 62.41%, ΔTime: 00:00:32 [2025-11-13 05:37:05,390][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 05:37:05,391][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 05:37:05,393][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 05:37:06,302][__main__][INFO] - Iteration 498 took 1m 0s (40.43% Gen, 58.08% Train). Generation: 24s, Training: 35s. Estimated remaining time: 43h 18m 19s. Estimated total time: 50h 45m 43s. Time estimates for 10 more iterations: 10m 9s, 100 more iterations: 1h 41m 31s, 500 more iterations: 8h 27m 37s. [2025-11-13 05:37:06,305][__main__][INFO] - Starting iteration 498. [2025-11-13 05:37:06,894][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 49 and human policies 1. [2025-11-13 05:37:06,895][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 05:37:22,993][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:37:25,996][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:37:28,847][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:37:33,900][__main__][INFO] - Number of regex retries in iteration 498: 3 [2025-11-13 05:37:33,901][__main__][INFO] - agents played in iteration 498 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 05:37:34,775][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:37:34,802][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:37:34,827][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:37:34,850][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:37:34,851][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 05:37:34,852][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 05:37:35,817][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 05:37:36,284][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 05:37:36,805][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 05:37:37,315][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 05:37:37,825][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 05:37:38,340][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 05:37:38,848][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 05:37:39,360][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 05:37:39,873][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 05:37:40,382][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 05:37:40,890][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 05:37:41,391][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 05:37:41,894][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 05:37:42,405][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 05:37:42,906][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 05:37:43,420][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 05:37:43,922][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 05:37:44,426][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 05:37:44,927][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 05:37:45,426][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 05:37:45,930][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 05:37:46,434][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 05:37:46,939][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 05:37:47,444][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 05:37:47,946][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 05:37:48,449][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 05:37:48,950][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 05:37:49,452][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 05:37:49,954][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 05:37:50,454][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 05:37:50,955][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 05:37:51,460][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 05:37:51,962][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 05:37:52,466][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 05:37:52,971][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 05:37:53,472][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 05:37:53,978][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 05:37:54,482][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 05:37:54,984][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 05:37:55,502][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 05:37:56,009][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 05:37:56,522][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 05:37:57,023][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 05:37:57,525][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 05:37:58,026][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 05:37:58,526][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 05:37:59,028][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 05:37:59,528][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 05:38:00,028][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 05:38:00,543][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 05:38:01,044][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 05:38:01,546][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 05:38:02,051][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 05:38:02,549][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 05:38:03,054][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 05:38:03,555][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 05:38:04,057][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 05:38:04,561][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 05:38:05,061][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 05:38:05,562][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 05:38:06,062][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 05:38:06,564][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 05:38:07,067][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 05:38:07,568][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 05:38:08,069][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10876 tokens. [2025-11-13 05:38:08,835][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.17%, Current % of VRAM taken: 59.63%, Block Peak % of device VRAM: 62.28%, ΔTime: 00:00:33 [2025-11-13 05:38:09,596][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 05:38:09,598][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 05:38:09,599][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 05:38:10,599][__main__][INFO] - Iteration 499 took 1m 3s (42.39% Gen, 56.04% Train). Generation: 27s, Training: 35s. Estimated remaining time: 45h 36m 46s. Estimated total time: 53h 5m 15s. Time estimates for 10 more iterations: 10m 37s, 100 more iterations: 1h 46m 10s, 500 more iterations: 8h 50m 52s. [2025-11-13 05:38:10,601][__main__][INFO] - Starting iteration 499. [2025-11-13 05:38:11,129][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 49 and human policies 1. [2025-11-13 05:38:11,130][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 05:38:26,877][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:38:28,368][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:38:29,256][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:38:30,178][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:38:33,568][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:38:36,189][__main__][INFO] - Number of regex retries in iteration 499: 5 [2025-11-13 05:38:36,190][__main__][INFO] - agents played in iteration 499 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 05:38:37,163][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:38:37,201][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:38:37,231][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:38:37,259][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:38:37,259][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 05:38:37,260][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 05:38:38,058][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 05:38:38,537][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 05:38:39,055][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 05:38:39,569][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 05:38:40,082][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 05:38:40,596][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 05:38:41,124][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 05:38:41,634][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 05:38:42,151][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 05:38:42,664][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 05:38:43,177][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 05:38:43,688][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 05:38:44,201][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 05:38:44,716][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 05:38:45,227][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 05:38:45,739][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 05:38:46,249][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 05:38:46,764][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 05:38:47,281][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 05:38:47,796][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 05:38:48,309][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 05:38:48,820][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 05:38:49,332][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 05:38:49,845][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 05:38:50,359][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 05:38:50,869][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 05:38:51,381][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 05:38:51,891][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 05:38:52,408][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 05:38:52,931][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 05:38:53,443][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 05:38:53,953][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 05:38:54,471][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 05:38:54,981][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 05:38:55,493][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 05:38:56,004][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 05:38:56,518][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 05:38:57,031][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 05:38:57,537][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 05:38:58,049][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 05:38:58,557][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 05:38:59,064][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 05:38:59,570][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 05:39:00,074][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 05:39:00,579][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 05:39:01,083][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 05:39:01,586][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 05:39:02,091][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 05:39:02,598][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 05:39:03,108][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 05:39:03,618][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 05:39:04,123][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 05:39:04,635][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 05:39:05,139][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 05:39:05,644][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 05:39:06,152][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 05:39:06,656][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 05:39:07,161][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 05:39:07,665][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 05:39:08,168][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 05:39:08,673][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 05:39:09,177][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 05:39:09,681][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 05:39:10,184][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 05:39:10,688][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10877 tokens. [2025-11-13 05:39:11,495][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.23%, Current % of VRAM taken: 59.69%, Block Peak % of device VRAM: 62.42%, ΔTime: 00:00:33 [2025-11-13 05:39:12,125][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 05:39:12,127][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 05:39:12,128][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 05:39:13,104][__main__][INFO] - Iteration 500 took 1m 1s (40.44% Gen, 57.99% Train). Generation: 25s, Training: 35s. Estimated remaining time: 44h 9m 15s. Estimated total time: 51h 38m 47s. Time estimates for 10 more iterations: 10m 19s, 100 more iterations: 1h 43m 17s, 500 more iterations: 8h 36m 27s. [2025-11-13 05:39:13,107][__main__][INFO] - Starting iteration 500. [2025-11-13 05:39:13,590][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 49 and human policies 1. [2025-11-13 05:39:13,591][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 05:39:30,438][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:39:30,440][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:39:41,205][__main__][INFO] - Number of regex retries in iteration 500: 2 [2025-11-13 05:39:41,206][__main__][INFO] - agents played in iteration 500 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 05:39:42,030][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:39:42,055][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:39:42,081][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:39:42,104][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:39:42,104][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 05:39:42,105][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 05:39:42,917][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 05:39:43,377][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 05:39:43,889][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 05:39:44,395][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 05:39:44,904][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 05:39:45,409][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 05:39:45,915][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 05:39:46,422][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 05:39:46,945][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 05:39:47,451][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 05:39:47,957][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 05:39:48,464][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 05:39:48,974][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 05:39:49,485][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 05:39:49,991][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 05:39:50,501][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 05:39:51,024][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 05:39:51,531][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 05:39:52,048][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 05:39:52,556][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 05:39:53,597][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 05:39:54,861][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 05:39:55,374][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 05:39:55,883][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 05:39:56,392][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 05:39:56,903][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 05:39:57,422][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 05:39:57,931][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 05:39:58,440][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 05:39:58,948][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 05:39:59,457][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 05:39:59,968][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 05:40:00,477][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 05:40:00,989][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 05:40:01,497][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 05:40:02,006][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 05:40:02,525][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 05:40:03,034][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 05:40:03,545][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 05:40:04,055][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 05:40:04,565][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 05:40:05,075][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 05:40:05,582][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 05:40:06,083][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 05:40:06,592][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 05:40:07,096][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 05:40:07,597][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 05:40:08,101][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 05:40:08,603][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 05:40:09,107][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 05:40:09,610][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 05:40:10,112][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 05:40:10,628][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 05:40:11,132][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 05:40:11,645][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 05:40:12,148][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 05:40:12,651][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 05:40:13,163][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 05:40:13,665][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 05:40:14,168][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 05:40:14,667][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 05:40:15,169][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 05:40:15,670][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 05:40:16,171][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 05:40:16,673][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10879 tokens. [2025-11-13 05:40:17,473][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.20%, Current % of VRAM taken: 59.65%, Block Peak % of device VRAM: 62.31%, ΔTime: 00:00:34 [2025-11-13 05:40:18,130][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 05:40:18,132][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 05:40:18,133][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 05:40:20,403][__main__][INFO] - Iteration 501 took 1m 6s (41.33% Gen, 55.27% Train). Generation: 27s, Training: 36s. Estimated remaining time: 48h 10m 1s. Estimated total time: 55h 40m 40s. Time estimates for 10 more iterations: 11m 8s, 100 more iterations: 1h 51m 21s, 500 more iterations: 9h 16m 46s. [2025-11-13 05:40:20,406][__main__][INFO] - Starting iteration 501. [2025-11-13 05:40:20,961][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 50 and human policies 1. [2025-11-13 05:40:20,961][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 05:40:39,287][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:40:40,174][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:40:42,594][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:40:43,162][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:40:49,955][__main__][INFO] - Number of regex retries in iteration 501: 4 [2025-11-13 05:40:49,955][__main__][INFO] - agents played in iteration 501 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 05:40:50,744][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:40:50,767][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:40:50,789][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:40:50,813][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:40:50,814][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 05:40:50,815][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 05:40:51,574][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 05:40:52,034][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 05:40:52,547][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 05:40:53,052][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 05:40:53,558][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 05:40:54,065][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 05:40:54,572][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 05:40:55,089][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 05:40:55,601][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 05:40:56,114][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 05:40:56,626][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 05:40:57,135][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 05:40:57,647][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 05:40:58,156][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 05:40:58,666][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 05:40:59,178][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 05:40:59,687][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 05:41:00,200][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 05:41:00,708][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 05:41:01,219][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 05:41:01,730][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 05:41:02,237][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 05:41:08,375][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 05:41:08,878][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 05:41:09,386][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 05:41:09,889][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 05:41:10,392][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 05:41:10,895][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 05:41:11,398][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 05:41:11,900][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 05:41:12,406][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 05:41:12,909][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 05:41:13,413][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 05:41:13,916][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 05:41:14,420][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 05:41:14,923][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 05:41:15,426][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 05:41:15,931][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 05:41:16,433][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 05:41:16,937][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 05:41:17,450][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 05:41:17,953][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 05:41:18,465][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 05:41:18,968][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 05:41:19,475][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 05:41:19,986][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 05:41:20,491][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 05:41:20,994][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 05:41:21,499][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 05:41:21,998][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 05:41:22,500][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 05:41:23,002][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 05:41:23,503][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 05:41:24,007][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10877 tokens. [2025-11-13 05:41:24,770][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.19%, Current % of VRAM taken: 59.65%, Block Peak % of device VRAM: 62.35%, ΔTime: 00:00:33 [2025-11-13 05:41:25,558][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 05:41:25,559][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 05:41:25,561][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 05:41:26,566][__main__][INFO] - Iteration 502 took 1m 5s (44.19% Gen, 54.27% Train). Generation: 28s, Training: 35s. Estimated remaining time: 47h 8m 33s. Estimated total time: 54h 40m 18s. Time estimates for 10 more iterations: 10m 56s, 100 more iterations: 1h 49m 20s, 500 more iterations: 9h 6m 43s. [2025-11-13 05:41:26,568][__main__][INFO] - Starting iteration 502. [2025-11-13 05:41:27,059][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 50 and human policies 1. [2025-11-13 05:41:27,060][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 05:41:46,206][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:41:46,211][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:41:49,020][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:41:55,138][__main__][INFO] - Number of regex retries in iteration 502: 3 [2025-11-13 05:41:55,139][__main__][INFO] - agents played in iteration 502 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 05:41:56,151][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:41:56,179][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:41:56,205][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:41:56,229][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:41:56,230][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 05:41:56,231][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 05:41:56,996][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 05:41:57,472][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 05:41:57,990][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 05:41:58,499][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 05:41:59,011][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 05:41:59,517][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 05:42:00,028][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 05:42:00,536][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 05:42:01,045][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 05:42:01,573][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 05:42:02,084][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 05:42:02,595][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 05:42:03,102][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 05:42:03,613][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 05:42:04,123][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 05:42:04,632][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 05:42:05,141][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 05:42:05,650][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 05:42:06,160][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 05:42:06,686][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 05:42:07,195][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 05:42:07,705][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 05:42:08,218][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 05:42:08,726][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 05:42:09,233][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 05:42:09,745][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 05:42:10,254][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 05:42:10,766][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 05:42:11,276][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 05:42:11,785][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 05:42:12,295][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 05:42:12,804][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 05:42:13,317][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 05:42:13,850][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 05:42:14,358][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 05:42:14,869][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 05:42:15,376][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 05:42:15,901][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 05:42:16,411][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 05:42:16,922][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 05:42:17,432][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 05:42:17,940][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 05:42:18,452][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 05:42:18,959][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 05:42:19,468][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 05:42:19,982][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 05:42:20,484][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 05:42:20,996][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 05:42:21,500][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 05:42:22,002][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 05:42:22,506][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 05:42:23,008][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 05:42:23,513][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 05:42:24,016][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 05:42:24,519][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 05:42:25,025][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 05:42:25,530][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 05:42:26,034][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 05:42:26,541][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 05:42:27,047][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 05:42:27,553][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 05:42:28,057][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 05:42:28,565][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 05:42:29,086][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 05:42:29,597][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10878 tokens. [2025-11-13 05:42:30,440][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.26%, Current % of VRAM taken: 59.71%, Block Peak % of device VRAM: 62.41%, ΔTime: 00:00:33 [2025-11-13 05:42:31,094][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 05:42:31,096][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 05:42:31,099][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 05:42:32,028][__main__][INFO] - Iteration 503 took 1m 4s (43.22% Gen, 55.35% Train). Generation: 28s, Training: 35s. Estimated remaining time: 46h 35m 39s. Estimated total time: 54h 8m 29s. Time estimates for 10 more iterations: 10m 49s, 100 more iterations: 1h 48m 16s, 500 more iterations: 9h 1m 24s. [2025-11-13 05:42:32,030][__main__][INFO] - Starting iteration 503. [2025-11-13 05:42:32,537][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 50 and human policies 1. [2025-11-13 05:42:32,538][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 05:42:52,212][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:42:52,386][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:42:54,361][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:42:55,488][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:43:00,505][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:43:01,456][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:43:03,809][__main__][INFO] - Number of regex retries in iteration 503: 6 [2025-11-13 05:43:03,810][__main__][INFO] - agents played in iteration 503 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 05:43:04,644][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:43:04,672][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:43:04,698][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:43:04,722][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:43:04,722][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 05:43:04,723][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 05:43:05,412][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 05:43:05,870][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 05:43:06,378][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 05:43:06,882][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 05:43:07,387][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 05:43:07,890][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 05:43:08,408][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 05:43:08,911][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 05:43:09,416][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 05:43:09,929][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 05:43:10,439][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 05:43:10,948][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 05:43:11,459][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 05:43:11,972][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 05:43:12,484][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 05:43:12,994][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 05:43:13,509][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 05:43:14,018][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 05:43:14,526][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 05:43:15,035][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 05:43:15,544][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 05:43:16,052][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 05:43:16,561][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 05:43:17,070][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 05:43:17,583][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 05:43:18,090][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 05:43:18,599][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 05:43:19,105][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 05:43:19,613][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 05:43:20,136][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 05:43:20,643][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 05:43:21,150][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 05:43:21,661][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 05:43:22,165][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 05:43:22,675][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 05:43:23,183][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 05:43:23,690][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 05:43:24,201][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 05:43:24,711][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 05:43:25,226][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 05:43:25,732][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 05:43:26,230][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 05:43:26,744][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 05:43:27,245][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 05:43:27,745][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 05:43:28,245][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 05:43:28,748][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 05:43:29,253][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 05:43:29,754][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 05:43:30,255][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 05:43:30,764][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 05:43:31,268][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 05:43:31,773][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 05:43:32,283][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 05:43:32,790][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 05:43:33,299][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 05:43:33,805][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 05:43:34,308][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 05:43:34,816][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 05:43:35,330][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 05:43:35,840][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 05:43:36,351][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 05:43:36,856][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 05:43:37,366][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 05:43:37,877][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10876 tokens. [2025-11-13 05:43:38,799][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.18%, Current % of VRAM taken: 59.63%, Block Peak % of device VRAM: 62.34%, ΔTime: 00:00:33 [2025-11-13 05:43:39,526][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 05:43:39,527][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 05:43:39,529][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 05:43:40,698][__main__][INFO] - Iteration 504 took 1m 8s (45.88% Gen, 52.41% Train). Generation: 31s, Training: 35s. Estimated remaining time: 49h 14m 4s. Estimated total time: 56h 48m 3s. Time estimates for 10 more iterations: 11m 21s, 100 more iterations: 1h 53m 36s, 500 more iterations: 9h 28m 0s. [2025-11-13 05:43:40,700][__main__][INFO] - Starting iteration 504. [2025-11-13 05:43:41,220][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 50 and human policies 1. [2025-11-13 05:43:41,221][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 05:43:57,702][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:43:58,201][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:44:00,624][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:44:02,456][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:44:02,949][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:44:05,419][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:44:07,656][__main__][INFO] - Number of regex retries in iteration 504: 6 [2025-11-13 05:44:07,656][__main__][INFO] - agents played in iteration 504 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 05:44:08,503][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:44:08,525][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:44:08,548][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:44:08,570][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:44:08,570][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 05:44:08,571][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 05:44:09,286][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 05:44:09,745][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 05:44:10,256][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 05:44:10,758][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 05:44:11,261][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 05:44:11,765][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 05:44:12,268][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 05:44:12,772][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 05:44:13,276][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 05:44:13,781][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 05:44:14,287][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 05:44:14,790][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 05:44:15,293][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 05:44:15,802][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 05:44:16,309][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 05:44:16,829][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 05:44:17,337][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 05:44:17,847][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 05:44:18,361][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 05:44:18,869][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 05:44:19,378][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 05:44:19,887][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 05:44:20,397][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 05:44:20,921][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 05:44:21,429][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 05:44:21,937][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 05:44:22,445][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 05:44:22,952][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 05:44:23,466][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 05:44:23,974][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 05:44:24,482][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 05:44:24,989][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 05:44:25,497][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 05:44:26,006][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 05:44:26,514][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 05:44:27,022][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 05:44:27,535][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 05:44:28,043][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 05:44:28,554][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 05:44:29,059][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 05:44:29,570][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 05:44:30,081][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 05:44:30,590][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 05:44:31,098][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 05:44:31,607][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 05:44:32,119][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 05:44:32,649][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 05:44:33,157][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 05:44:33,670][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 05:44:34,183][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 05:44:34,693][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 05:44:35,204][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 05:44:35,713][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 05:44:36,225][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 05:44:36,741][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 05:44:37,253][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 05:44:37,769][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 05:44:38,283][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 05:44:38,796][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 05:44:39,313][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 05:44:39,824][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 05:44:40,338][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 05:44:40,855][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 05:44:41,371][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 05:44:41,890][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10878 tokens. [2025-11-13 05:44:42,770][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.27%, Current % of VRAM taken: 59.72%, Block Peak % of device VRAM: 62.41%, ΔTime: 00:00:33 [2025-11-13 05:44:43,432][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 05:44:43,434][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 05:44:43,436][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 05:44:44,326][__main__][INFO] - Iteration 505 took 1m 3s (41.89% Gen, 56.70% Train). Generation: 26s, Training: 35s. Estimated remaining time: 45h 0m 18s. Estimated total time: 52h 35m 21s. Time estimates for 10 more iterations: 10m 31s, 100 more iterations: 1h 45m 10s, 500 more iterations: 8h 45m 53s. [2025-11-13 05:44:44,329][__main__][INFO] - Starting iteration 505. [2025-11-13 05:44:44,838][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 50 and human policies 1. [2025-11-13 05:44:44,838][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 05:45:01,502][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:45:02,308][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:45:04,503][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:45:05,682][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:45:08,990][__main__][INFO] - Number of regex retries in iteration 505: 4 [2025-11-13 05:45:08,991][__main__][INFO] - agents played in iteration 505 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 05:45:09,779][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:45:09,809][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:45:09,835][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:45:09,859][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:45:09,860][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 05:45:09,861][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 05:45:10,563][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 05:45:11,023][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 05:45:11,527][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 05:45:12,027][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 05:45:12,527][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 05:45:13,027][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 05:45:13,526][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 05:45:14,028][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 05:45:14,528][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 05:45:15,040][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 05:45:15,544][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 05:45:27,119][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 05:45:27,620][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 05:45:28,121][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 05:45:28,623][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 05:45:29,127][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 05:45:29,632][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 05:45:30,137][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 05:45:30,661][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 05:45:31,165][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 05:45:31,670][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 05:45:32,169][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 05:45:32,676][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 05:45:33,188][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 05:45:33,697][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 05:45:34,204][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 05:45:34,713][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 05:45:35,224][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 05:45:35,736][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 05:45:36,245][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 05:45:36,755][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 05:45:37,269][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 05:45:37,782][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 05:45:38,293][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 05:45:38,801][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 05:45:39,311][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 05:45:39,821][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 05:45:40,335][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 05:45:40,843][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 05:45:41,358][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 05:45:41,869][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 05:45:42,384][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 05:45:42,894][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10877 tokens. [2025-11-13 05:45:43,786][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.20%, Current % of VRAM taken: 59.66%, Block Peak % of device VRAM: 62.29%, ΔTime: 00:00:33 [2025-11-13 05:45:44,507][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 05:45:44,509][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 05:45:44,511][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 05:45:45,663][__main__][INFO] - Iteration 506 took 1m 0s (39.71% Gen, 58.40% Train). Generation: 24s, Training: 35s. Estimated remaining time: 43h 5m 15s. Estimated total time: 50h 41m 19s. Time estimates for 10 more iterations: 10m 8s, 100 more iterations: 1h 41m 22s, 500 more iterations: 8h 26m 53s. [2025-11-13 05:45:45,666][__main__][INFO] - Starting iteration 506. [2025-11-13 05:45:46,164][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 50 and human policies 1. [2025-11-13 05:45:46,164][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 05:46:06,463][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:46:08,570][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:46:09,224][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:46:10,112][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:46:13,543][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:46:16,206][__main__][INFO] - Number of regex retries in iteration 506: 5 [2025-11-13 05:46:16,206][__main__][INFO] - agents played in iteration 506 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 05:46:17,076][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:46:17,102][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:46:17,128][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:46:17,152][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:46:17,152][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 05:46:17,154][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 05:46:17,870][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 05:46:18,329][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 05:46:18,836][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 05:46:19,340][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 05:46:19,843][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 05:46:20,348][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 05:46:20,852][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 05:46:21,355][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 05:46:21,883][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 05:46:22,381][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 05:46:22,885][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 05:46:23,384][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 05:46:23,886][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 05:46:24,390][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 05:46:24,893][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 05:46:25,394][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 05:46:25,898][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 05:46:26,398][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 05:46:26,899][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 05:46:27,399][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 05:46:27,899][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 05:46:28,402][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 05:46:28,904][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 05:46:29,405][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 05:46:29,907][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 05:46:30,408][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 05:46:30,910][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 05:46:31,411][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 05:46:31,911][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 05:46:32,413][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 05:46:32,917][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 05:46:33,420][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 05:46:33,924][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 05:46:34,428][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 05:46:34,932][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 05:46:35,433][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 05:46:35,944][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 05:46:36,468][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 05:46:36,976][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 05:46:37,491][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 05:46:38,003][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 05:46:38,516][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 05:46:39,029][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 05:46:39,542][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 05:46:40,062][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 05:46:40,573][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 05:46:41,086][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 05:46:41,604][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 05:46:42,119][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 05:46:42,630][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 05:46:43,139][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 05:46:43,651][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 05:46:44,179][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 05:46:44,689][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 05:46:45,202][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 05:46:45,714][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 05:46:46,225][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 05:46:46,745][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 05:46:47,263][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 05:46:47,775][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 05:46:48,290][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 05:46:48,802][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 05:46:49,317][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 05:46:49,832][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 05:46:50,348][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10874 tokens. [2025-11-13 05:46:51,245][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.26%, Current % of VRAM taken: 59.71%, Block Peak % of device VRAM: 62.32%, ΔTime: 00:00:33 [2025-11-13 05:46:51,912][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 05:46:51,914][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 05:46:51,916][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 05:46:52,766][__main__][INFO] - Iteration 507 took 1m 6s (45.10% Gen, 53.62% Train). Generation: 30s, Training: 35s. Estimated remaining time: 47h 52m 58s. Estimated total time: 55h 30m 9s. Time estimates for 10 more iterations: 11m 6s, 100 more iterations: 1h 51m 0s, 500 more iterations: 9h 15m 1s. [2025-11-13 05:46:52,768][__main__][INFO] - Starting iteration 507. [2025-11-13 05:46:53,302][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 50 and human policies 1. [2025-11-13 05:46:53,303][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 05:47:10,787][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:47:15,178][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:47:20,317][__main__][INFO] - Number of regex retries in iteration 507: 2 [2025-11-13 05:47:20,318][__main__][INFO] - agents played in iteration 507 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 05:47:21,166][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:47:21,192][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:47:21,217][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:47:21,239][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:47:21,240][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 05:47:21,241][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 05:47:21,939][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 05:47:22,400][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 05:47:22,908][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 05:47:23,413][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 05:47:23,916][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 05:47:24,419][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 05:47:24,924][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 05:47:25,427][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 05:47:25,931][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 05:47:26,433][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 05:47:26,935][mllm.training.trainer_common][INFO] - 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Processing mini-batch 21 of 64 [2025-11-13 05:47:33,001][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 05:47:33,507][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 05:47:34,010][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 05:47:34,512][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 05:47:35,011][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 05:47:35,513][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 05:47:36,014][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 05:47:36,520][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 05:47:37,022][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 05:47:37,524][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 05:47:38,028][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 05:47:38,535][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 05:47:39,041][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 05:47:39,546][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 05:47:40,052][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 05:47:40,568][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 05:47:41,073][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 05:47:41,587][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 05:47:42,098][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 05:47:42,607][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 05:47:43,124][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 05:47:43,641][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 05:47:44,157][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 05:47:44,671][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 05:47:45,183][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 05:47:45,717][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 05:47:46,230][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 05:47:46,737][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 05:47:47,247][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 05:47:47,756][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 05:47:48,273][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 05:47:48,789][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 05:47:49,302][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 05:47:49,821][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 05:47:50,333][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 05:47:50,847][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 05:47:51,361][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 05:47:51,876][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 05:47:52,403][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 05:47:52,911][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 05:47:53,424][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 05:47:53,936][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 05:47:54,452][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10874 tokens. [2025-11-13 05:47:55,337][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.21%, Current % of VRAM taken: 59.66%, Block Peak % of device VRAM: 62.35%, ΔTime: 00:00:33 [2025-11-13 05:47:56,065][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 05:47:56,068][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 05:47:56,070][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 05:47:57,155][__main__][INFO] - Iteration 508 took 1m 3s (42.31% Gen, 55.99% Train). Generation: 27s, Training: 35s. Estimated remaining time: 45h 34m 26s. Estimated total time: 53h 12m 42s. Time estimates for 10 more iterations: 10m 38s, 100 more iterations: 1h 46m 25s, 500 more iterations: 8h 52m 7s. [2025-11-13 05:47:57,158][__main__][INFO] - Starting iteration 508. [2025-11-13 05:47:57,678][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 50 and human policies 1. [2025-11-13 05:47:57,679][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 05:48:27,693][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:48:29,817][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:48:31,257][__main__][INFO] - Number of regex retries in iteration 508: 2 [2025-11-13 05:48:31,258][__main__][INFO] - agents played in iteration 508 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 05:48:32,096][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:48:32,123][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:48:32,149][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:48:32,173][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:48:32,173][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 05:48:32,174][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 05:48:32,919][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 05:48:33,380][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 05:48:33,888][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 05:48:34,408][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 05:48:34,913][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 05:48:35,417][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 05:48:35,919][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 05:48:36,423][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 05:48:36,928][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 05:48:37,431][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 05:48:37,933][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 05:48:38,435][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 05:48:38,936][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 05:48:39,437][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 05:48:39,938][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 05:48:40,440][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 05:48:40,945][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 05:48:41,446][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 05:48:41,948][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 05:48:42,455][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 05:48:42,962][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 05:48:43,467][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 05:48:43,983][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 05:48:44,489][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 05:48:45,012][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 05:48:45,522][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 05:48:46,034][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 05:48:46,555][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 05:48:47,065][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 05:48:47,575][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 05:48:48,086][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 05:48:48,594][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 05:48:49,116][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 05:48:49,631][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 05:48:50,142][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 05:48:50,655][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 05:48:51,169][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 05:48:51,698][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 05:48:52,213][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 05:48:52,728][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 05:48:53,244][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 05:48:53,758][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 05:48:54,280][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 05:48:54,794][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 05:48:55,311][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 05:48:55,829][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 05:48:56,341][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 05:48:56,854][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 05:48:57,367][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 05:48:57,880][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 05:48:58,412][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 05:48:58,927][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 05:48:59,442][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 05:48:59,957][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 05:49:00,469][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 05:49:00,986][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 05:49:01,498][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 05:49:02,013][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 05:49:02,525][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 05:49:03,040][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 05:49:03,559][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 05:49:04,074][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 05:49:04,587][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 05:49:05,104][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 05:49:05,618][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10874 tokens. [2025-11-13 05:49:06,476][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.22%, Current % of VRAM taken: 59.68%, Block Peak % of device VRAM: 62.32%, ΔTime: 00:00:33 [2025-11-13 05:49:07,144][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 05:49:07,146][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 05:49:07,148][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 05:49:08,056][__main__][INFO] - Iteration 509 took 1m 10s (47.71% Gen, 51.00% Train). Generation: 33s, Training: 35s. Estimated remaining time: 50h 59m 28s. Estimated total time: 58h 38m 54s. Time estimates for 10 more iterations: 11m 43s, 100 more iterations: 1h 57m 17s, 500 more iterations: 9h 46m 29s. [2025-11-13 05:49:08,058][__main__][INFO] - Starting iteration 509. [2025-11-13 05:49:08,569][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 50 and human policies 1. [2025-11-13 05:49:08,569][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 05:49:23,397][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:49:24,319][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:49:26,459][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:49:33,552][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:49:33,558][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:49:33,645][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:49:34,564][__main__][INFO] - Number of regex retries in iteration 509: 6 [2025-11-13 05:49:34,565][__main__][INFO] - agents played in iteration 509 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 05:49:35,528][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:49:35,553][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:49:35,578][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:49:35,602][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:49:35,602][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 05:49:35,603][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 05:49:36,322][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 05:49:36,782][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 05:49:37,289][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 05:49:37,795][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 05:49:38,297][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 05:49:38,809][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 05:49:39,311][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 05:49:39,814][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 05:49:40,326][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 05:49:40,827][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 05:49:41,329][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 05:49:52,936][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 05:49:53,446][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 05:49:53,956][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 05:49:54,469][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 05:49:54,978][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 05:49:55,488][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 05:49:56,003][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 05:49:56,513][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 05:49:57,022][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 05:49:57,533][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 05:49:58,039][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 05:49:58,558][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 05:49:59,080][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 05:49:59,593][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 05:50:00,109][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 05:50:00,616][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 05:50:01,135][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 05:50:02,739][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 05:50:03,392][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 05:50:03,905][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 05:50:04,420][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 05:50:04,935][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 05:50:05,449][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 05:50:05,964][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 05:50:06,477][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 05:50:06,988][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 05:50:07,500][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 05:50:08,018][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 05:50:08,531][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 05:50:09,045][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 05:50:09,559][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 05:50:10,074][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10877 tokens. [2025-11-13 05:50:10,971][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.27%, Current % of VRAM taken: 59.72%, Block Peak % of device VRAM: 62.41%, ΔTime: 00:00:34 [2025-11-13 05:50:11,639][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 05:50:11,641][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 05:50:11,643][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 05:50:12,607][__main__][INFO] - Iteration 510 took 1m 4s (40.59% Gen, 57.90% Train). Generation: 25s, Training: 37s. Estimated remaining time: 45h 41m 27s. Estimated total time: 53h 21m 58s. Time estimates for 10 more iterations: 10m 40s, 100 more iterations: 1h 46m 43s, 500 more iterations: 8h 53m 39s. [2025-11-13 05:50:12,609][__main__][INFO] - Starting iteration 510. [2025-11-13 05:50:13,129][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 50 and human policies 1. [2025-11-13 05:50:13,130][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 05:50:31,683][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:50:32,426][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:50:40,624][__main__][INFO] - Number of regex retries in iteration 510: 2 [2025-11-13 05:50:40,624][__main__][INFO] - agents played in iteration 510 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 05:50:41,541][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:50:41,567][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:50:41,591][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:50:41,614][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:50:41,614][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 05:50:41,615][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 05:50:42,336][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 05:50:42,795][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 05:50:43,306][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 05:50:43,809][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 05:50:44,312][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 05:50:44,813][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 05:50:45,314][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 05:50:45,825][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 05:50:46,326][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 05:50:46,827][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 05:50:47,338][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 05:50:58,956][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 05:50:59,466][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 05:50:59,977][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 05:51:00,488][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 05:51:00,996][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 05:51:01,511][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 05:51:02,022][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 05:51:02,532][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 05:51:03,046][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 05:51:03,555][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 05:51:04,065][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 05:51:04,575][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 05:51:05,084][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 05:51:05,596][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 05:51:06,107][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 05:51:06,620][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 05:51:07,133][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 05:51:07,681][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 05:51:08,199][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 05:51:08,716][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 05:51:09,232][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 05:51:09,746][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 05:51:10,264][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 05:51:10,783][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 05:51:11,297][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 05:51:11,808][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 05:51:12,324][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 05:51:12,837][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 05:51:13,351][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 05:51:13,865][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 05:51:14,377][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 05:51:14,894][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10874 tokens. [2025-11-13 05:51:15,763][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.27%, Current % of VRAM taken: 59.73%, Block Peak % of device VRAM: 62.57%, ΔTime: 00:00:33 [2025-11-13 05:51:16,534][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 05:51:16,537][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 05:51:16,538][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 05:51:18,726][__main__][INFO] - Iteration 511 took 1m 5s (41.91% Gen, 54.75% Train). Generation: 27s, Training: 35s. Estimated remaining time: 46h 58m 16s. Estimated total time: 54h 39m 53s. Time estimates for 10 more iterations: 10m 55s, 100 more iterations: 1h 49m 19s, 500 more iterations: 9h 6m 38s. [2025-11-13 05:51:18,729][__main__][INFO] - Starting iteration 511. [2025-11-13 05:51:19,231][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 51 and human policies 1. [2025-11-13 05:51:19,232][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 05:51:33,836][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:51:33,893][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:51:34,115][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:51:34,118][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:51:36,021][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:51:40,072][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:51:45,355][__main__][INFO] - Number of regex retries in iteration 511: 6 [2025-11-13 05:51:45,356][__main__][INFO] - agents played in iteration 511 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 05:51:46,188][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.62%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:51:46,215][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.62%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:51:46,242][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.62%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:51:46,265][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.62%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:51:46,265][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 05:51:46,266][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 05:51:46,963][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 05:51:47,419][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 05:51:47,928][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 05:51:48,428][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 05:51:48,939][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 05:51:49,435][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 05:51:49,933][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 05:51:50,434][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 05:51:50,933][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 05:51:51,436][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 05:51:51,935][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 05:51:52,436][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 05:51:52,951][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 05:51:53,452][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 05:51:53,953][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 05:51:54,453][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 05:51:54,953][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 05:51:55,459][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 05:51:56,011][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 05:51:56,514][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 05:51:57,024][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 05:51:57,526][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 05:51:58,039][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 05:51:58,540][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 05:51:59,044][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 05:51:59,557][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 05:52:00,056][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 05:52:00,562][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 05:52:01,067][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 05:52:01,568][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 05:52:02,073][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 05:52:02,578][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 05:52:03,090][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 05:52:03,596][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 05:52:04,105][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 05:52:04,641][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 05:52:05,149][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 05:52:05,658][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 05:52:06,171][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 05:52:06,680][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 05:52:07,190][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 05:52:07,697][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 05:52:08,206][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 05:52:08,732][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 05:52:09,246][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 05:52:09,757][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 05:52:10,269][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 05:52:10,778][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 05:52:11,369][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 05:52:12,944][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 05:52:13,453][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 05:52:13,969][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 05:52:14,480][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 05:52:14,992][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 05:52:15,505][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 05:52:16,018][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 05:52:16,547][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 05:52:17,054][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 05:52:17,563][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 05:52:18,077][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 05:52:18,588][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 05:52:19,100][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 05:52:19,612][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 05:52:20,126][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 05:52:20,640][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10879 tokens. [2025-11-13 05:52:21,503][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.20%, Current % of VRAM taken: 59.65%, Block Peak % of device VRAM: 62.30%, ΔTime: 00:00:34 [2025-11-13 05:52:22,159][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 05:52:22,161][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 05:52:22,163][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 05:52:23,113][__main__][INFO] - Iteration 512 took 1m 3s (40.89% Gen, 57.62% Train). Generation: 26s, Training: 36s. Estimated remaining time: 45h 31m 26s. Estimated total time: 53h 14m 7s. Time estimates for 10 more iterations: 10m 38s, 100 more iterations: 1h 46m 28s, 500 more iterations: 8h 52m 21s. [2025-11-13 05:52:23,116][__main__][INFO] - Starting iteration 512. [2025-11-13 05:52:23,620][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 51 and human policies 1. [2025-11-13 05:52:23,621][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 05:52:40,710][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:52:41,470][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:52:42,219][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:52:42,458][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:52:42,715][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:52:44,374][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:52:45,417][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:52:51,231][__main__][INFO] - Number of regex retries in iteration 512: 7 [2025-11-13 05:52:51,232][__main__][INFO] - agents played in iteration 512 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 05:52:52,080][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:52:52,108][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:52:52,135][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:52:52,158][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:52:52,159][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 05:52:52,160][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 05:52:52,874][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 05:52:53,343][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 05:52:53,853][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 05:52:54,356][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 05:52:54,867][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 05:52:55,370][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 05:52:56,007][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 05:52:56,510][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 05:52:57,012][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 05:52:57,526][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 05:52:58,029][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 05:52:58,531][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 05:52:59,034][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 05:52:59,537][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 05:53:00,055][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 05:53:00,556][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 05:53:01,057][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 05:53:01,560][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 05:53:02,062][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 05:53:02,568][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 05:53:03,069][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 05:53:03,571][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 05:53:04,076][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 05:53:04,581][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 05:53:05,083][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 05:53:05,587][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 05:53:06,091][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 05:53:06,594][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 05:53:07,099][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 05:53:07,612][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 05:53:08,118][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 05:53:08,626][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 05:53:09,155][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 05:53:09,666][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 05:53:10,179][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 05:53:10,690][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 05:53:11,199][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 05:53:11,707][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 05:53:12,214][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 05:53:12,724][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 05:53:13,240][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 05:53:13,749][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 05:53:14,260][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 05:53:14,772][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 05:53:15,283][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 05:53:15,791][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 05:53:16,301][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 05:53:16,810][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 05:53:17,322][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 05:53:17,835][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 05:53:18,352][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 05:53:18,863][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 05:53:19,375][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 05:53:19,889][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 05:53:20,400][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 05:53:20,916][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 05:53:21,426][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 05:53:21,939][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 05:53:22,448][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 05:53:22,957][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 05:53:23,475][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 05:53:23,985][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 05:53:24,498][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 05:53:25,011][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 05:53:25,522][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10812 tokens. [2025-11-13 05:53:26,395][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.17%, Current % of VRAM taken: 59.62%, Block Peak % of device VRAM: 62.28%, ΔTime: 00:00:33 [2025-11-13 05:53:27,164][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 05:53:27,166][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 05:53:27,168][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 05:53:28,287][__main__][INFO] - Iteration 513 took 1m 4s (42.70% Gen, 55.57% Train). Generation: 27s, Training: 35s. Estimated remaining time: 46h 9m 34s. Estimated total time: 53h 53m 21s. Time estimates for 10 more iterations: 10m 46s, 100 more iterations: 1h 47m 46s, 500 more iterations: 8h 58m 53s. [2025-11-13 05:53:28,289][__main__][INFO] - Starting iteration 513. [2025-11-13 05:53:28,797][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 51 and human policies 1. [2025-11-13 05:53:28,797][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 05:53:43,583][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:53:43,595][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:53:44,478][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:53:46,024][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:53:47,519][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:53:47,807][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:53:50,304][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:53:54,720][__main__][INFO] - Number of regex retries in iteration 513: 7 [2025-11-13 05:53:54,721][__main__][INFO] - agents played in iteration 513 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 05:53:55,513][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:53:55,541][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:53:55,567][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:53:55,590][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:53:55,590][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 05:53:55,591][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 05:53:56,363][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 05:53:56,823][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 05:53:57,337][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 05:53:57,838][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 05:53:58,338][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 05:53:58,842][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 05:53:59,342][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 05:53:59,846][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 05:54:00,346][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 05:54:00,846][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 05:54:01,365][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 05:54:01,866][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 05:54:02,369][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 05:54:02,871][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 05:54:03,374][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 05:54:03,875][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 05:54:04,374][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 05:54:04,874][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 05:54:05,376][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 05:54:05,877][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 05:54:06,378][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 05:54:06,878][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 05:54:07,378][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 05:54:07,880][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 05:54:08,381][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 05:54:08,883][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 05:54:09,386][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 05:54:09,890][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 05:54:10,391][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 05:54:10,891][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 05:54:11,392][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 05:54:11,893][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 05:54:12,401][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 05:54:12,903][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 05:54:13,408][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 05:54:13,912][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 05:54:14,416][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 05:54:14,918][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 05:54:15,425][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 05:54:15,937][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 05:54:16,446][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 05:54:16,959][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 05:54:17,472][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 05:54:17,983][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 05:54:18,497][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 05:54:19,013][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 05:54:19,525][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 05:54:20,044][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 05:54:20,557][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 05:54:21,073][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 05:54:21,582][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 05:54:22,096][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 05:54:22,608][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 05:54:23,120][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 05:54:23,637][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 05:54:24,152][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 05:54:24,665][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 05:54:25,189][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 05:54:25,701][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 05:54:26,214][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 05:54:26,728][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 05:54:27,243][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 05:54:27,774][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 05:54:28,289][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 05:54:28,801][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10872 tokens. [2025-11-13 05:54:29,716][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.19%, Current % of VRAM taken: 59.64%, Block Peak % of device VRAM: 62.31%, ΔTime: 00:00:33 [2025-11-13 05:54:30,399][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 05:54:30,401][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 05:54:30,402][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 05:54:31,289][__main__][INFO] - Iteration 514 took 1m 2s (41.48% Gen, 57.10% Train). Generation: 25s, Training: 35s. Estimated remaining time: 44h 19m 49s. Estimated total time: 52h 4m 38s. Time estimates for 10 more iterations: 10m 24s, 100 more iterations: 1h 44m 9s, 500 more iterations: 8h 40m 46s. [2025-11-13 05:54:31,291][__main__][INFO] - Starting iteration 514. [2025-11-13 05:54:31,836][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 51 and human policies 1. [2025-11-13 05:54:31,836][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 05:54:48,826][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:54:49,689][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:54:52,640][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:54:54,747][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:54:58,579][__main__][INFO] - Number of regex retries in iteration 514: 4 [2025-11-13 05:54:58,580][__main__][INFO] - agents played in iteration 514 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 05:54:59,362][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:54:59,388][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:54:59,412][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:54:59,434][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:54:59,435][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 05:54:59,436][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 05:55:00,174][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 05:55:00,634][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 05:55:01,141][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 05:55:01,648][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 05:55:02,171][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 05:55:02,675][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 05:55:03,180][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 05:55:03,681][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 05:55:04,185][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 05:55:04,696][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 05:55:05,211][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 05:55:05,717][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 05:55:06,223][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 05:55:06,727][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 05:55:07,231][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 05:55:07,735][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 05:55:08,238][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 05:55:08,753][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 05:55:09,257][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 05:55:09,760][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 05:55:10,268][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 05:55:10,777][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 05:55:11,286][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 05:55:11,791][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 05:55:12,295][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 05:55:12,799][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 05:55:13,302][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 05:55:13,806][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 05:55:14,309][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 05:55:14,813][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 05:55:15,316][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 05:55:15,824][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 05:55:16,328][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 05:55:16,831][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 05:55:17,333][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 05:55:17,843][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 05:55:18,346][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 05:55:18,853][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 05:55:19,364][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 05:55:19,870][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 05:55:20,382][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 05:55:20,888][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 05:55:21,393][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 05:55:21,905][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 05:55:22,417][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 05:55:22,930][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 05:55:23,439][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 05:55:23,952][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 05:55:24,466][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 05:55:24,980][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 05:55:25,493][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 05:55:26,008][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 05:55:26,526][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 05:55:27,049][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 05:55:27,559][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 05:55:28,077][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 05:55:28,597][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 05:55:29,112][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 05:55:29,629][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 05:55:30,142][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 05:55:30,655][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 05:55:31,186][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 05:55:31,703][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 05:55:32,217][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 05:55:32,733][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10879 tokens. [2025-11-13 05:55:33,569][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.20%, Current % of VRAM taken: 59.65%, Block Peak % of device VRAM: 62.31%, ΔTime: 00:00:33 [2025-11-13 05:55:34,320][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 05:55:34,321][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 05:55:34,323][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 05:55:35,368][__main__][INFO] - Iteration 515 took 1m 3s (42.09% Gen, 56.26% Train). Generation: 26s, Training: 35s. Estimated remaining time: 45h 10m 44s. Estimated total time: 52h 56m 38s. Time estimates for 10 more iterations: 10m 35s, 100 more iterations: 1h 45m 53s, 500 more iterations: 8h 49m 26s. [2025-11-13 05:55:35,370][__main__][INFO] - Starting iteration 515. [2025-11-13 05:55:36,110][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 51 and human policies 1. [2025-11-13 05:55:36,111][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 05:55:55,504][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:56:04,292][mllm.models.large_language_model_local][WARNING] - Response Given the values, the optimal proposal would try to capture the higher value items. Since both you and Bob value books the most and balls the least, a strategic proposal would be to propose slightly more of the items you value higher to pressure Bob into proposing less of those items. Proposal: 10 hats, 8 books, 12 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:56:05,686][__main__][INFO] - Number of regex retries in iteration 515: 2 [2025-11-13 05:56:05,687][__main__][INFO] - agents played in iteration 515 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 05:56:06,516][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:56:06,539][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:56:06,563][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:56:06,585][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:56:06,586][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 05:56:06,587][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 05:56:07,310][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 05:56:07,784][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 05:56:08,292][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 05:56:08,797][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 05:56:09,303][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 05:56:09,805][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 05:56:10,310][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 05:56:10,813][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 05:56:11,317][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 05:56:11,823][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 05:56:12,330][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 05:56:12,832][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 05:56:13,334][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 05:56:13,836][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 05:56:14,341][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 05:56:14,842][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 05:56:15,347][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 05:56:15,850][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 05:56:16,356][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 05:56:16,882][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 05:56:17,385][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 05:56:17,887][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 05:56:18,389][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 05:56:18,892][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 05:56:19,398][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 05:56:19,900][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 05:56:20,404][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 05:56:20,907][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 05:56:21,410][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 05:56:21,911][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 05:56:22,413][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 05:56:22,914][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 05:56:23,417][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 05:56:23,918][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 05:56:24,419][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 05:56:24,917][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 05:56:25,417][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 05:56:25,921][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 05:56:26,426][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 05:56:26,930][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 05:56:27,435][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 05:56:27,940][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 05:56:28,443][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 05:56:28,953][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 05:56:29,464][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 05:56:29,979][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 05:56:30,493][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 05:56:31,007][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 05:56:31,520][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 05:56:32,035][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 05:56:32,551][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 05:56:33,066][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 05:56:33,583][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 05:56:34,095][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 05:56:34,609][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 05:56:35,121][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 05:56:35,634][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 05:56:36,149][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 05:56:36,663][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 05:56:37,175][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 05:56:37,703][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 05:56:38,216][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 05:56:38,729][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 05:56:39,244][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 05:56:39,762][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10843 tokens. [2025-11-13 05:56:40,657][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.23%, Current % of VRAM taken: 59.68%, Block Peak % of device VRAM: 62.41%, ΔTime: 00:00:33 [2025-11-13 05:56:41,331][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 05:56:41,333][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 05:56:41,335][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 05:56:42,234][__main__][INFO] - Iteration 516 took 1m 6s (44.73% Gen, 53.91% Train). Generation: 29s, Training: 35s. Estimated remaining time: 47h 19m 12s. Estimated total time: 55h 6m 13s. Time estimates for 10 more iterations: 11m 1s, 100 more iterations: 1h 50m 12s, 500 more iterations: 9h 11m 2s. [2025-11-13 05:56:42,236][__main__][INFO] - Starting iteration 516. [2025-11-13 05:56:42,745][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 51 and human policies 1. [2025-11-13 05:56:42,746][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 05:56:59,328][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:57:00,233][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:57:00,235][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:57:01,300][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:57:01,302][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:57:02,626][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:57:06,469][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:57:06,724][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:57:06,995][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:57:08,096][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:57:09,282][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:57:10,315][__main__][INFO] - Number of regex retries in iteration 516: 11 [2025-11-13 05:57:10,316][__main__][INFO] - agents played in iteration 516 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 05:57:11,090][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:57:11,113][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:57:11,137][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:57:11,159][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:57:11,160][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 05:57:11,161][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 05:57:11,884][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 05:57:12,344][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 05:57:12,856][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 05:57:13,360][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 05:57:13,867][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 05:57:14,372][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 05:57:14,876][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 05:57:15,397][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 05:57:15,899][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 05:57:16,406][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 05:57:16,909][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 05:57:17,416][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 05:57:17,920][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 05:57:18,420][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 05:57:18,921][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 05:57:19,429][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 05:57:19,934][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 05:57:20,444][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 05:57:20,950][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 05:57:21,456][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 05:57:21,971][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 05:57:22,485][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 05:57:22,991][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 05:57:23,496][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 05:57:24,004][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 05:57:24,510][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 05:57:25,014][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 05:57:25,518][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 05:57:26,023][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 05:57:26,526][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 05:57:27,030][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 05:57:27,533][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 05:57:28,039][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 05:57:28,545][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 05:57:29,048][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 05:57:29,554][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 05:57:30,070][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 05:57:30,574][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 05:57:31,101][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 05:57:31,606][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 05:57:32,110][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 05:57:32,621][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 05:57:33,134][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 05:57:33,649][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 05:57:34,160][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 05:57:34,673][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 05:57:35,191][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 05:57:35,704][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 05:57:36,219][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 05:57:36,736][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 05:57:37,249][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 05:57:37,776][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 05:57:38,287][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 05:57:38,798][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 05:57:39,313][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 05:57:39,827][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 05:57:40,343][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 05:57:40,859][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 05:57:41,376][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 05:57:41,896][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 05:57:42,407][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 05:57:42,922][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 05:57:43,437][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 05:57:43,948][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 05:57:44,458][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10875 tokens. [2025-11-13 05:57:45,295][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.23%, Current % of VRAM taken: 59.69%, Block Peak % of device VRAM: 62.49%, ΔTime: 00:00:33 [2025-11-13 05:57:46,060][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 05:57:46,061][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 05:57:46,063][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 05:57:48,724][__main__][INFO] - Iteration 517 took 1m 5s (41.79% Gen, 54.18% Train). Generation: 27s, Training: 35s. Estimated remaining time: 47h 10m 53s. Estimated total time: 54h 59m 0s. Time estimates for 10 more iterations: 10m 59s, 100 more iterations: 1h 49m 58s, 500 more iterations: 9h 9m 50s. [2025-11-13 05:57:48,735][__main__][INFO] - Starting iteration 517. [2025-11-13 05:57:49,214][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 51 and human policies 1. [2025-11-13 05:57:49,215][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 05:58:14,220][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:58:15,914][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:58:16,938][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:58:20,920][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:58:22,052][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:58:25,223][__main__][INFO] - Number of regex retries in iteration 517: 5 [2025-11-13 05:58:25,224][__main__][INFO] - agents played in iteration 517 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 05:58:26,081][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:58:26,106][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:58:26,128][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:58:26,152][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:58:26,153][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 05:58:26,154][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 05:58:26,989][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 05:58:27,449][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 05:58:27,956][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 05:58:28,459][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 05:58:28,960][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 05:58:29,461][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 05:58:29,962][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 05:58:30,466][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 05:58:30,968][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 05:58:31,472][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 05:58:31,984][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 05:58:32,484][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 05:58:32,999][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 05:58:33,504][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 05:58:34,007][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 05:58:34,514][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 05:58:35,020][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 05:58:35,528][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 05:58:36,032][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 05:58:36,538][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 05:58:37,052][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 05:58:37,563][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 05:58:38,076][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 05:58:38,586][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 05:58:39,099][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 05:58:39,624][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 05:58:40,140][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 05:58:40,653][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 05:58:41,172][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 05:58:41,685][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 05:58:42,214][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 05:58:42,729][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 05:58:43,244][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 05:58:43,765][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 05:58:44,280][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 05:58:44,804][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 05:58:45,316][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 05:58:45,830][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 05:58:46,342][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 05:58:46,856][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 05:58:47,369][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 05:58:47,904][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 05:58:48,423][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 05:58:48,947][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 05:58:49,464][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 05:58:49,982][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 05:58:50,501][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 05:58:51,017][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 05:58:51,535][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 05:58:52,049][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 05:58:52,561][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 05:58:53,076][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 05:58:53,588][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 05:58:54,120][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 05:58:54,632][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 05:58:55,149][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 05:58:55,665][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 05:58:56,180][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 05:58:56,705][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 05:58:57,216][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 05:58:57,732][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 05:58:58,242][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 05:58:58,756][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 05:58:59,265][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 05:58:59,775][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10879 tokens. [2025-11-13 05:59:00,599][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.26%, Current % of VRAM taken: 59.71%, Block Peak % of device VRAM: 62.49%, ΔTime: 00:00:33 [2025-11-13 05:59:01,253][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 05:59:01,255][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 05:59:01,257][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 05:59:02,382][__main__][INFO] - Iteration 518 took 1m 13s (49.21% Gen, 49.25% Train). Generation: 36s, Training: 36s. Estimated remaining time: 53h 9m 5s. Estimated total time: 60h 58m 26s. Time estimates for 10 more iterations: 12m 11s, 100 more iterations: 2h 1m 56s, 500 more iterations: 10h 9m 44s. [2025-11-13 05:59:02,384][__main__][INFO] - Starting iteration 518. [2025-11-13 05:59:02,856][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 51 and human policies 1. [2025-11-13 05:59:02,856][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 05:59:15,021][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:59:16,230][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:59:18,373][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:59:20,316][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 05:59:25,550][__main__][INFO] - Number of regex retries in iteration 518: 4 [2025-11-13 05:59:25,551][__main__][INFO] - agents played in iteration 518 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 05:59:26,367][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:59:26,397][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:59:26,424][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:59:26,448][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 05:59:26,449][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 05:59:26,450][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 05:59:27,262][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 05:59:27,722][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 05:59:28,235][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 05:59:28,737][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 05:59:29,239][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 05:59:29,745][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 05:59:30,251][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 05:59:30,756][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 05:59:31,258][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 05:59:31,759][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 05:59:32,262][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 05:59:32,764][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 05:59:33,266][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 05:59:33,771][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 05:59:34,273][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 05:59:34,777][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 05:59:35,279][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 05:59:35,779][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 05:59:36,291][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 05:59:36,793][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 05:59:37,306][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 05:59:37,808][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 05:59:38,309][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 05:59:38,820][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 05:59:39,323][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 05:59:39,825][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 05:59:40,325][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 05:59:40,826][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 05:59:41,332][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 05:59:41,837][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 05:59:42,340][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 05:59:42,851][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 05:59:43,355][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 05:59:43,861][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 05:59:44,370][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 05:59:44,882][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 05:59:45,405][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 05:59:45,919][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 05:59:46,428][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 05:59:46,938][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 05:59:47,450][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 05:59:47,967][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 05:59:48,482][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 05:59:48,994][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 05:59:49,511][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 05:59:50,021][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 05:59:50,536][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 05:59:51,108][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 05:59:52,472][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 05:59:52,980][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 05:59:53,490][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 05:59:54,000][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 05:59:54,524][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 05:59:55,036][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 05:59:55,552][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 05:59:56,066][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 05:59:56,577][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 05:59:57,091][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 05:59:57,603][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 05:59:58,116][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 05:59:58,630][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 05:59:59,141][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 05:59:59,653][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 06:00:00,167][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 06:00:00,677][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10875 tokens. [2025-11-13 06:00:01,572][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.19%, Current % of VRAM taken: 59.64%, Block Peak % of device VRAM: 62.42%, ΔTime: 00:00:34 [2025-11-13 06:00:02,255][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 06:00:02,256][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 06:00:02,258][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 06:00:03,219][__main__][INFO] - Iteration 519 took 1m 0s (37.60% Gen, 60.81% Train). Generation: 22s, Training: 36s. Estimated remaining time: 42h 27m 49s. Estimated total time: 50h 18m 10s. Time estimates for 10 more iterations: 10m 3s, 100 more iterations: 1h 40m 36s, 500 more iterations: 8h 23m 1s. [2025-11-13 06:00:03,221][__main__][INFO] - Starting iteration 519. [2025-11-13 06:00:03,721][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 51 and human policies 1. [2025-11-13 06:00:03,722][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 06:00:23,554][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:00:23,840][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:00:23,842][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:00:25,379][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:00:25,733][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:00:29,517][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:00:30,647][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:00:30,721][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:00:30,829][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:00:31,219][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:00:31,310][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:00:34,554][__main__][INFO] - Number of regex retries in iteration 519: 11 [2025-11-13 06:00:34,554][__main__][INFO] - agents played in iteration 519 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 06:00:35,425][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:00:35,453][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:00:35,480][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:00:35,503][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:00:35,504][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 06:00:35,505][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 06:00:36,285][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 06:00:36,745][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 06:00:37,252][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 06:00:37,755][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 06:00:38,257][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 06:00:38,757][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 06:00:39,259][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 06:00:39,760][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 06:00:40,262][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 06:00:40,767][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 06:00:41,269][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 06:00:41,771][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 06:00:42,273][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 06:00:42,774][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 06:00:43,278][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 06:00:43,779][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 06:00:44,280][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 06:00:44,782][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 06:00:45,283][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 06:00:45,786][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 06:00:46,303][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 06:00:46,808][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 06:00:47,334][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 06:00:47,841][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 06:00:48,340][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 06:00:48,848][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 06:00:49,358][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 06:00:49,870][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 06:00:50,382][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 06:00:50,893][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 06:00:51,417][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 06:00:51,929][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 06:00:52,444][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 06:00:52,954][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 06:00:53,466][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 06:00:53,983][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 06:00:54,499][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 06:00:55,027][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 06:00:55,544][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 06:00:56,057][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 06:00:56,576][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 06:00:57,088][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 06:00:57,596][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 06:00:58,107][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 06:00:58,624][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 06:00:59,148][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 06:00:59,661][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 06:01:00,169][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 06:01:00,681][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 06:01:01,197][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 06:01:01,731][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 06:01:02,246][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 06:01:02,760][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 06:01:03,276][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 06:01:03,792][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 06:01:04,312][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 06:01:04,826][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 06:01:05,340][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 06:01:05,859][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 06:01:06,374][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 06:01:06,886][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 06:01:07,399][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 06:01:07,912][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 06:01:08,426][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 06:01:08,944][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10877 tokens. [2025-11-13 06:01:09,787][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.20%, Current % of VRAM taken: 59.65%, Block Peak % of device VRAM: 62.28%, ΔTime: 00:00:33 [2025-11-13 06:01:10,557][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 06:01:10,558][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 06:01:10,560][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 06:01:11,560][__main__][INFO] - Iteration 520 took 1m 7s (45.45% Gen, 53.07% Train). Generation: 30s, Training: 36s. Estimated remaining time: 48h 40m 29s. Estimated total time: 56h 31m 59s. Time estimates for 10 more iterations: 11m 18s, 100 more iterations: 1h 53m 3s, 500 more iterations: 9h 25m 19s. [2025-11-13 06:01:11,562][__main__][INFO] - Starting iteration 520. [2025-11-13 06:01:12,068][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 51 and human policies 1. [2025-11-13 06:01:12,069][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 06:01:24,458][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:01:24,542][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:01:24,632][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:01:25,402][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:01:27,409][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:01:28,212][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:01:29,647][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:01:30,136][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:01:30,705][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:01:32,355][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:01:35,238][__main__][INFO] - Number of regex retries in iteration 520: 10 [2025-11-13 06:01:35,239][__main__][INFO] - agents played in iteration 520 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 06:01:36,354][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:01:36,381][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:01:36,407][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:01:36,430][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:01:36,431][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 06:01:36,432][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 06:01:37,128][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 06:01:37,595][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 06:01:38,100][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 06:01:38,605][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 06:01:39,107][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 06:01:39,608][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 06:01:40,111][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 06:01:40,612][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 06:01:41,112][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 06:01:41,615][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 06:01:42,120][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 06:01:42,636][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 06:01:43,136][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 06:01:43,637][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 06:01:44,138][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 06:01:44,637][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 06:01:45,141][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 06:01:45,644][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 06:01:46,146][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 06:01:46,653][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 06:01:47,153][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 06:01:47,655][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 06:01:48,156][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 06:01:48,658][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 06:01:49,161][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 06:01:49,669][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 06:01:50,173][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 06:01:50,675][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 06:01:51,178][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 06:01:51,679][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 06:01:52,187][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 06:01:52,691][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 06:01:53,200][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 06:01:53,732][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 06:01:54,239][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 06:01:54,744][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 06:01:55,258][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 06:01:55,775][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 06:01:56,291][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 06:01:56,807][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 06:01:57,315][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 06:01:57,822][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 06:01:58,343][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 06:01:58,855][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 06:01:59,368][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 06:01:59,876][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 06:02:00,389][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 06:02:00,903][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 06:02:01,416][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 06:02:01,926][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 06:02:02,444][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 06:02:04,497][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 06:02:05,401][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 06:02:05,914][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 06:02:06,429][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 06:02:06,943][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 06:02:07,463][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 06:02:07,976][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 06:02:08,486][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 06:02:08,996][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 06:02:09,508][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 06:02:10,026][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 06:02:10,538][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 06:02:11,054][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 06:02:11,563][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10878 tokens. [2025-11-13 06:02:12,461][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.24%, Current % of VRAM taken: 59.69%, Block Peak % of device VRAM: 62.52%, ΔTime: 00:00:35 [2025-11-13 06:02:13,103][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 06:02:13,105][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 06:02:13,106][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 06:02:15,159][__main__][INFO] - Iteration 521 took 1m 3s (36.72% Gen, 60.02% Train). Generation: 23s, Training: 37s. Estimated remaining time: 44h 42m 1s. Estimated total time: 52h 34m 34s. Time estimates for 10 more iterations: 10m 30s, 100 more iterations: 1h 45m 9s, 500 more iterations: 8h 45m 45s. [2025-11-13 06:02:15,165][__main__][INFO] - Starting iteration 521. [2025-11-13 06:02:15,683][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 52 and human policies 1. [2025-11-13 06:02:15,684][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 06:02:35,170][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:02:35,212][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:02:38,494][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:02:45,035][__main__][INFO] - Number of regex retries in iteration 521: 3 [2025-11-13 06:02:45,036][__main__][INFO] - agents played in iteration 521 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 06:02:45,872][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:02:45,900][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:02:45,926][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:02:45,949][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:02:45,950][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 06:02:45,950][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 06:02:46,750][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 06:02:47,211][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 06:02:47,722][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 06:02:48,233][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 06:02:48,738][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 06:02:49,244][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 06:02:49,750][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 06:02:50,256][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 06:02:50,760][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 06:02:51,267][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 06:02:51,772][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 06:02:52,275][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 06:02:52,780][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 06:02:53,288][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 06:02:53,793][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 06:02:54,300][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 06:02:54,804][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 06:02:55,309][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 06:02:55,816][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 06:02:56,319][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 06:02:56,826][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 06:02:57,340][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 06:02:57,843][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 06:02:58,350][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 06:02:58,863][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 06:02:59,375][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 06:02:59,894][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 06:03:00,404][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 06:03:00,914][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 06:03:01,421][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 06:03:01,934][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 06:03:02,456][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 06:03:02,966][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 06:03:03,477][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 06:03:03,994][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 06:03:04,504][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 06:03:05,015][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 06:03:05,527][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 06:03:06,038][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 06:03:06,562][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 06:03:07,072][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 06:03:07,585][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 06:03:08,097][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 06:03:08,608][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 06:03:09,119][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 06:03:09,630][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 06:03:10,142][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 06:03:10,657][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 06:03:11,171][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 06:03:11,693][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 06:03:12,206][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 06:03:12,719][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 06:03:13,234][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 06:03:13,746][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 06:03:14,274][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 06:03:14,785][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 06:03:15,301][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 06:03:15,817][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 06:03:16,329][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 06:03:16,841][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 06:03:17,354][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 06:03:17,866][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 06:03:18,383][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 06:03:18,893][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 06:03:19,402][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10877 tokens. [2025-11-13 06:03:20,271][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.24%, Current % of VRAM taken: 59.69%, Block Peak % of device VRAM: 62.35%, ΔTime: 00:00:33 [2025-11-13 06:03:21,068][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 06:03:21,070][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 06:03:21,072][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 06:03:22,012][__main__][INFO] - Iteration 522 took 1m 6s (44.25% Gen, 54.33% Train). Generation: 29s, Training: 36s. Estimated remaining time: 47h 22m 47s. Estimated total time: 55h 16m 27s. Time estimates for 10 more iterations: 11m 3s, 100 more iterations: 1h 50m 32s, 500 more iterations: 9h 12m 44s. [2025-11-13 06:03:22,015][__main__][INFO] - Starting iteration 522. [2025-11-13 06:03:22,524][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 52 and human policies 1. [2025-11-13 06:03:22,524][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 06:03:39,077][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:03:42,038][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:03:46,756][__main__][INFO] - Number of regex retries in iteration 522: 2 [2025-11-13 06:03:46,757][__main__][INFO] - agents played in iteration 522 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 06:03:47,554][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:03:47,581][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:03:47,607][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:03:47,631][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:03:47,631][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 06:03:47,632][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 06:03:48,349][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 06:03:48,818][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 06:03:49,328][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 06:03:49,834][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 06:03:50,340][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 06:03:50,844][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 06:03:51,360][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 06:03:51,862][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 06:03:52,362][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 06:03:52,867][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 06:03:53,370][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 06:03:53,889][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 06:03:54,392][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 06:03:54,893][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 06:03:55,394][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 06:03:55,893][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 06:03:56,398][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 06:03:56,899][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 06:03:57,398][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 06:03:57,903][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 06:03:58,403][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 06:03:58,904][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 06:03:59,411][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 06:03:59,912][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 06:04:00,413][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 06:04:00,915][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 06:04:01,416][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 06:04:01,921][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 06:04:02,424][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 06:04:02,924][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 06:04:03,427][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 06:04:03,928][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 06:04:04,431][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 06:04:04,937][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 06:04:05,442][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 06:04:05,955][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 06:04:06,460][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 06:04:06,964][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 06:04:07,470][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 06:04:07,979][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 06:04:08,494][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 06:04:09,108][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 06:04:10,815][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 06:04:11,327][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 06:04:11,837][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 06:04:12,357][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 06:04:12,870][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 06:04:13,381][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 06:04:13,896][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 06:04:14,409][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 06:04:14,917][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 06:04:15,425][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 06:04:15,934][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 06:04:16,464][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 06:04:16,975][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 06:04:17,489][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 06:04:17,998][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 06:04:18,511][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 06:04:19,025][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 06:04:19,539][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 06:04:20,055][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 06:04:20,574][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 06:04:21,086][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 06:04:21,609][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 06:04:22,122][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10877 tokens. [2025-11-13 06:04:23,012][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.21%, Current % of VRAM taken: 59.66%, Block Peak % of device VRAM: 62.24%, ΔTime: 00:00:34 [2025-11-13 06:04:23,673][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 06:04:23,674][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 06:04:23,676][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 06:04:24,496][__main__][INFO] - Iteration 523 took 1m 1s (39.10% Gen, 59.57% Train). Generation: 24s, Training: 36s. Estimated remaining time: 43h 43m 57s. Estimated total time: 51h 38m 40s. Time estimates for 10 more iterations: 10m 19s, 100 more iterations: 1h 43m 17s, 500 more iterations: 8h 36m 26s. [2025-11-13 06:04:24,499][__main__][INFO] - Starting iteration 523. [2025-11-13 06:04:25,011][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 52 and human policies 1. [2025-11-13 06:04:25,012][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 06:04:44,498][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:04:46,641][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:04:52,284][__main__][INFO] - Number of regex retries in iteration 523: 2 [2025-11-13 06:04:52,284][__main__][INFO] - agents played in iteration 523 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 06:04:53,102][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:04:53,129][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:04:53,156][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:04:53,183][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:04:53,184][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 06:04:53,185][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 06:04:53,934][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 06:04:54,397][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 06:04:54,907][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 06:04:55,413][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 06:04:55,918][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 06:04:56,423][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 06:04:56,934][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 06:04:57,441][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 06:04:57,945][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 06:04:58,456][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 06:04:58,970][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 06:04:59,472][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 06:04:59,991][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 06:05:00,493][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 06:05:00,994][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 06:05:01,497][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 06:05:01,999][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 06:05:02,508][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 06:05:03,012][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 06:05:03,521][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 06:05:04,026][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 06:05:04,529][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 06:05:05,034][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 06:05:05,537][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 06:05:06,040][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 06:05:06,545][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 06:05:07,050][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 06:05:07,551][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 06:05:08,054][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 06:05:08,556][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 06:05:09,059][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 06:05:09,560][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 06:05:10,061][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 06:05:10,576][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 06:05:11,079][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 06:05:11,603][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 06:05:12,106][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 06:05:12,612][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 06:05:13,111][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 06:05:13,613][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 06:05:14,119][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 06:05:14,621][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 06:05:15,127][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 06:05:15,635][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 06:05:16,141][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 06:05:16,646][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 06:05:17,152][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 06:05:17,658][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 06:05:18,166][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 06:05:18,668][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 06:05:19,178][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 06:05:19,697][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 06:05:20,207][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 06:05:20,716][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 06:05:21,221][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 06:05:21,728][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 06:05:22,240][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 06:05:22,754][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 06:05:23,267][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 06:05:23,781][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 06:05:24,296][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 06:05:24,817][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 06:05:25,329][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 06:05:25,840][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 06:05:26,349][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10879 tokens. [2025-11-13 06:05:27,229][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.23%, Current % of VRAM taken: 59.68%, Block Peak % of device VRAM: 62.41%, ΔTime: 00:00:33 [2025-11-13 06:05:27,974][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 06:05:27,975][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 06:05:27,976][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 06:05:28,940][__main__][INFO] - Iteration 524 took 1m 3s (42.66% Gen, 55.83% Train). Generation: 27s, Training: 35s. Estimated remaining time: 45h 20m 43s. Estimated total time: 53h 16m 30s. Time estimates for 10 more iterations: 10m 39s, 100 more iterations: 1h 46m 33s, 500 more iterations: 8h 52m 45s. [2025-11-13 06:05:28,943][__main__][INFO] - Starting iteration 524. [2025-11-13 06:05:29,467][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 52 and human policies 1. [2025-11-13 06:05:29,467][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 06:05:41,273][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:05:41,287][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:05:43,524][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:05:46,662][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:05:47,287][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:05:49,066][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:05:51,450][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:05:51,568][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:05:52,646][__main__][INFO] - Number of regex retries in iteration 524: 8 [2025-11-13 06:05:52,647][__main__][INFO] - agents played in iteration 524 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 06:05:53,484][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:05:53,511][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:05:53,548][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:05:53,572][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:05:53,572][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 06:05:53,573][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 06:05:54,312][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 06:05:54,770][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 06:05:55,277][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 06:05:55,780][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 06:05:56,295][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 06:05:56,795][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 06:05:57,298][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 06:05:57,802][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 06:05:58,304][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 06:05:58,821][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 06:05:59,325][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 06:05:59,829][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 06:06:00,334][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 06:06:00,841][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 06:06:01,349][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 06:06:01,856][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 06:06:02,363][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 06:06:02,868][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 06:06:03,373][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 06:06:03,877][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 06:06:04,380][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 06:06:04,883][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 06:06:05,399][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 06:06:05,902][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 06:06:06,405][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 06:06:06,909][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 06:06:07,413][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 06:06:07,917][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 06:06:08,419][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 06:06:08,923][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 06:06:09,428][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 06:06:09,929][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 06:06:10,429][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 06:06:10,933][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 06:06:11,440][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 06:06:11,945][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 06:06:12,454][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 06:06:12,960][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 06:06:13,463][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 06:06:13,966][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 06:06:14,468][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 06:06:14,973][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 06:06:15,477][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 06:06:15,981][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 06:06:16,484][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 06:06:16,989][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 06:06:17,500][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 06:06:18,003][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 06:06:18,523][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 06:06:19,028][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 06:06:19,537][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 06:06:20,052][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 06:06:20,561][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 06:06:21,071][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 06:06:21,580][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 06:06:22,093][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 06:06:22,611][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 06:06:23,121][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 06:06:23,635][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 06:06:24,144][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 06:06:24,658][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 06:06:25,170][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 06:06:25,681][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 06:06:26,192][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 06:06:26,702][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10876 tokens. [2025-11-13 06:06:27,561][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.12%, Current % of VRAM taken: 59.58%, Block Peak % of device VRAM: 62.41%, ΔTime: 00:00:33 [2025-11-13 06:06:28,229][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 06:06:28,231][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 06:06:28,233][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 06:06:29,041][__main__][INFO] - Iteration 525 took 59s (38.91% Gen, 59.73% Train). Generation: 23s, Training: 35s. Estimated remaining time: 41h 41m 57s. Estimated total time: 49h 38m 44s. Time estimates for 10 more iterations: 9m 55s, 100 more iterations: 1h 39m 17s, 500 more iterations: 8h 16m 27s. [2025-11-13 06:06:29,043][__main__][INFO] - Starting iteration 525. [2025-11-13 06:06:29,551][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 52 and human policies 1. [2025-11-13 06:06:29,552][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 06:06:50,040][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:06:51,269][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:06:52,764][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:06:53,314][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:06:53,869][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:06:53,871][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:06:54,520][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:06:59,496][__main__][INFO] - Number of regex retries in iteration 525: 7 [2025-11-13 06:06:59,497][__main__][INFO] - agents played in iteration 525 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 06:07:00,337][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.53%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:07:00,366][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.53%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:07:00,393][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.53%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:07:00,416][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.53%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:07:00,416][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 06:07:00,417][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 06:07:01,134][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 06:07:01,599][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 06:07:02,111][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 06:07:02,619][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 06:07:03,128][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 06:07:03,632][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 06:07:04,140][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 06:07:04,645][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 06:07:05,153][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 06:07:05,661][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 06:07:06,163][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 06:07:06,672][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 06:07:07,177][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 06:07:07,680][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 06:07:08,184][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 06:07:08,687][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 06:07:09,190][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 06:07:09,694][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 06:07:10,197][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 06:07:10,700][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 06:07:11,203][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 06:07:11,706][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 06:07:12,237][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 06:07:12,739][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 06:07:13,245][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 06:07:13,747][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 06:07:14,251][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 06:07:14,757][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 06:07:15,259][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 06:07:15,762][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 06:07:16,266][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 06:07:16,770][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 06:07:17,273][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 06:07:17,779][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 06:07:18,282][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 06:07:18,785][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 06:07:19,287][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 06:07:19,790][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 06:07:20,300][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 06:07:20,803][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 06:07:21,311][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 06:07:21,814][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 06:07:22,320][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 06:07:22,826][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 06:07:23,329][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 06:07:23,832][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 06:07:24,338][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 06:07:24,841][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 06:07:25,354][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 06:07:25,859][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 06:07:26,364][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 06:07:26,875][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 06:07:27,381][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 06:07:27,886][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 06:07:28,396][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 06:07:28,907][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 06:07:29,419][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 06:07:29,931][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 06:07:30,442][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 06:07:30,948][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 06:07:31,460][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 06:07:31,974][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 06:07:32,484][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 06:07:32,999][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 06:07:33,513][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10876 tokens. [2025-11-13 06:07:34,381][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.17%, Current % of VRAM taken: 59.62%, Block Peak % of device VRAM: 62.28%, ΔTime: 00:00:33 [2025-11-13 06:07:35,107][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 06:07:35,109][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 06:07:35,110][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 06:07:36,058][__main__][INFO] - Iteration 526 took 1m 6s (45.02% Gen, 53.55% Train). Generation: 29s, Training: 35s. Estimated remaining time: 47h 27m 30s. Estimated total time: 55h 25m 25s. Time estimates for 10 more iterations: 11m 5s, 100 more iterations: 1h 50m 50s, 500 more iterations: 9h 14m 14s. [2025-11-13 06:07:36,061][__main__][INFO] - Starting iteration 526. [2025-11-13 06:07:36,575][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 52 and human policies 1. [2025-11-13 06:07:36,575][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 06:07:52,484][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:07:53,530][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:07:53,698][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:07:54,200][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:07:55,424][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:07:55,702][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:07:56,429][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:08:00,486][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:08:02,247][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:08:03,361][__main__][INFO] - Number of regex retries in iteration 526: 9 [2025-11-13 06:08:03,362][__main__][INFO] - agents played in iteration 526 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 06:08:04,194][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:08:04,217][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:08:04,239][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:08:04,262][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:08:04,262][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 06:08:04,264][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 06:08:05,018][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 06:08:05,478][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 06:08:05,988][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 06:08:06,494][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 06:08:06,998][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 06:08:07,502][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 06:08:08,005][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 06:08:08,509][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 06:08:09,014][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 06:08:09,518][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 06:08:10,020][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 06:08:21,617][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 06:08:22,126][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 06:08:22,628][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 06:08:23,131][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 06:08:23,639][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 06:08:24,140][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 06:08:24,651][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 06:08:25,153][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 06:08:25,656][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 06:08:26,166][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 06:08:26,669][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 06:08:27,173][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 06:08:27,678][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 06:08:28,181][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 06:08:28,689][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 06:08:29,197][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 06:08:29,705][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 06:08:30,213][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 06:08:30,718][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 06:08:31,231][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 06:08:31,743][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 06:08:32,258][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 06:08:32,773][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 06:08:33,287][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 06:08:33,806][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 06:08:34,323][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 06:08:34,838][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 06:08:35,358][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 06:08:35,873][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 06:08:36,389][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 06:08:36,904][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 06:08:37,420][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10879 tokens. [2025-11-13 06:08:38,310][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.25%, Current % of VRAM taken: 59.70%, Block Peak % of device VRAM: 62.28%, ΔTime: 00:00:33 [2025-11-13 06:08:38,972][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 06:08:38,975][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 06:08:38,977][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 06:08:39,930][__main__][INFO] - Iteration 527 took 1m 3s (42.28% Gen, 56.21% Train). Generation: 26s, Training: 35s. Estimated remaining time: 44h 48m 51s. Estimated total time: 52h 47m 50s. Time estimates for 10 more iterations: 10m 33s, 100 more iterations: 1h 45m 35s, 500 more iterations: 8h 47m 58s. [2025-11-13 06:08:39,933][__main__][INFO] - Starting iteration 527. [2025-11-13 06:08:40,442][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 52 and human policies 1. [2025-11-13 06:08:40,443][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 06:08:53,816][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:08:53,873][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:08:53,991][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:08:54,600][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:08:56,335][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:08:57,153][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:08:57,155][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:08:57,967][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:08:59,207][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:08:59,318][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:09:01,788][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:09:03,202][__main__][INFO] - Number of regex retries in iteration 527: 11 [2025-11-13 06:09:03,203][__main__][INFO] - agents played in iteration 527 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 06:09:04,177][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:09:04,201][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:09:04,225][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:09:04,249][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:09:04,249][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 06:09:04,250][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 06:09:05,059][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 06:09:05,527][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 06:09:06,033][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 06:09:06,538][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 06:09:07,059][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 06:09:07,563][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 06:09:08,065][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 06:09:08,567][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 06:09:09,070][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 06:09:09,580][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 06:09:10,083][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 06:09:10,585][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 06:09:11,088][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 06:09:11,591][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 06:09:12,096][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 06:09:12,598][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 06:09:13,101][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 06:09:13,604][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 06:09:14,106][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 06:09:14,610][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 06:09:15,115][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 06:09:15,616][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 06:09:16,120][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 06:09:16,624][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 06:09:17,126][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 06:09:17,627][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 06:09:18,128][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 06:09:18,645][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 06:09:19,149][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 06:09:19,657][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 06:09:20,164][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 06:09:20,666][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 06:09:21,169][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 06:09:21,673][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 06:09:22,176][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 06:09:22,684][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 06:09:23,183][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 06:09:23,683][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 06:09:24,183][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 06:09:24,684][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 06:09:25,186][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 06:09:25,692][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 06:09:26,195][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 06:09:26,700][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 06:09:27,202][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 06:09:27,706][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 06:09:28,210][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 06:09:28,712][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 06:09:29,220][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 06:09:29,722][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 06:09:30,223][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 06:09:30,736][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 06:09:31,237][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 06:09:31,750][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 06:09:32,254][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 06:09:32,758][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 06:09:33,261][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 06:09:33,763][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 06:09:34,269][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 06:09:34,773][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 06:09:35,277][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 06:09:35,788][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 06:09:36,299][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 06:09:36,805][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 06:09:37,316][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10879 tokens. [2025-11-13 06:09:38,207][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.20%, Current % of VRAM taken: 59.65%, Block Peak % of device VRAM: 62.41%, ΔTime: 00:00:33 [2025-11-13 06:09:38,974][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 06:09:38,976][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 06:09:38,978][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 06:09:39,848][__main__][INFO] - Iteration 528 took 59s (38.31% Gen, 60.22% Train). Generation: 22s, Training: 35s. Estimated remaining time: 41h 30m 21s. Estimated total time: 49h 30m 19s. Time estimates for 10 more iterations: 9m 54s, 100 more iterations: 1h 39m 0s, 500 more iterations: 8h 15m 3s. [2025-11-13 06:09:39,850][__main__][INFO] - Starting iteration 528. [2025-11-13 06:09:40,357][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 52 and human policies 1. [2025-11-13 06:09:40,358][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 06:09:53,164][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:09:54,057][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:09:54,143][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:09:58,224][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:09:59,004][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:10:03,686][__main__][INFO] - Number of regex retries in iteration 528: 5 [2025-11-13 06:10:03,686][__main__][INFO] - agents played in iteration 528 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 06:10:04,613][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:10:04,635][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:10:04,659][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:10:04,682][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:10:04,683][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 06:10:04,683][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 06:10:05,472][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 06:10:05,950][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 06:10:06,464][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 06:10:06,976][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 06:10:07,490][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 06:10:08,000][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 06:10:08,511][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 06:10:09,020][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 06:10:09,534][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 06:10:10,057][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 06:10:10,572][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 06:10:11,084][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 06:10:11,597][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 06:10:12,106][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 06:10:12,622][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 06:10:13,132][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 06:10:13,635][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 06:10:14,147][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 06:10:14,651][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 06:10:15,162][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 06:10:15,665][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 06:10:16,168][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 06:10:16,672][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 06:10:17,175][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 06:10:17,680][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 06:10:18,187][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 06:10:18,691][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 06:10:19,197][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 06:10:19,699][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 06:10:20,204][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 06:10:20,709][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 06:10:21,212][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 06:10:21,716][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 06:10:22,218][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 06:10:22,721][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 06:10:23,237][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 06:10:23,745][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 06:10:24,260][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 06:10:24,763][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 06:10:25,267][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 06:10:25,781][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 06:10:26,288][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 06:10:26,793][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 06:10:27,296][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 06:10:27,799][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 06:10:28,301][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 06:10:28,804][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 06:10:29,307][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 06:10:29,811][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 06:10:30,314][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 06:10:30,816][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 06:10:31,320][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 06:10:31,822][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 06:10:32,330][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 06:10:32,835][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 06:10:33,338][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 06:10:33,854][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 06:10:34,360][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 06:10:34,864][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 06:10:35,369][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 06:10:35,877][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 06:10:36,392][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 06:10:36,902][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 06:10:37,414][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 06:10:37,923][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10874 tokens. [2025-11-13 06:10:38,783][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.23%, Current % of VRAM taken: 59.68%, Block Peak % of device VRAM: 62.31%, ΔTime: 00:00:33 [2025-11-13 06:10:39,436][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 06:10:39,437][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 06:10:39,440][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 06:10:40,317][__main__][INFO] - Iteration 529 took 59s (38.91% Gen, 59.63% Train). Generation: 23s, Training: 35s. Estimated remaining time: 41h 57m 0s. Estimated total time: 49h 57m 59s. Time estimates for 10 more iterations: 9m 59s, 100 more iterations: 1h 39m 55s, 500 more iterations: 8h 19m 39s. [2025-11-13 06:10:40,319][__main__][INFO] - Starting iteration 529. [2025-11-13 06:10:40,822][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 52 and human policies 1. [2025-11-13 06:10:40,823][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 06:10:58,840][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:10:59,013][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:10:59,735][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:10:59,823][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:10:59,916][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:11:00,578][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:11:00,829][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:11:02,489][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:11:02,612][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:11:03,774][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:11:05,950][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:11:06,188][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:11:09,083][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:11:09,829][__main__][INFO] - Number of regex retries in iteration 529: 13 [2025-11-13 06:11:09,830][__main__][INFO] - agents played in iteration 529 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 06:11:10,704][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:11:10,728][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:11:10,750][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:11:10,773][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:11:10,773][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 06:11:10,775][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 06:11:11,565][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 06:11:12,030][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 06:11:12,544][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 06:11:13,056][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 06:11:13,576][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 06:11:14,087][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 06:11:14,600][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 06:11:15,112][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 06:11:15,624][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 06:11:16,134][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 06:11:16,640][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 06:11:17,144][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 06:11:17,647][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 06:11:18,152][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 06:11:18,659][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 06:11:19,165][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 06:11:19,671][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 06:11:20,181][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 06:11:20,685][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 06:11:21,194][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 06:11:21,698][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 06:11:22,202][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 06:11:22,706][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 06:11:23,211][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 06:11:23,715][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 06:11:24,218][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 06:11:24,723][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 06:11:25,231][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 06:11:25,735][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 06:11:26,241][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 06:11:26,745][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 06:11:27,248][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 06:11:27,763][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 06:11:28,267][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 06:11:28,775][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 06:11:29,283][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 06:11:29,788][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 06:11:30,295][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 06:11:30,797][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 06:11:31,299][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 06:11:31,805][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 06:11:32,310][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 06:11:32,814][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 06:11:33,316][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 06:11:33,816][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 06:11:34,320][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 06:11:34,821][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 06:11:35,325][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 06:11:35,829][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 06:11:36,331][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 06:11:36,833][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 06:11:37,335][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 06:11:37,837][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 06:11:38,339][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 06:11:38,840][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 06:11:39,341][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 06:11:39,844][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 06:11:40,345][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 06:11:40,848][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 06:11:41,354][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 06:11:41,859][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 06:11:42,364][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 06:11:42,869][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 06:11:43,375][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 06:11:43,882][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10869 tokens. [2025-11-13 06:11:44,739][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.20%, Current % of VRAM taken: 59.66%, Block Peak % of device VRAM: 62.28%, ΔTime: 00:00:33 [2025-11-13 06:11:45,480][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 06:11:45,482][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 06:11:45,485][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 06:11:46,623][__main__][INFO] - Iteration 530 took 1m 5s (44.08% Gen, 54.18% Train). Generation: 29s, Training: 35s. Estimated remaining time: 46h 47m 58s. Estimated total time: 54h 50m 3s. Time estimates for 10 more iterations: 10m 58s, 100 more iterations: 1h 49m 40s, 500 more iterations: 9h 8m 20s. [2025-11-13 06:11:46,625][__main__][INFO] - Starting iteration 530. [2025-11-13 06:11:47,123][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 52 and human policies 1. [2025-11-13 06:11:47,124][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 06:11:59,726][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:11:59,821][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:12:00,633][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:12:00,896][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:12:01,556][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:12:03,679][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:12:05,001][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:12:05,118][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:12:05,875][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:12:06,589][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:12:07,207][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:12:08,672][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:12:10,082][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:12:11,077][__main__][INFO] - Number of regex retries in iteration 530: 13 [2025-11-13 06:12:11,078][__main__][INFO] - agents played in iteration 530 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 06:12:11,976][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:12:11,999][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:12:12,023][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:12:12,046][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:12:12,047][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 06:12:12,047][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 06:12:12,864][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 06:12:13,332][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 06:12:13,850][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 06:12:14,362][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 06:12:14,876][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 06:12:15,395][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 06:12:15,908][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 06:12:16,420][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 06:12:16,938][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 06:12:17,454][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 06:12:17,973][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 06:12:18,487][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 06:12:18,998][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 06:12:19,511][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 06:12:20,022][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 06:12:20,532][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 06:12:21,043][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 06:12:21,550][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 06:12:22,078][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 06:12:22,590][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 06:12:23,106][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 06:12:23,619][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 06:12:24,129][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 06:12:24,639][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 06:12:25,148][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 06:12:25,658][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 06:12:26,168][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 06:12:26,677][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 06:12:27,203][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 06:12:27,710][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 06:12:28,219][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 06:12:28,730][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 06:12:29,238][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 06:12:29,751][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 06:12:30,253][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 06:12:30,757][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 06:12:31,272][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 06:12:31,776][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 06:12:32,289][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 06:12:32,793][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 06:12:33,299][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 06:12:33,802][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 06:12:34,306][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 06:12:34,808][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 06:12:35,315][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 06:12:35,816][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 06:12:36,320][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 06:12:36,824][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 06:12:37,323][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 06:12:37,825][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 06:12:38,327][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 06:12:38,830][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 06:12:39,335][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 06:12:39,836][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 06:12:40,339][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 06:12:40,844][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 06:12:41,347][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 06:12:41,849][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 06:12:42,355][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 06:12:42,859][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 06:12:43,372][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 06:12:43,879][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 06:12:44,386][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 06:12:44,891][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 06:12:45,395][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10878 tokens. [2025-11-13 06:12:46,206][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.28%, Current % of VRAM taken: 59.73%, Block Peak % of device VRAM: 62.41%, ΔTime: 00:00:33 [2025-11-13 06:12:46,847][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 06:12:46,849][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 06:12:46,850][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 06:12:48,579][__main__][INFO] - Iteration 531 took 1m 1s (38.98% Gen, 58.21% Train). Generation: 23s, Training: 35s. Estimated remaining time: 43h 9m 43s. Estimated total time: 51h 12m 49s. Time estimates for 10 more iterations: 10m 14s, 100 more iterations: 1h 42m 25s, 500 more iterations: 8h 32m 8s. [2025-11-13 06:12:48,582][__main__][INFO] - Starting iteration 531. [2025-11-13 06:12:49,090][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 53 and human policies 1. [2025-11-13 06:12:49,091][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 06:13:09,079][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:13:09,161][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:13:10,771][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:13:11,235][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:13:12,032][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:13:12,798][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:13:13,434][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:13:15,549][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:13:16,254][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:13:16,666][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:13:20,157][__main__][INFO] - Number of regex retries in iteration 531: 10 [2025-11-13 06:13:20,157][__main__][INFO] - agents played in iteration 531 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 06:13:21,006][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:13:21,031][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:13:21,057][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:13:21,080][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:13:21,080][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 06:13:21,081][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 06:13:21,882][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 06:13:22,353][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 06:13:22,873][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 06:13:23,383][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 06:13:23,895][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 06:13:24,405][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 06:13:24,918][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 06:13:25,434][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 06:13:25,946][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 06:13:26,458][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 06:13:26,968][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 06:13:27,483][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 06:13:28,006][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 06:13:28,516][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 06:13:29,026][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 06:13:29,540][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 06:13:30,049][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 06:13:30,559][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 06:13:31,073][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 06:13:31,582][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 06:13:32,102][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 06:13:32,611][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 06:13:33,126][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 06:13:33,628][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 06:13:34,130][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 06:13:34,633][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 06:13:35,134][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 06:13:35,635][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 06:13:36,138][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 06:13:36,639][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 06:13:37,140][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 06:13:37,641][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 06:13:38,142][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 06:13:38,645][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 06:13:39,146][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 06:13:39,649][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 06:13:40,155][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 06:13:40,657][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 06:13:41,162][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 06:13:41,664][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 06:13:42,166][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 06:13:42,671][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 06:13:43,173][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 06:13:43,679][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 06:13:44,195][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 06:13:44,699][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 06:13:45,222][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 06:13:45,725][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 06:13:46,227][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 06:13:46,732][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 06:13:47,233][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 06:13:47,735][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 06:13:48,237][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 06:13:48,739][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 06:13:49,243][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 06:13:49,744][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 06:13:50,246][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 06:13:50,749][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 06:13:51,252][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 06:13:51,754][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 06:13:52,255][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 06:13:52,757][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 06:13:53,260][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 06:13:53,759][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 06:13:54,260][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10874 tokens. [2025-11-13 06:13:54,987][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.26%, Current % of VRAM taken: 59.71%, Block Peak % of device VRAM: 62.35%, ΔTime: 00:00:33 [2025-11-13 06:13:55,734][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 06:13:55,736][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 06:13:55,737][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 06:13:56,609][__main__][INFO] - Iteration 532 took 1m 7s (46.01% Gen, 52.70% Train). Generation: 31s, Training: 35s. Estimated remaining time: 48h 11m 45s. Estimated total time: 56h 16m 0s. Time estimates for 10 more iterations: 11m 15s, 100 more iterations: 1h 52m 32s, 500 more iterations: 9h 22m 40s. [2025-11-13 06:13:56,611][__main__][INFO] - Starting iteration 532. [2025-11-13 06:13:57,091][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 53 and human policies 1. [2025-11-13 06:13:57,091][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 06:14:12,332][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:14:13,392][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:14:13,396][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:14:14,109][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:14:15,326][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:14:16,288][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:14:19,829][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:14:20,771][__main__][INFO] - Number of regex retries in iteration 532: 7 [2025-11-13 06:14:20,771][__main__][INFO] - agents played in iteration 532 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 06:14:21,678][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:14:21,701][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:14:21,725][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:14:21,764][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:14:21,765][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 06:14:21,765][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 06:14:22,586][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 06:14:23,056][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 06:14:23,577][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 06:14:24,094][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 06:14:24,620][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 06:14:25,136][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 06:14:25,651][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 06:14:26,167][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 06:14:26,683][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 06:14:27,203][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 06:14:27,717][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 06:14:28,230][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 06:14:28,747][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 06:14:29,259][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 06:14:29,772][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 06:14:30,286][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 06:14:30,803][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 06:14:31,331][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 06:14:31,846][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 06:14:32,363][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 06:14:32,875][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 06:14:33,387][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 06:14:33,909][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 06:14:34,415][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 06:14:34,925][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 06:14:35,435][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 06:14:35,945][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 06:14:36,453][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 06:14:36,960][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 06:14:37,472][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 06:14:37,981][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 06:14:38,488][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 06:14:38,997][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 06:14:39,518][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 06:14:40,027][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 06:14:40,548][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 06:14:41,062][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 06:14:41,571][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 06:14:42,080][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 06:14:42,593][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 06:14:43,127][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 06:14:43,638][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 06:14:44,149][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 06:14:44,660][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 06:14:45,166][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 06:14:45,670][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 06:14:46,176][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 06:14:46,680][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 06:14:47,186][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 06:14:47,695][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 06:14:48,199][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 06:14:48,716][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 06:14:49,219][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 06:14:49,722][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 06:14:50,228][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 06:14:50,730][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 06:14:51,239][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 06:14:51,741][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 06:14:52,245][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 06:14:52,748][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 06:14:53,252][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 06:14:53,754][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 06:14:54,259][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 06:14:54,761][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 06:14:55,266][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10879 tokens. [2025-11-13 06:14:56,135][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.26%, Current % of VRAM taken: 59.71%, Block Peak % of device VRAM: 62.32%, ΔTime: 00:00:33 [2025-11-13 06:14:56,782][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 06:14:56,784][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 06:14:56,785][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 06:14:57,830][__main__][INFO] - Iteration 533 took 1m 0s (38.99% Gen, 59.29% Train). Generation: 23s, Training: 36s. Estimated remaining time: 42h 31m 43s. Estimated total time: 50h 36m 59s. Time estimates for 10 more iterations: 10m 7s, 100 more iterations: 1h 41m 13s, 500 more iterations: 8h 26m 9s. [2025-11-13 06:14:57,833][__main__][INFO] - Starting iteration 533. [2025-11-13 06:14:58,315][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 53 and human policies 1. [2025-11-13 06:14:58,316][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 06:15:22,683][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:15:23,453][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:15:25,507][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:15:25,510][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:15:26,489][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:15:27,678][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:15:29,050][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:15:32,235][__main__][INFO] - Number of regex retries in iteration 533: 7 [2025-11-13 06:15:32,235][__main__][INFO] - agents played in iteration 533 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 06:15:33,093][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:15:33,116][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:15:33,139][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:15:33,162][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:15:33,162][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 06:15:33,163][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 06:15:34,003][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 06:15:34,468][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 06:15:34,989][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 06:15:35,503][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 06:15:36,013][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 06:15:36,538][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 06:15:37,054][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 06:15:37,571][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 06:15:38,083][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 06:15:38,590][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 06:15:39,101][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 06:15:39,612][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 06:15:40,126][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 06:15:40,633][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 06:15:41,142][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 06:15:41,660][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 06:15:42,168][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 06:15:42,681][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 06:15:43,192][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 06:15:43,703][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 06:15:44,224][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 06:15:44,734][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 06:15:50,856][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 06:15:51,362][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 06:15:51,866][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 06:15:52,369][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 06:15:52,872][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 06:15:53,378][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 06:15:53,882][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 06:15:54,385][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 06:15:54,891][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 06:15:55,394][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 06:15:55,900][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 06:15:56,403][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 06:15:56,906][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 06:15:57,411][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 06:15:57,915][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 06:15:58,419][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 06:15:58,922][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 06:15:59,426][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 06:15:59,943][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 06:16:00,448][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 06:16:00,960][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 06:16:01,461][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 06:16:01,963][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 06:16:02,468][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 06:16:02,971][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 06:16:03,476][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 06:16:03,983][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 06:16:04,486][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 06:16:04,988][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 06:16:05,491][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 06:16:05,994][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 06:16:06,501][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10877 tokens. [2025-11-13 06:16:07,244][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.26%, Current % of VRAM taken: 59.71%, Block Peak % of device VRAM: 62.32%, ΔTime: 00:00:33 [2025-11-13 06:16:07,990][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 06:16:07,992][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 06:16:07,994][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 06:16:08,918][__main__][INFO] - Iteration 534 took 1m 10s (48.04% Gen, 50.65% Train). Generation: 33s, Training: 35s. Estimated remaining time: 50h 43m 43s. Estimated total time: 58h 50m 10s. Time estimates for 10 more iterations: 11m 46s, 100 more iterations: 1h 57m 40s, 500 more iterations: 9h 48m 21s. [2025-11-13 06:16:08,920][__main__][INFO] - Starting iteration 534. [2025-11-13 06:16:09,388][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 53 and human policies 1. [2025-11-13 06:16:09,389][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 06:16:26,334][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:16:27,029][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:16:27,033][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:16:27,198][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:16:27,547][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:16:28,310][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:16:28,311][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:16:29,077][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:16:32,203][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:16:32,205][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:16:32,379][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:16:32,701][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:16:36,284][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:16:36,641][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:16:37,760][__main__][INFO] - Number of regex retries in iteration 534: 14 [2025-11-13 06:16:37,761][__main__][INFO] - agents played in iteration 534 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 06:16:38,651][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:16:38,684][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:16:38,712][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:16:38,736][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:16:38,737][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 06:16:38,738][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 06:16:39,581][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 06:16:40,051][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 06:16:40,575][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 06:16:41,090][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 06:16:41,601][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 06:16:42,114][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 06:16:42,626][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 06:16:43,153][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 06:16:43,668][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 06:16:44,181][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 06:16:44,694][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 06:16:45,206][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 06:16:45,719][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 06:16:46,231][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 06:16:46,745][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 06:16:47,278][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 06:16:47,802][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 06:16:48,327][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 06:16:48,843][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 06:16:49,355][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 06:16:49,869][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 06:16:50,378][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 06:16:50,887][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 06:16:51,400][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 06:16:51,908][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 06:16:52,419][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 06:16:52,927][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 06:16:53,433][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 06:16:53,941][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 06:16:54,450][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 06:16:54,965][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 06:16:55,472][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 06:16:55,982][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 06:16:56,492][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 06:16:57,006][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 06:16:57,517][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 06:16:58,028][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 06:16:58,536][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 06:16:59,053][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 06:16:59,560][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 06:17:00,075][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 06:17:00,584][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 06:17:01,090][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 06:17:01,600][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 06:17:02,106][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 06:17:02,608][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 06:17:03,112][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 06:17:03,615][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 06:17:04,116][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 06:17:04,615][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 06:17:05,115][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 06:17:05,616][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 06:17:06,116][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 06:17:06,617][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 06:17:07,117][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 06:17:07,616][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 06:17:08,118][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 06:17:08,620][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 06:17:09,122][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 06:17:09,624][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 06:17:10,125][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 06:17:10,632][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 06:17:11,134][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 06:17:11,637][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 06:17:12,150][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10878 tokens. [2025-11-13 06:17:12,948][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.25%, Current % of VRAM taken: 59.71%, Block Peak % of device VRAM: 62.31%, ΔTime: 00:00:33 [2025-11-13 06:17:13,602][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 06:17:13,604][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 06:17:13,605][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 06:17:14,403][__main__][INFO] - Iteration 535 took 1m 5s (43.64% Gen, 55.13% Train). Generation: 28s, Training: 35s. Estimated remaining time: 46h 3m 15s. Estimated total time: 54h 10m 48s. Time estimates for 10 more iterations: 10m 50s, 100 more iterations: 1h 48m 21s, 500 more iterations: 9h 1m 48s. [2025-11-13 06:17:14,405][__main__][INFO] - Starting iteration 535. [2025-11-13 06:17:14,889][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 53 and human policies 1. [2025-11-13 06:17:14,890][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 06:17:33,474][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:17:33,476][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:17:33,477][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:17:33,576][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:17:34,579][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:17:35,214][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:17:35,541][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:17:37,223][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:17:37,266][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:17:37,268][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:17:38,463][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:17:38,464][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:17:38,468][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:17:39,232][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:17:41,036][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:17:43,326][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:17:44,518][__main__][INFO] - Number of regex retries in iteration 535: 16 [2025-11-13 06:17:44,519][__main__][INFO] - agents played in iteration 535 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 06:17:45,339][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:17:45,367][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:17:45,393][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:17:45,417][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:17:45,417][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 06:17:45,418][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 06:17:46,241][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 06:17:46,709][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 06:17:47,224][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 06:17:47,733][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 06:17:48,247][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 06:17:48,759][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 06:17:49,271][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 06:17:49,781][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 06:17:50,303][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 06:17:50,813][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 06:17:51,320][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 06:17:51,829][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 06:17:52,340][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 06:17:52,861][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 06:17:53,372][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 06:17:53,885][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 06:17:54,392][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 06:17:54,904][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 06:17:55,421][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 06:17:55,935][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 06:17:56,444][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 06:17:56,961][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 06:17:57,472][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 06:17:57,983][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 06:17:58,490][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 06:17:58,999][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 06:17:59,513][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 06:18:00,022][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 06:18:00,810][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 06:18:02,038][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 06:18:02,553][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 06:18:03,060][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 06:18:03,579][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 06:18:04,086][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 06:18:04,598][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 06:18:05,102][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 06:18:05,610][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 06:18:06,118][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 06:18:06,622][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 06:18:07,128][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 06:18:07,638][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 06:18:08,148][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 06:18:08,670][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 06:18:09,179][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 06:18:09,686][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 06:18:10,192][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 06:18:10,698][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 06:18:11,205][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 06:18:11,705][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 06:18:12,207][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 06:18:12,714][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 06:18:13,218][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 06:18:13,721][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 06:18:14,224][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 06:18:14,726][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 06:18:15,227][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 06:18:15,728][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 06:18:16,233][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 06:18:16,737][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 06:18:17,239][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 06:18:17,741][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 06:18:18,244][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 06:18:18,750][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 06:18:19,253][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 06:18:19,758][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10879 tokens. [2025-11-13 06:18:20,627][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.23%, Current % of VRAM taken: 59.68%, Block Peak % of device VRAM: 62.34%, ΔTime: 00:00:34 [2025-11-13 06:18:21,277][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 06:18:21,279][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 06:18:21,280][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 06:18:22,272][__main__][INFO] - Iteration 536 took 1m 7s (43.97% Gen, 54.55% Train). Generation: 29s, Training: 36s. Estimated remaining time: 48h 0m 30s. Estimated total time: 56h 9m 11s. Time estimates for 10 more iterations: 11m 13s, 100 more iterations: 1h 52m 18s, 500 more iterations: 9h 21m 31s. [2025-11-13 06:18:22,274][__main__][INFO] - Starting iteration 536. [2025-11-13 06:18:22,786][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 53 and human policies 1. [2025-11-13 06:18:22,787][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 06:18:43,284][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:18:45,217][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:18:45,293][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:18:46,430][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:18:46,520][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:18:47,513][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:18:48,045][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:18:48,096][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:18:49,154][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:18:50,502][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:18:51,041][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:18:51,972][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:18:53,347][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:18:54,726][__main__][INFO] - Number of regex retries in iteration 536: 13 [2025-11-13 06:18:54,727][__main__][INFO] - agents played in iteration 536 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 06:18:55,680][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:18:55,709][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:18:55,737][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:18:55,763][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:18:55,764][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 06:18:55,765][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 06:18:56,603][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 06:18:57,071][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 06:18:57,590][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 06:18:58,101][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 06:18:58,615][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 06:18:59,127][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 06:18:59,641][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 06:19:00,154][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 06:19:00,670][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 06:19:01,184][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 06:19:01,697][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 06:19:02,208][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 06:19:02,726][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 06:19:03,239][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 06:19:03,749][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 06:19:04,259][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 06:19:04,772][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 06:19:05,284][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 06:19:05,795][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 06:19:06,304][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 06:19:06,816][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 06:19:07,320][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 06:19:07,831][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 06:19:08,340][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 06:19:08,853][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 06:19:09,367][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 06:19:09,878][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 06:19:10,388][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 06:19:10,900][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 06:19:11,409][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 06:19:11,925][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 06:19:12,435][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 06:19:12,944][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 06:19:13,453][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 06:19:13,966][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 06:19:14,476][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 06:19:14,988][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 06:19:15,494][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 06:19:16,001][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 06:19:16,502][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 06:19:17,010][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 06:19:17,511][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 06:19:18,013][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 06:19:18,516][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 06:19:19,018][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 06:19:19,521][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 06:19:20,020][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 06:19:20,522][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 06:19:21,032][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 06:19:21,535][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 06:19:22,037][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 06:19:22,540][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 06:19:23,050][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 06:19:23,553][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 06:19:24,055][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 06:19:24,557][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 06:19:25,063][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 06:19:25,565][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 06:19:26,066][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 06:19:26,575][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 06:19:27,079][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 06:19:27,599][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 06:19:28,101][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 06:19:28,605][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 06:19:29,109][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10876 tokens. [2025-11-13 06:19:29,891][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.22%, Current % of VRAM taken: 59.67%, Block Peak % of device VRAM: 62.35%, ΔTime: 00:00:33 [2025-11-13 06:19:30,664][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 06:19:30,666][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 06:19:30,667][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 06:19:31,561][__main__][INFO] - Iteration 537 took 1m 8s (46.44% Gen, 52.26% Train). Generation: 31s, Training: 35s. Estimated remaining time: 49h 8m 56s. Estimated total time: 57h 18m 46s. Time estimates for 10 more iterations: 11m 27s, 100 more iterations: 1h 54m 37s, 500 more iterations: 9h 33m 7s. [2025-11-13 06:19:31,563][__main__][INFO] - Starting iteration 537. [2025-11-13 06:19:32,050][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 53 and human policies 1. [2025-11-13 06:19:32,050][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 06:19:49,561][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:19:49,727][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:19:49,729][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:19:50,531][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:19:51,674][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:19:51,943][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:19:57,380][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:19:58,810][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:20:01,659][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:20:01,661][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:20:01,701][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:20:02,667][__main__][INFO] - Number of regex retries in iteration 537: 11 [2025-11-13 06:20:02,668][__main__][INFO] - agents played in iteration 537 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 06:20:03,555][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:20:03,581][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:20:03,606][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:20:03,631][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:20:03,632][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 06:20:03,633][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 06:20:04,439][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 06:20:04,901][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 06:20:05,417][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 06:20:05,938][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 06:20:06,449][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 06:20:06,962][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 06:20:07,473][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 06:20:07,986][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 06:20:08,510][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 06:20:09,023][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 06:20:09,533][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 06:20:10,045][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 06:20:10,554][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 06:20:11,063][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 06:20:11,572][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 06:20:12,083][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 06:20:12,595][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 06:20:13,105][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 06:20:13,613][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 06:20:14,127][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 06:20:14,638][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 06:20:15,148][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 06:20:15,657][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 06:20:16,164][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 06:20:16,674][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 06:20:17,187][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 06:20:17,705][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 06:20:18,215][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 06:20:18,726][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 06:20:19,244][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 06:20:19,755][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 06:20:20,265][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 06:20:20,778][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 06:20:21,287][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 06:20:21,825][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 06:20:22,337][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 06:20:22,855][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 06:20:23,367][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 06:20:23,877][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 06:20:24,391][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 06:20:24,900][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 06:20:25,408][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 06:20:25,921][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 06:20:26,432][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 06:20:26,940][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 06:20:27,441][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 06:20:27,942][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 06:20:28,445][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 06:20:28,950][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 06:20:29,453][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 06:20:29,958][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 06:20:30,459][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 06:20:30,962][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 06:20:31,464][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 06:20:31,967][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 06:20:32,475][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 06:20:32,979][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 06:20:33,484][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 06:20:34,020][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 06:20:34,523][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 06:20:35,040][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 06:20:35,551][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 06:20:36,061][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 06:20:36,578][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 06:20:37,090][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10878 tokens. [2025-11-13 06:20:37,938][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.30%, Current % of VRAM taken: 59.75%, Block Peak % of device VRAM: 62.38%, ΔTime: 00:00:33 [2025-11-13 06:20:38,570][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 06:20:38,572][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 06:20:38,574][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 06:20:39,375][__main__][INFO] - Iteration 538 took 1m 7s (45.48% Gen, 53.33% Train). Generation: 30s, Training: 35s. Estimated remaining time: 47h 55m 20s. Estimated total time: 56h 6m 18s. Time estimates for 10 more iterations: 11m 13s, 100 more iterations: 1h 52m 12s, 500 more iterations: 9h 21m 3s. [2025-11-13 06:20:39,378][__main__][INFO] - Starting iteration 538. [2025-11-13 06:20:39,882][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 53 and human policies 1. [2025-11-13 06:20:39,883][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 06:20:58,482][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:20:58,485][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:20:58,610][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:20:59,281][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:20:59,341][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:20:59,344][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:21:00,481][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:21:01,268][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:21:02,094][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:21:02,373][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:21:02,375][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:21:02,552][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:21:02,554][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:21:02,983][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:21:03,040][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:21:03,337][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:21:03,428][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:21:03,705][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:21:04,076][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:21:04,246][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:21:04,430][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:21:05,341][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:21:05,531][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:21:05,622][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:21:06,209][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:21:08,055][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:21:08,332][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:21:09,230][__main__][INFO] - Number of regex retries in iteration 538: 27 [2025-11-13 06:21:09,231][__main__][INFO] - agents played in iteration 538 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 06:21:10,042][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:21:10,065][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:21:10,089][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:21:10,112][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:21:10,113][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 06:21:10,114][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 06:21:10,973][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 06:21:11,443][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 06:21:11,964][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 06:21:12,477][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 06:21:12,987][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 06:21:13,491][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 06:21:13,996][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 06:21:14,503][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 06:21:15,007][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 06:21:15,514][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 06:21:16,044][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 06:21:16,548][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 06:21:17,057][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 06:21:17,566][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 06:21:18,074][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 06:21:18,588][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 06:21:19,093][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 06:21:19,606][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 06:21:20,128][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 06:21:20,642][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 06:21:21,155][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 06:21:21,663][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 06:21:22,172][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 06:21:22,686][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 06:21:23,198][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 06:21:23,717][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 06:21:24,225][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 06:21:24,741][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 06:21:25,252][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 06:21:25,760][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 06:21:26,270][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 06:21:26,777][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 06:21:27,288][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 06:21:27,802][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 06:21:28,312][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 06:21:28,821][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 06:21:29,341][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 06:21:29,850][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 06:21:30,366][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 06:21:30,868][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 06:21:31,374][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 06:21:31,876][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 06:21:32,377][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 06:21:32,880][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 06:21:33,382][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 06:21:33,883][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 06:21:34,386][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 06:21:34,888][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 06:21:35,389][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 06:21:35,890][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 06:21:36,393][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 06:21:36,897][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 06:21:37,399][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 06:21:37,901][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 06:21:38,402][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 06:21:38,902][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 06:21:39,405][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 06:21:39,908][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 06:21:40,409][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 06:21:40,910][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 06:21:41,414][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 06:21:41,917][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 06:21:42,420][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 06:21:42,923][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 06:21:43,440][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10878 tokens. [2025-11-13 06:21:44,228][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.17%, Current % of VRAM taken: 59.62%, Block Peak % of device VRAM: 62.31%, ΔTime: 00:00:33 [2025-11-13 06:21:44,986][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 06:21:44,988][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 06:21:44,990][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 06:21:45,974][__main__][INFO] - Iteration 539 took 1m 6s (44.40% Gen, 54.10% Train). Generation: 29s, Training: 35s. Estimated remaining time: 46h 52m 33s. Estimated total time: 55h 4m 37s. Time estimates for 10 more iterations: 11m 0s, 100 more iterations: 1h 50m 9s, 500 more iterations: 9h 10m 46s. [2025-11-13 06:21:45,976][__main__][INFO] - Starting iteration 539. [2025-11-13 06:21:46,465][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 53 and human policies 1. [2025-11-13 06:21:46,465][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 06:22:03,538][mllm.models.large_language_model_local][WARNING] - Response Proposal: 5 hats, 5 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:22:03,777][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:22:04,618][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:22:04,702][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:22:04,705][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:22:04,706][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:22:05,371][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:22:05,398][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:22:07,954][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:22:09,792][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:22:10,081][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:22:11,586][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:22:12,928][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:22:13,555][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:22:14,856][__main__][INFO] - Number of regex retries in iteration 539: 14 [2025-11-13 06:22:14,857][__main__][INFO] - agents played in iteration 539 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 06:22:15,734][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.54%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:22:15,759][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.54%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:22:15,785][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.54%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:22:15,808][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.54%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:22:15,808][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 06:22:15,809][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 06:22:16,596][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 06:22:17,062][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 06:22:17,584][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 06:22:18,095][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 06:22:18,606][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 06:22:19,120][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 06:22:19,630][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 06:22:20,151][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 06:22:20,665][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 06:22:21,181][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 06:22:21,700][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 06:22:22,211][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 06:22:22,722][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 06:22:23,243][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 06:22:23,755][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 06:22:24,267][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 06:22:24,780][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 06:22:25,291][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 06:22:25,799][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 06:22:26,308][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 06:22:26,818][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 06:22:27,327][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 06:22:27,836][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 06:22:28,365][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 06:22:28,873][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 06:22:29,384][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 06:22:29,894][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 06:22:30,404][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 06:22:30,913][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 06:22:31,424][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 06:22:31,936][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 06:22:32,461][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 06:22:32,979][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 06:22:33,492][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 06:22:34,003][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 06:22:34,514][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 06:22:35,038][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 06:22:35,548][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 06:22:36,061][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 06:22:36,569][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 06:22:37,081][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 06:22:37,590][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 06:22:38,099][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 06:22:38,610][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 06:22:39,120][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 06:22:39,627][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 06:22:40,138][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 06:22:40,640][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 06:22:41,142][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 06:22:41,648][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 06:22:42,151][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 06:22:42,653][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 06:22:43,157][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 06:22:43,660][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 06:22:44,176][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 06:22:44,680][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 06:22:45,181][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 06:22:45,685][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 06:22:46,187][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 06:22:46,691][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 06:22:47,194][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 06:22:47,699][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 06:22:48,205][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 06:22:48,708][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 06:22:49,212][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10877 tokens. [2025-11-13 06:22:49,992][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.23%, Current % of VRAM taken: 59.68%, Block Peak % of device VRAM: 62.35%, ΔTime: 00:00:33 [2025-11-13 06:22:50,663][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 06:22:50,665][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 06:22:50,667][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 06:22:51,482][__main__][INFO] - Iteration 540 took 1m 5s (43.67% Gen, 55.08% Train). Generation: 28s, Training: 35s. Estimated remaining time: 45h 57m 43s. Estimated total time: 54h 10m 53s. Time estimates for 10 more iterations: 10m 50s, 100 more iterations: 1h 48m 21s, 500 more iterations: 9h 1m 48s. [2025-11-13 06:22:51,484][__main__][INFO] - Starting iteration 540. [2025-11-13 06:22:51,986][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 53 and human policies 1. [2025-11-13 06:22:51,987][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 06:23:10,092][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:23:10,301][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:23:10,302][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:23:10,307][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:23:11,783][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:23:12,219][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:23:12,221][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:23:12,223][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:23:12,307][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:23:13,139][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:23:13,318][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:23:13,709][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:23:14,986][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:23:15,931][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:23:16,222][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:23:17,283][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:23:17,885][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:23:17,985][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:23:18,260][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:23:18,356][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:23:18,414][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:23:20,108][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:23:21,276][__main__][INFO] - Number of regex retries in iteration 540: 22 [2025-11-13 06:23:21,277][__main__][INFO] - agents played in iteration 540 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 06:23:22,106][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:23:22,129][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:23:22,153][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:23:22,176][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:23:22,176][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 06:23:22,177][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 06:23:22,943][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 06:23:23,415][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 06:23:23,929][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 06:23:24,433][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 06:23:24,948][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 06:23:25,451][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 06:23:25,976][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 06:23:26,482][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 06:23:26,990][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 06:23:27,498][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 06:23:28,004][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 06:23:28,512][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 06:23:29,018][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 06:23:29,528][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 06:23:30,040][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 06:23:30,549][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 06:23:31,060][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 06:23:31,569][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 06:23:32,079][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 06:23:32,595][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 06:23:33,105][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 06:23:33,613][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 06:23:34,125][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 06:23:34,640][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 06:23:35,177][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 06:23:35,690][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 06:23:36,201][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 06:23:36,712][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 06:23:37,226][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 06:23:37,736][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 06:23:38,247][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 06:23:38,758][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 06:23:39,273][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 06:23:39,784][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 06:23:40,301][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 06:23:40,810][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 06:23:41,320][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 06:23:41,831][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 06:23:42,338][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 06:23:42,849][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 06:23:43,361][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 06:23:43,875][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 06:23:44,386][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 06:23:44,888][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 06:23:45,392][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 06:23:45,896][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 06:23:46,398][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 06:23:46,916][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 06:23:47,418][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 06:23:47,922][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 06:23:48,424][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 06:23:48,926][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 06:23:49,433][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 06:23:49,935][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 06:23:50,438][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 06:23:50,942][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 06:23:51,445][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 06:23:51,947][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 06:23:52,449][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 06:23:52,950][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 06:23:53,458][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 06:23:53,963][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 06:23:54,468][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 06:23:54,969][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 06:23:55,473][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10878 tokens. [2025-11-13 06:23:56,293][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.20%, Current % of VRAM taken: 59.65%, Block Peak % of device VRAM: 62.32%, ΔTime: 00:00:33 [2025-11-13 06:23:57,049][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 06:23:57,051][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 06:23:57,053][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 06:23:58,798][__main__][INFO] - Iteration 541 took 1m 6s (43.84% Gen, 53.55% Train). Generation: 29s, Training: 35s. Estimated remaining time: 47h 26m 19s. Estimated total time: 55h 40m 36s. Time estimates for 10 more iterations: 11m 8s, 100 more iterations: 1h 51m 21s, 500 more iterations: 9h 16m 46s. [2025-11-13 06:23:58,800][__main__][INFO] - Starting iteration 541. [2025-11-13 06:23:59,292][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 54 and human policies 1. [2025-11-13 06:23:59,292][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 06:24:22,647][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:24:22,821][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:24:22,822][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:24:22,825][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:24:23,754][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:24:24,462][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:24:24,577][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:24:24,867][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:24:27,102][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:24:27,287][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:24:27,580][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:24:27,672][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:24:27,764][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:24:28,241][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:24:29,372][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:24:30,118][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:24:30,622][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:24:31,432][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:24:33,076][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:24:34,261][__main__][INFO] - Number of regex retries in iteration 541: 19 [2025-11-13 06:24:34,261][__main__][INFO] - agents played in iteration 541 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 06:24:35,104][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:24:35,127][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:24:35,152][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:24:35,177][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:24:35,177][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 06:24:35,178][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 06:24:35,994][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 06:24:36,459][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 06:24:36,980][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 06:24:37,494][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 06:24:38,010][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 06:24:38,519][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 06:24:39,027][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 06:24:39,542][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 06:24:40,053][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 06:24:40,567][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 06:24:41,075][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 06:24:41,588][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 06:24:42,100][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 06:24:42,610][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 06:24:43,125][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 06:24:43,638][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 06:24:44,149][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 06:24:44,665][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 06:24:45,174][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 06:24:45,686][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 06:24:46,199][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 06:24:46,712][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 06:24:47,227][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 06:24:47,737][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 06:24:48,250][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 06:24:48,761][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 06:24:49,270][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 06:24:49,777][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 06:24:50,293][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 06:24:50,799][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 06:24:51,313][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 06:24:51,824][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 06:24:52,335][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 06:24:52,850][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 06:24:53,361][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 06:24:53,870][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 06:24:54,375][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 06:24:54,881][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 06:24:55,387][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 06:24:55,889][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 06:24:56,393][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 06:24:56,896][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 06:24:57,400][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 06:24:57,921][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 06:24:58,420][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 06:24:58,921][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 06:24:59,428][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 06:24:59,929][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 06:25:00,430][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 06:25:00,932][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 06:25:01,437][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 06:25:01,943][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 06:25:02,446][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 06:25:02,950][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 06:25:03,453][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 06:25:03,958][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 06:25:04,462][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 06:25:04,964][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 06:25:05,469][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 06:25:05,980][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 06:25:06,486][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 06:25:06,997][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 06:25:07,499][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 06:25:08,003][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 06:25:08,513][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10877 tokens. [2025-11-13 06:25:09,324][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.18%, Current % of VRAM taken: 59.63%, Block Peak % of device VRAM: 62.31%, ΔTime: 00:00:33 [2025-11-13 06:25:09,987][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 06:25:09,990][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 06:25:09,992][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 06:25:10,791][__main__][INFO] - Iteration 542 took 1m 11s (48.91% Gen, 49.97% Train). Generation: 34s, Training: 35s. Estimated remaining time: 51h 19m 31s. Estimated total time: 59h 35m 1s. Time estimates for 10 more iterations: 11m 55s, 100 more iterations: 1h 59m 10s, 500 more iterations: 9h 55m 50s. [2025-11-13 06:25:10,793][__main__][INFO] - Starting iteration 542. [2025-11-13 06:25:11,290][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 54 and human policies 1. [2025-11-13 06:25:11,290][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 06:25:22,869][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:25:22,871][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:25:22,928][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:25:23,668][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:25:23,954][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:25:23,956][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:25:24,044][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:25:24,048][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:25:24,132][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:25:24,676][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:25:24,679][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:25:24,741][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:25:24,826][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:25:25,163][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:25:25,165][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:25:25,168][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:25:25,788][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:25:26,048][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:25:26,051][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:25:26,594][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:25:26,817][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:25:27,708][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:25:28,732][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:25:29,188][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:25:29,276][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:25:29,372][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:25:29,913][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:25:30,173][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:25:30,976][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:25:31,147][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:25:31,519][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:25:32,039][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:25:32,674][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:25:34,213][__main__][INFO] - Number of regex retries in iteration 542: 33 [2025-11-13 06:25:34,214][__main__][INFO] - agents played in iteration 542 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 06:25:35,039][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:25:35,063][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:25:35,086][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:25:35,109][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:25:35,109][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 06:25:35,110][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 06:25:35,892][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 06:25:36,358][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 06:25:36,865][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 06:25:37,372][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 06:25:37,876][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 06:25:38,382][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 06:25:38,898][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 06:25:39,405][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 06:25:39,920][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 06:25:40,425][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 06:25:40,930][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 06:25:41,438][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 06:25:41,944][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 06:25:42,455][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 06:25:42,970][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 06:25:43,482][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 06:25:44,005][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 06:25:44,516][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 06:25:45,023][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 06:25:45,533][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 06:25:46,040][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 06:25:46,551][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 06:25:47,061][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 06:25:47,570][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 06:25:48,082][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 06:25:48,589][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 06:25:49,105][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 06:25:49,613][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 06:25:50,122][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 06:25:50,639][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 06:25:51,152][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 06:25:51,660][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 06:25:52,465][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 06:25:54,480][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 06:25:54,988][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 06:25:55,496][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 06:25:56,011][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 06:25:56,521][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 06:25:57,035][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 06:25:57,537][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 06:25:58,040][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 06:25:58,545][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 06:25:59,048][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 06:25:59,554][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 06:26:00,057][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 06:26:00,562][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 06:26:01,066][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 06:26:01,570][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 06:26:02,075][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 06:26:02,576][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 06:26:03,080][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 06:26:03,583][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 06:26:04,085][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 06:26:04,587][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 06:26:05,089][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 06:26:05,593][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 06:26:06,122][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 06:26:06,626][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 06:26:07,130][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 06:26:07,635][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 06:26:08,137][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 06:26:08,646][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 06:26:09,156][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 06:26:09,665][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 06:26:10,181][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10877 tokens. [2025-11-13 06:26:11,016][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.16%, Current % of VRAM taken: 59.61%, Block Peak % of device VRAM: 62.31%, ΔTime: 00:00:35 [2025-11-13 06:26:11,704][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 06:26:11,707][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 06:26:11,709][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 06:26:12,626][__main__][INFO] - Iteration 543 took 1m 1s (37.37% Gen, 61.13% Train). Generation: 22s, Training: 37s. Estimated remaining time: 42h 50m 20s. Estimated total time: 51h 6m 51s. Time estimates for 10 more iterations: 10m 13s, 100 more iterations: 1h 42m 13s, 500 more iterations: 8h 31m 8s. [2025-11-13 06:26:12,629][__main__][INFO] - Starting iteration 543. [2025-11-13 06:26:13,121][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 54 and human policies 1. [2025-11-13 06:26:13,122][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 06:26:36,267][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:26:36,359][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:26:36,468][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:26:37,520][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:26:37,609][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:26:38,181][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:26:38,472][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:26:38,561][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:26:38,654][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:26:38,742][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:26:39,255][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:26:39,879][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:26:40,948][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:26:41,712][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:26:41,984][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:26:42,824][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:26:43,881][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:26:44,280][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:26:44,405][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:26:44,972][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:26:46,870][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:26:48,340][__main__][INFO] - Number of regex retries in iteration 543: 21 [2025-11-13 06:26:48,341][__main__][INFO] - agents played in iteration 543 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 06:26:49,178][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:26:49,201][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:26:49,224][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:26:49,247][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:26:49,248][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 06:26:49,249][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 06:26:50,031][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 06:26:50,513][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 06:26:51,027][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 06:26:51,539][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 06:26:52,051][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 06:26:52,562][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 06:26:53,074][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 06:26:53,587][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 06:26:54,104][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 06:26:54,622][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 06:26:55,132][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 06:26:55,648][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 06:26:56,162][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 06:26:56,672][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 06:26:57,187][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 06:26:57,698][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 06:26:58,213][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 06:26:58,728][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 06:26:59,235][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 06:26:59,749][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 06:27:00,253][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 06:27:00,754][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 06:27:01,255][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 06:27:01,756][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 06:27:02,262][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 06:27:02,762][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 06:27:03,264][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 06:27:03,764][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 06:27:04,265][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 06:27:04,767][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 06:27:05,267][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 06:27:05,767][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 06:27:06,274][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 06:27:06,774][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 06:27:07,277][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 06:27:07,778][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 06:27:08,280][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 06:27:08,783][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 06:27:09,287][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 06:27:09,793][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 06:27:10,298][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 06:27:10,801][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 06:27:11,304][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 06:27:11,806][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 06:27:12,307][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 06:27:12,822][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 06:27:13,324][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 06:27:13,830][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 06:27:14,337][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 06:27:14,843][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 06:27:15,351][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 06:27:15,858][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 06:27:16,359][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 06:27:16,869][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 06:27:17,379][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 06:27:17,890][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 06:27:18,398][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 06:27:18,910][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 06:27:19,432][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 06:27:19,944][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 06:27:20,455][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 06:27:20,963][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 06:27:21,472][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 06:27:21,985][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 06:27:22,497][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10880 tokens. [2025-11-13 06:27:23,324][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.25%, Current % of VRAM taken: 59.70%, Block Peak % of device VRAM: 62.32%, ΔTime: 00:00:33 [2025-11-13 06:27:24,088][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 06:27:24,089][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 06:27:24,091][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 06:27:25,016][__main__][INFO] - Iteration 544 took 1m 11s (48.99% Gen, 49.73% Train). Generation: 35s, Training: 35s. Estimated remaining time: 51h 37m 3s. Estimated total time: 59h 54m 47s. Time estimates for 10 more iterations: 11m 58s, 100 more iterations: 1h 59m 49s, 500 more iterations: 9h 59m 7s. [2025-11-13 06:27:25,019][__main__][INFO] - Starting iteration 544. [2025-11-13 06:27:25,513][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 54 and human policies 1. [2025-11-13 06:27:25,513][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 06:27:40,987][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:27:41,152][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:27:41,703][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:27:41,790][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:27:41,960][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:27:42,493][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:27:42,599][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:27:42,605][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:27:43,043][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:27:43,420][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:27:43,491][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:27:43,493][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:27:43,615][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:27:43,706][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:27:43,709][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:27:43,936][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:27:43,939][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:27:47,469][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:27:47,666][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:27:48,448][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:27:48,737][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:27:49,368][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:27:50,589][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:27:50,680][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:27:50,757][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:27:51,054][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:27:51,260][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:27:52,651][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:27:54,170][__main__][INFO] - Number of regex retries in iteration 544: 28 [2025-11-13 06:27:54,170][__main__][INFO] - agents played in iteration 544 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 06:27:55,051][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:27:55,074][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:27:55,097][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:27:55,121][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:27:55,121][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 06:27:55,122][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 06:27:56,050][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 06:27:56,513][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 06:27:57,031][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 06:27:57,555][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 06:27:58,061][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 06:27:58,571][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 06:27:59,081][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 06:27:59,591][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 06:28:00,102][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 06:28:00,617][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 06:28:01,130][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 06:28:01,645][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 06:28:02,155][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 06:28:02,667][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 06:28:03,196][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 06:28:03,711][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 06:28:04,231][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 06:28:04,746][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 06:28:05,261][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 06:28:05,774][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 06:28:06,291][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 06:28:06,807][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 06:28:07,320][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 06:28:07,843][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 06:28:08,358][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 06:28:08,866][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 06:28:09,371][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 06:28:09,877][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 06:28:10,382][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 06:28:10,886][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 06:28:11,391][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 06:28:11,895][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 06:28:12,400][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 06:28:12,904][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 06:28:13,407][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 06:28:13,910][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 06:28:14,421][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 06:28:14,924][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 06:28:15,430][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 06:28:15,939][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 06:28:16,444][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 06:28:16,948][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 06:28:17,453][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 06:28:17,957][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 06:28:18,465][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 06:28:18,970][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 06:28:19,476][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 06:28:19,981][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 06:28:20,484][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 06:28:20,988][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 06:28:21,492][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 06:28:21,998][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 06:28:22,508][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 06:28:23,015][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 06:28:23,525][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 06:28:24,033][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 06:28:24,537][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 06:28:25,052][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 06:28:25,555][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 06:28:26,067][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 06:28:26,581][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 06:28:27,090][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 06:28:27,603][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 06:28:28,113][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 06:28:28,622][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10875 tokens. [2025-11-13 06:28:29,449][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.24%, Current % of VRAM taken: 59.69%, Block Peak % of device VRAM: 62.51%, ΔTime: 00:00:33 [2025-11-13 06:28:30,120][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 06:28:30,122][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 06:28:30,124][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 06:28:30,983][__main__][INFO] - Iteration 545 took 1m 5s (43.77% Gen, 54.92% Train). Generation: 28s, Training: 35s. Estimated remaining time: 46h 14m 43s. Estimated total time: 54h 33m 33s. Time estimates for 10 more iterations: 10m 54s, 100 more iterations: 1h 49m 7s, 500 more iterations: 9h 5m 35s. [2025-11-13 06:28:30,985][__main__][INFO] - Starting iteration 545. [2025-11-13 06:28:31,492][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 54 and human policies 1. [2025-11-13 06:28:31,493][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 06:28:48,641][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:28:48,697][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:28:48,702][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:28:48,784][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:28:48,788][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:28:49,448][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:28:49,517][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:28:49,520][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:28:49,755][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:28:50,508][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:28:50,510][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:28:50,683][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:28:50,687][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:28:50,856][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:28:51,343][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:28:51,731][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:28:51,733][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:28:52,565][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:28:52,567][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:28:52,569][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:28:53,370][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:28:53,372][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:28:53,547][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:28:53,634][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:28:54,390][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:28:54,393][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:28:54,707][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:28:55,655][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:28:55,946][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:28:55,951][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:28:56,528][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:28:56,671][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:28:56,970][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:28:56,972][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:28:57,520][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:28:58,408][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:28:58,411][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:28:58,604][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:28:59,870][__main__][INFO] - Number of regex retries in iteration 545: 38 [2025-11-13 06:28:59,871][__main__][INFO] - agents played in iteration 545 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 06:29:00,689][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:29:00,713][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:29:00,737][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:29:00,760][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:29:00,761][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 06:29:00,762][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 06:29:01,569][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 06:29:02,035][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 06:29:02,550][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 06:29:03,066][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 06:29:03,578][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 06:29:04,087][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 06:29:04,600][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 06:29:05,107][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 06:29:05,617][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 06:29:06,131][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 06:29:06,642][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 06:29:07,159][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 06:29:07,668][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 06:29:08,180][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 06:29:08,691][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 06:29:09,205][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 06:29:09,723][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 06:29:10,230][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 06:29:10,740][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 06:29:11,249][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 06:29:11,760][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 06:29:12,284][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 06:29:12,796][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 06:29:13,309][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 06:29:13,813][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 06:29:14,316][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 06:29:14,819][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 06:29:15,320][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 06:29:15,823][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 06:29:16,325][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 06:29:16,827][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 06:29:17,329][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 06:29:17,830][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 06:29:18,332][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 06:29:18,859][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 06:29:19,360][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 06:29:19,862][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 06:29:20,364][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 06:29:20,865][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 06:29:21,367][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 06:29:21,867][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 06:29:22,372][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 06:29:22,882][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 06:29:23,386][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 06:29:23,890][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 06:29:24,393][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 06:29:24,897][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 06:29:25,403][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 06:29:25,910][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 06:29:26,412][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 06:29:26,915][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 06:29:27,418][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 06:29:27,920][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 06:29:28,422][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 06:29:28,923][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 06:29:29,429][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 06:29:29,932][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 06:29:30,433][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 06:29:30,944][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 06:29:31,448][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 06:29:31,961][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 06:29:32,465][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 06:29:32,968][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 06:29:33,473][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 06:29:33,977][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10877 tokens. [2025-11-13 06:29:34,809][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.24%, Current % of VRAM taken: 59.69%, Block Peak % of device VRAM: 62.41%, ΔTime: 00:00:33 [2025-11-13 06:29:35,555][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 06:29:35,556][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 06:29:35,558][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 06:29:36,479][__main__][INFO] - Iteration 546 took 1m 4s (43.67% Gen, 54.91% Train). Generation: 28s, Training: 35s. Estimated remaining time: 45h 49m 27s. Estimated total time: 54h 9m 21s. Time estimates for 10 more iterations: 10m 49s, 100 more iterations: 1h 48m 18s, 500 more iterations: 9h 1m 33s. [2025-11-13 06:29:36,481][__main__][INFO] - Starting iteration 546. [2025-11-13 06:29:37,095][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 54 and human policies 1. [2025-11-13 06:29:37,095][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 06:29:54,304][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:29:54,442][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:29:54,443][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:29:55,237][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:29:55,497][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:29:55,774][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:29:56,270][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:29:56,406][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:29:56,495][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:29:56,582][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:29:56,584][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:29:56,587][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:29:57,188][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:29:57,192][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:29:57,428][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:29:57,430][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:29:57,432][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:29:57,519][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:29:57,523][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:29:57,611][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:29:57,614][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:29:58,165][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:29:58,168][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:29:58,518][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:29:58,520][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:29:59,244][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:29:59,307][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:29:59,664][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:30:00,471][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:30:00,869][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:30:00,871][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:30:01,511][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:30:01,573][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:30:01,636][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:30:02,695][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:30:03,303][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:30:03,911][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:30:03,914][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:30:03,938][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:30:03,985][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:30:06,022][__main__][INFO] - Number of regex retries in iteration 546: 40 [2025-11-13 06:30:06,023][__main__][INFO] - agents played in iteration 546 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 06:30:06,912][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:30:06,935][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:30:06,960][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:30:06,984][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:30:06,984][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 06:30:06,985][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 06:30:07,821][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 06:30:08,289][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 06:30:08,803][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 06:30:09,310][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 06:30:09,822][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 06:30:10,335][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 06:30:10,846][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 06:30:11,355][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 06:30:11,867][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 06:30:12,380][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 06:30:12,892][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 06:30:13,403][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 06:30:13,914][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 06:30:14,424][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 06:30:14,935][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 06:30:15,443][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 06:30:15,962][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 06:30:16,474][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 06:30:16,991][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 06:30:17,501][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 06:30:18,013][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 06:30:18,523][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 06:30:19,035][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 06:30:19,547][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 06:30:20,053][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 06:30:20,558][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 06:30:21,069][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 06:30:21,571][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 06:30:22,084][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 06:30:22,585][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 06:30:23,088][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 06:30:23,590][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 06:30:24,092][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 06:30:24,595][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 06:30:25,096][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 06:30:25,598][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 06:30:26,104][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 06:30:26,606][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 06:30:27,108][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 06:30:27,611][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 06:30:28,112][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 06:30:28,617][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 06:30:29,119][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 06:30:29,624][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 06:30:30,128][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 06:30:30,630][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 06:30:31,132][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 06:30:31,638][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 06:30:32,143][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 06:30:32,647][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 06:30:33,154][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 06:30:33,656][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 06:30:34,159][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 06:30:34,661][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 06:30:35,181][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 06:30:35,683][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 06:30:36,184][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 06:30:36,688][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 06:30:37,188][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 06:30:37,695][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 06:30:38,198][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 06:30:38,703][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 06:30:39,209][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 06:30:39,712][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 06:30:40,218][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10865 tokens. [2025-11-13 06:30:41,012][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.24%, Current % of VRAM taken: 59.69%, Block Peak % of device VRAM: 62.35%, ΔTime: 00:00:33 [2025-11-13 06:30:41,668][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 06:30:41,670][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 06:30:41,672][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 06:30:42,473][__main__][INFO] - Iteration 547 took 1m 5s (44.25% Gen, 54.53% Train). Generation: 28s, Training: 35s. Estimated remaining time: 46h 7m 55s. Estimated total time: 54h 28m 56s. Time estimates for 10 more iterations: 10m 53s, 100 more iterations: 1h 48m 57s, 500 more iterations: 9h 4m 49s. [2025-11-13 06:30:42,475][__main__][INFO] - Starting iteration 547. [2025-11-13 06:30:42,981][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 54 and human policies 1. [2025-11-13 06:30:42,982][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 06:31:01,534][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:31:01,537][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:31:01,586][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:31:01,628][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:31:01,632][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:31:01,687][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:31:01,689][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:31:01,690][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:31:01,771][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:31:02,465][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:31:02,695][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:31:03,362][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:31:03,364][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:31:03,366][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:31:03,390][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:31:03,430][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:31:03,687][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:31:04,406][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:31:04,583][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:31:04,757][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:31:05,095][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:31:05,267][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:31:05,359][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:31:05,450][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:31:05,626][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:31:05,717][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:31:05,719][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:31:06,263][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:31:06,494][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:31:06,678][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:31:06,766][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:31:07,281][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:31:07,359][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:31:07,704][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:31:08,508][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:31:08,564][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:31:08,990][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:31:08,994][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:31:09,719][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:31:09,972][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:31:10,284][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:31:10,287][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:31:10,624][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:31:11,412][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:31:11,586][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:31:11,786][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:31:11,875][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:31:11,890][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:31:12,888][__main__][INFO] - Number of regex retries in iteration 547: 48 [2025-11-13 06:31:12,889][__main__][INFO] - agents played in iteration 547 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 06:31:13,736][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:31:13,761][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:31:13,786][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:31:13,808][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:31:13,809][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 06:31:13,810][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 06:31:14,662][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 06:31:15,129][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 06:31:15,646][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 06:31:16,163][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 06:31:16,673][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 06:31:17,196][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 06:31:17,705][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 06:31:18,216][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 06:31:18,729][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 06:31:19,245][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 06:31:19,756][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 06:31:20,269][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 06:31:20,781][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 06:31:21,312][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 06:31:21,822][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 06:31:23,347][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 06:31:23,900][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 06:31:24,410][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 06:31:24,919][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 06:31:25,428][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 06:31:25,937][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 06:31:26,449][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 06:31:26,954][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 06:31:27,457][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 06:31:27,971][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 06:31:28,474][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 06:31:28,990][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 06:31:29,498][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 06:31:30,005][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 06:31:30,509][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 06:31:31,013][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 06:31:31,517][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 06:31:32,025][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 06:31:32,531][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 06:31:33,036][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 06:31:33,542][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 06:31:34,048][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 06:31:34,550][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 06:31:35,054][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 06:31:35,559][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 06:31:36,065][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 06:31:36,570][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 06:31:37,086][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 06:31:37,591][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 06:31:38,102][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 06:31:38,606][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 06:31:39,113][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 06:31:39,621][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 06:31:40,129][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 06:31:40,634][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 06:31:41,139][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 06:31:41,646][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 06:31:42,152][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 06:31:42,654][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 06:31:43,161][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 06:31:43,674][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 06:31:44,177][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 06:31:44,703][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 06:31:45,206][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 06:31:45,708][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 06:31:46,218][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 06:31:46,721][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 06:31:47,226][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 06:31:47,729][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 06:31:48,234][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10876 tokens. [2025-11-13 06:31:49,034][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.23%, Current % of VRAM taken: 59.68%, Block Peak % of device VRAM: 62.41%, ΔTime: 00:00:34 [2025-11-13 06:31:49,689][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 06:31:49,690][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 06:31:49,692][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 06:31:50,537][__main__][INFO] - Iteration 548 took 1m 7s (44.27% Gen, 54.48% Train). Generation: 29s, Training: 36s. Estimated remaining time: 47h 55m 43s. Estimated total time: 56h 17m 52s. Time estimates for 10 more iterations: 11m 15s, 100 more iterations: 1h 52m 35s, 500 more iterations: 9h 22m 58s. [2025-11-13 06:31:50,540][__main__][INFO] - Starting iteration 548. [2025-11-13 06:31:51,054][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 54 and human policies 1. [2025-11-13 06:31:51,054][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 06:32:07,317][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:32:07,322][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:32:07,370][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:32:07,373][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:32:07,376][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:32:07,378][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:32:07,380][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:32:07,383][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:32:07,465][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:32:07,511][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:32:07,513][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:32:07,514][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:32:07,517][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:32:08,126][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:32:08,341][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:32:08,432][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:32:08,975][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:32:08,998][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:32:09,071][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:32:09,207][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:32:09,297][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:32:09,300][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:32:09,382][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:32:09,957][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:32:09,960][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:32:10,009][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:32:10,011][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:32:10,095][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:32:10,180][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:32:10,183][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:32:10,349][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:32:10,352][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:32:10,354][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:32:10,434][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:32:10,436][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:32:10,921][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:32:10,946][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:32:11,191][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:32:11,356][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:32:11,358][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:32:11,364][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:32:11,368][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:32:11,439][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:32:11,893][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:32:11,896][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:32:11,943][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:32:12,025][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:32:12,192][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:32:12,196][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:32:12,265][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:32:12,268][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:32:12,326][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:32:12,329][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:32:12,331][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:32:12,831][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:32:12,836][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:32:12,871][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:32:12,910][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:32:12,957][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:32:13,153][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:32:13,238][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:32:13,242][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:32:13,300][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:32:13,307][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:32:13,371][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:32:13,373][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:32:13,375][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:32:13,735][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:32:13,853][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:32:13,858][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:32:13,885][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:32:14,029][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:32:14,031][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:32:14,134][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:32:14,226][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:32:14,389][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:32:14,391][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:32:14,846][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:32:14,852][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:32:14,855][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:32:15,032][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:32:15,109][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:32:15,207][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:32:15,475][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:32:15,587][mllm.models.large_language_model_local][WARNING] - Response Given the values, it is clear that both you and Bob have a higher value for books. However, you also have a higher value for hats compared to Bob. Therefore, it might be beneficial to propose a distribution that takes advantage of these values while also considering the proportional allocation in case of excess claims. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:32:16,220][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:32:16,386][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:32:16,392][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:32:16,648][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:32:16,703][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:32:16,753][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:32:17,032][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:32:17,878][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:32:17,969][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:32:18,181][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:32:18,456][mllm.models.large_language_model_local][WARNING] - Response Given the values, it seems both you and Bob have differing preferences for the items. To maximize your points, you should propose to take all items that you value more highly. Here, you value hats and books more, and Bob values hats and balls more. You should focus on taking as many books as possible since they have the highest value for you. Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:32:18,998][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:32:20,212][__main__][INFO] - Number of regex retries in iteration 548: 97 [2025-11-13 06:32:20,213][__main__][INFO] - agents played in iteration 548 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 06:32:21,091][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:32:21,115][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:32:21,139][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:32:21,163][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:32:21,163][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 06:32:21,165][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 06:32:21,989][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 06:32:22,451][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 06:32:22,969][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 06:32:23,477][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 06:32:23,989][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 06:32:24,495][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 06:32:25,003][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 06:32:25,513][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 06:32:26,023][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 06:32:26,536][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 06:32:27,043][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 06:32:27,553][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 06:32:28,063][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 06:32:28,573][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 06:32:29,083][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 06:32:29,605][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 06:32:30,114][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 06:32:30,630][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 06:32:31,138][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 06:32:31,648][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 06:32:32,157][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 06:32:32,665][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 06:32:38,770][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 06:32:39,273][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 06:32:39,776][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 06:32:40,279][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 06:32:40,782][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 06:32:41,287][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 06:32:41,788][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 06:32:42,290][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 06:32:42,804][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 06:32:43,305][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 06:32:43,817][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 06:32:44,316][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 06:32:44,819][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 06:32:45,335][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 06:32:45,838][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 06:32:46,339][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 06:32:46,842][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 06:32:47,344][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 06:32:47,849][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 06:32:48,351][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 06:32:48,854][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 06:32:49,358][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 06:32:49,862][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 06:32:50,367][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 06:32:50,870][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 06:32:51,371][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 06:32:51,872][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 06:32:52,371][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 06:32:52,871][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 06:32:53,373][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 06:32:53,878][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 06:32:54,383][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10875 tokens. [2025-11-13 06:32:55,176][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.14%, Current % of VRAM taken: 59.60%, Block Peak % of device VRAM: 62.11%, ΔTime: 00:00:33 [2025-11-13 06:32:55,964][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 06:32:55,965][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 06:32:55,967][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 06:32:56,878][__main__][INFO] - Iteration 549 took 1m 5s (44.30% Gen, 54.32% Train). Generation: 29s, Training: 35s. Estimated remaining time: 46h 28m 0s. Estimated total time: 54h 51m 15s. Time estimates for 10 more iterations: 10m 58s, 100 more iterations: 1h 49m 42s, 500 more iterations: 9h 8m 32s. [2025-11-13 06:32:56,880][__main__][INFO] - Starting iteration 549. [2025-11-13 06:32:57,356][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 54 and human policies 1. [2025-11-13 06:32:57,357][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 06:33:10,191][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:33:10,198][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:33:10,282][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:33:10,287][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:33:10,384][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:33:10,386][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:33:10,387][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:33:10,389][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:33:10,440][mllm.models.large_language_model_local][WARNING] - Response NewProposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:33:10,442][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:33:10,444][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:33:11,095][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:33:11,259][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:33:11,426][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:33:11,429][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:33:11,902][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:33:11,928][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:33:11,929][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:33:11,930][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:33:11,931][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:33:11,951][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:33:11,952][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:33:12,030][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:33:12,166][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:33:12,169][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:33:12,335][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:33:12,743][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:33:12,797][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:33:12,801][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:33:12,839][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:33:12,842][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:33:12,918][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:33:12,921][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:33:13,090][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:33:13,093][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:33:13,263][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:33:13,793][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:33:13,795][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:33:13,847][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:33:13,851][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:33:14,084][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:33:14,327][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:33:14,422][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:33:14,851][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:33:14,854][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:33:14,897][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:33:14,899][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:33:14,990][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:33:14,992][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:33:15,177][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:33:15,336][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:33:15,424][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:33:15,787][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:33:15,970][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:33:15,972][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:33:16,012][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:33:16,014][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:33:16,097][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:33:16,099][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:33:16,184][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:33:16,186][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:33:16,361][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:33:16,363][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:33:16,457][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:33:16,461][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:33:16,982][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:33:17,351][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:33:17,437][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:33:17,975][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:33:17,978][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:33:18,023][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:33:18,028][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:33:18,625][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:33:19,035][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:33:19,230][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:33:19,315][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:33:19,371][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:33:19,548][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:33:20,261][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:33:20,562][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:33:20,565][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:33:20,678][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:33:20,747][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:33:20,750][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:33:20,903][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:33:20,974][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:33:21,144][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:33:21,221][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:33:22,025][__main__][INFO] - Number of regex retries in iteration 549: 88 [2025-11-13 06:33:22,026][__main__][INFO] - agents played in iteration 549 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 06:33:22,806][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.53%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:33:22,828][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.53%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:33:22,851][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.53%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:33:22,875][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.53%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:33:22,875][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 06:33:22,876][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 06:33:23,711][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 06:33:24,170][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 06:33:24,681][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 06:33:25,189][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 06:33:25,695][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 06:33:26,196][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 06:33:26,706][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 06:33:27,232][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 06:33:27,745][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 06:33:28,254][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 06:33:28,766][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 06:33:29,276][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 06:33:29,789][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 06:33:30,298][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 06:33:30,810][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 06:33:31,349][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 06:33:32,941][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 06:33:33,450][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 06:33:33,965][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 06:33:34,475][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 06:33:34,984][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 06:33:35,495][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 06:33:36,006][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 06:33:36,527][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 06:33:37,037][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 06:33:37,563][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 06:33:38,075][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 06:33:38,586][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 06:33:39,093][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 06:33:39,604][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 06:33:40,125][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 06:33:40,633][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 06:33:41,143][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 06:33:41,649][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 06:33:42,157][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 06:33:42,668][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 06:33:43,177][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 06:33:43,686][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 06:33:44,197][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 06:33:44,704][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 06:33:45,213][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 06:33:45,724][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 06:33:46,230][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 06:33:46,757][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 06:33:47,260][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 06:33:47,766][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 06:33:48,267][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 06:33:48,771][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 06:33:49,273][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 06:33:49,774][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 06:33:50,275][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 06:33:50,781][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 06:33:51,282][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 06:33:51,783][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 06:33:52,284][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 06:33:52,788][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 06:33:53,295][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 06:33:53,800][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 06:33:54,306][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 06:33:54,817][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 06:33:55,322][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 06:33:55,848][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 06:33:56,352][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 06:33:56,859][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 06:33:57,366][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10873 tokens. [2025-11-13 06:33:58,209][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.25%, Current % of VRAM taken: 59.70%, Block Peak % of device VRAM: 62.30%, ΔTime: 00:00:34 [2025-11-13 06:33:58,857][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 06:33:58,859][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 06:33:58,860][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 06:33:59,672][__main__][INFO] - Iteration 550 took 1m 2s (39.59% Gen, 59.11% Train). Generation: 24s, Training: 36s. Estimated remaining time: 43h 31m 32s. Estimated total time: 51h 55m 50s. Time estimates for 10 more iterations: 10m 23s, 100 more iterations: 1h 43m 51s, 500 more iterations: 8h 39m 18s. [2025-11-13 06:33:59,674][__main__][INFO] - Starting iteration 550. [2025-11-13 06:34:00,172][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 54 and human policies 1. [2025-11-13 06:34:00,172][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 06:34:19,645][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:34:19,706][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:34:19,709][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:34:19,710][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:34:19,712][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:34:19,714][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:34:19,717][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:34:19,721][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:34:19,776][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:34:19,777][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:34:19,847][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:34:19,850][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:34:19,929][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:34:19,931][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:34:20,510][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:34:20,608][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:34:20,698][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:34:20,699][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:34:20,783][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:34:21,454][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:34:21,460][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:34:21,464][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:34:21,572][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:34:21,574][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:34:21,833][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:34:22,380][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:34:22,418][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:34:22,470][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:34:22,472][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:34:22,552][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:34:22,718][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:34:22,798][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:34:22,801][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:34:22,803][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:34:22,967][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:34:23,051][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:34:23,411][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:34:23,449][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:34:23,491][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:34:23,554][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:34:23,555][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:34:23,558][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:34:23,644][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:34:23,720][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:34:23,723][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:34:24,028][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:34:24,489][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:34:24,535][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:34:24,541][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:34:24,618][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:34:25,141][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:34:25,448][mllm.models.large_language_model_local][WARNING] - Response Given the new values, where you have a high value for hats and Bob has a high value for balls, but both of you have low values for books, it makes sense to propose a split that maximizes your value given your high per-item values for hats. Proposal: 10 hats, 10 balls, 0 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:34:25,623][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:34:25,626][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:34:25,691][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:34:25,694][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:34:25,765][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:34:25,767][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:34:25,769][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:34:25,960][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:34:26,055][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:34:26,152][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:34:26,672][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:34:26,677][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:34:26,720][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:34:26,723][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:34:26,776][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:34:26,778][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:34:26,831][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:34:27,800][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:34:27,841][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:34:27,844][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:34:28,115][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:34:29,081][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:34:29,206][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:34:29,258][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:34:29,319][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:34:29,411][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:34:29,415][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:34:29,483][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:34:29,489][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:34:29,711][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:34:30,396][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:34:30,451][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:34:30,878][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:34:30,940][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:34:32,084][__main__][INFO] - Number of regex retries in iteration 550: 86 [2025-11-13 06:34:32,085][__main__][INFO] - agents played in iteration 550 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 06:34:32,930][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:34:32,959][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:34:32,987][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:34:33,011][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:34:33,011][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 06:34:33,013][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 06:34:33,790][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 06:34:34,251][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 06:34:34,764][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 06:34:35,267][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 06:34:35,772][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 06:34:36,280][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 06:34:36,785][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 06:34:37,293][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 06:34:37,802][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 06:34:38,312][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 06:34:38,822][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 06:34:39,340][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 06:34:39,849][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 06:34:40,365][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 06:34:40,869][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 06:34:41,380][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 06:34:41,890][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 06:34:42,398][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 06:34:42,904][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 06:34:43,407][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 06:34:43,911][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 06:34:44,422][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 06:34:44,924][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 06:34:45,434][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 06:34:45,942][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 06:34:46,450][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 06:34:46,962][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 06:34:47,470][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 06:34:47,979][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 06:34:48,492][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 06:34:49,001][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 06:34:49,510][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 06:34:50,016][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 06:34:50,519][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 06:34:51,033][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 06:34:51,540][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 06:34:52,042][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 06:34:52,547][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 06:34:53,050][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 06:34:53,561][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 06:34:54,065][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 06:34:54,566][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 06:34:55,071][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 06:34:55,572][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 06:34:56,074][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 06:34:56,578][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 06:34:57,080][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 06:34:57,585][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 06:34:58,087][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 06:34:58,589][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 06:34:59,094][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 06:34:59,597][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 06:35:00,100][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 06:35:00,606][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 06:35:01,110][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 06:35:01,620][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 06:35:02,128][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 06:35:02,636][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 06:35:03,142][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 06:35:03,651][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 06:35:04,167][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 06:35:04,672][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 06:35:05,175][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 06:35:05,683][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 06:35:06,192][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10878 tokens. [2025-11-13 06:35:07,012][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.16%, Current % of VRAM taken: 59.62%, Block Peak % of device VRAM: 62.35%, ΔTime: 00:00:33 [2025-11-13 06:35:07,768][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 06:35:07,772][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 06:35:07,774][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 06:35:09,584][__main__][INFO] - Iteration 551 took 1m 9s (45.97% Gen, 51.42% Train). Generation: 31s, Training: 35s. Estimated remaining time: 49h 25m 10s. Estimated total time: 57h 50m 38s. Time estimates for 10 more iterations: 11m 34s, 100 more iterations: 1h 55m 41s, 500 more iterations: 9h 38m 26s. [2025-11-13 06:35:09,587][__main__][INFO] - Starting iteration 551. [2025-11-13 06:35:10,090][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 55 and human policies 1. [2025-11-13 06:35:10,090][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 06:35:25,792][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:25,800][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:25,864][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:25,868][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:25,942][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:25,944][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:25,946][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:25,948][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:25,950][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:25,952][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:25,955][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:25,957][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:25,958][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:25,960][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:26,598][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:35:26,662][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:26,784][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:26,787][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:26,871][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:35:26,875][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:35:26,878][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:26,881][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:26,964][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:27,003][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:27,004][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:27,006][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:27,469][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:35:27,757][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:27,760][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:27,763][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:27,765][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:27,770][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:27,776][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:27,784][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:27,787][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:27,790][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:30,117][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:30,160][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:30,249][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:30,333][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:30,337][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:30,420][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:30,422][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:30,510][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:30,606][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:30,608][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:30,610][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:30,612][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:30,989][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:31,017][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:31,051][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:31,054][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:31,148][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:31,150][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:31,260][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:31,358][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:31,361][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:31,446][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:31,634][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:31,636][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:32,136][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:32,139][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:32,288][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:32,387][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:32,389][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:32,391][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:32,394][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:32,559][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:32,563][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:32,565][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:32,566][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:32,652][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:32,758][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:32,760][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:33,770][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:33,774][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:33,841][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:33,936][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:33,938][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:34,057][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:34,447][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:34,450][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:34,501][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:34,566][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:34,570][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:34,931][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:35,017][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:35,164][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:35,166][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:35:36,084][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:36,140][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:36,144][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:36,233][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:36,235][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:36,236][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:36,334][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:36,655][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:36,774][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:37,142][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:37,225][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:35:37,341][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:37,647][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:37,714][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:37,741][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:37,791][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:37,960][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:37,992][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:38,831][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:38,902][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:39,182][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:39,270][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:39,350][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:39,375][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:39,398][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:39,444][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:39,465][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:39,576][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:39,765][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:39,872][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:35:40,756][__main__][INFO] - Number of regex retries in iteration 551: 119 [2025-11-13 06:35:40,757][__main__][INFO] - agents played in iteration 551 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 06:35:41,652][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.54%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:35:41,678][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.54%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:35:41,701][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.54%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:35:41,721][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.54%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:35:41,722][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 06:35:41,722][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 06:35:42,510][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 06:35:42,965][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 06:35:43,467][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 06:35:43,965][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 06:35:44,463][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 06:35:44,977][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 06:35:45,473][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 06:35:45,968][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 06:35:46,478][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 06:35:46,974][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 06:35:47,472][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 06:35:47,969][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 06:35:48,468][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 06:35:48,975][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 06:35:49,470][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 06:35:49,966][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 06:35:50,466][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 06:35:50,962][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 06:35:51,468][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 06:35:51,965][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 06:35:52,460][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 06:35:52,970][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 06:35:53,464][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 06:35:53,963][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 06:35:54,460][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 06:35:54,955][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 06:35:55,464][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 06:35:55,959][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 06:35:56,454][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 06:35:56,951][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 06:35:57,446][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 06:35:57,944][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 06:35:58,443][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 06:35:58,941][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 06:35:59,437][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 06:35:59,937][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 06:36:00,437][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 06:36:00,934][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 06:36:01,428][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 06:36:01,927][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 06:36:02,423][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 06:36:02,920][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 06:36:03,417][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 06:36:03,908][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 06:36:04,402][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 06:36:04,899][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 06:36:05,393][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 06:36:05,883][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 06:36:06,377][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 06:36:06,869][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 06:36:07,360][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 06:36:07,852][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 06:36:08,345][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 06:36:08,863][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 06:36:09,359][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 06:36:09,852][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 06:36:10,349][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 06:36:10,839][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 06:36:11,336][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 06:36:11,843][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 06:36:12,339][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 06:36:12,843][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 06:36:13,340][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 06:36:13,837][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 06:36:14,346][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10878 tokens. [2025-11-13 06:36:15,217][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.20%, Current % of VRAM taken: 59.65%, Block Peak % of device VRAM: 62.32%, ΔTime: 00:00:32 [2025-11-13 06:36:15,871][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 06:36:15,872][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 06:36:15,874][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 06:36:16,842][__main__][INFO] - Iteration 552 took 1m 6s (45.94% Gen, 52.61% Train). Generation: 30s, Training: 35s. Estimated remaining time: 47h 11m 4s. Estimated total time: 55h 37m 40s. Time estimates for 10 more iterations: 11m 7s, 100 more iterations: 1h 51m 15s, 500 more iterations: 9h 16m 16s. [2025-11-13 06:36:16,845][__main__][INFO] - Starting iteration 552. [2025-11-13 06:36:17,369][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 55 and human policies 1. [2025-11-13 06:36:17,369][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 06:36:33,422][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:33,427][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:33,471][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:33,473][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:33,476][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:33,510][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:33,512][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:33,621][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:33,622][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:33,625][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:33,701][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:34,410][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:36:34,580][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:34,583][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:34,668][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:34,670][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:34,672][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:34,674][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:34,676][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:34,677][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:34,756][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:34,841][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:34,843][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:35,295][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:35,351][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:35,413][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:35,415][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:35,498][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:35,500][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:35,581][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:35,582][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:35,584][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:36:35,586][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:36:35,666][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:35,756][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:35,759][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:35,761][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:35,763][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:36:35,764][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:36,224][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:36,274][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:36,317][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:36,464][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:36,465][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:36,553][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:36,556][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:36,558][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:36,561][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:36,777][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:36,779][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:36:36,863][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:37,229][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:37,450][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:37,452][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:36:37,455][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:37,457][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:37,538][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:36:37,540][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:37,542][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:37,628][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:37,630][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:37,632][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:37,634][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:37,709][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:37,797][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:36:38,002][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:38,373][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:38,403][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:38,432][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:38,466][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:38,572][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:38,573][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:36:38,577][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:38,579][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:38,686][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:38,688][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:36:38,691][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:38,692][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:41,077][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:41,246][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:41,249][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:41,253][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:41,256][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:41,259][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:41,261][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:36:41,268][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:41,271][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:41,311][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:41,314][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:41,395][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:42,920][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:42,924][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:42,988][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:42,991][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:42,994][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:43,767][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:43,819][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:44,178][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:44,389][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:36:44,391][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:44,484][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:44,816][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:45,074][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:45,620][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:45,623][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:45,665][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:36:45,672][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:36:46,362][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:46,655][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:46,761][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:46,764][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:46,767][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:47,477][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:47,721][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:36:47,764][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:47,879][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:47,982][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:48,215][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:48,271][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:48,355][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:48,443][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:48,445][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:48,824][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:48,912][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:48,953][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:48,957][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:48,960][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:48,965][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 1 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:49,087][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:49,993][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:50,314][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:50,556][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:36:50,589][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:50,847][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:36:51,564][__main__][INFO] - Number of regex retries in iteration 552: 134 [2025-11-13 06:36:51,564][__main__][INFO] - agents played in iteration 552 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 06:36:52,514][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:36:52,540][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:36:52,569][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:36:52,593][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:36:52,593][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 06:36:52,594][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 06:36:53,328][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 06:36:53,779][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 06:36:54,282][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 06:36:54,779][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 06:36:55,282][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 06:36:55,779][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 06:36:56,274][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 06:36:56,784][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 06:36:57,284][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 06:36:57,786][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 06:36:58,283][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 06:36:58,778][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 06:36:59,284][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 06:36:59,781][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 06:37:00,277][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 06:37:00,779][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 06:37:01,273][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 06:37:01,769][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 06:37:02,265][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 06:37:02,762][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 06:37:03,272][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 06:37:03,768][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 06:37:04,264][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 06:37:04,760][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 06:37:05,251][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 06:37:05,743][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 06:37:06,241][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 06:37:06,737][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 06:37:07,235][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 06:37:07,736][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 06:37:08,232][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 06:37:08,724][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 06:37:09,213][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 06:37:09,702][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 06:37:10,191][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 06:37:10,681][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 06:37:11,170][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 06:37:11,661][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 06:37:12,151][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 06:37:12,648][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 06:37:13,141][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 06:37:13,636][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 06:37:14,128][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 06:37:14,623][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 06:37:15,115][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 06:37:15,609][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 06:37:16,106][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 06:37:16,601][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 06:37:17,102][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 06:37:17,598][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 06:37:18,094][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 06:37:18,591][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 06:37:19,088][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 06:37:19,583][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 06:37:20,085][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 06:37:20,580][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 06:37:21,079][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 06:37:21,584][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 06:37:22,080][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 06:37:22,580][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 06:37:23,082][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 06:37:23,580][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 06:37:24,082][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 06:37:24,579][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 06:37:25,079][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10878 tokens. [2025-11-13 06:37:25,954][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.25%, Current % of VRAM taken: 59.70%, Block Peak % of device VRAM: 62.28%, ΔTime: 00:00:32 [2025-11-13 06:37:26,699][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 06:37:26,701][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 06:37:26,702][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 06:37:27,531][__main__][INFO] - Iteration 553 took 1m 10s (48.73% Gen, 50.08% Train). Generation: 34s, Training: 35s. Estimated remaining time: 50h 0m 24s. Estimated total time: 58h 28m 10s. Time estimates for 10 more iterations: 11m 41s, 100 more iterations: 1h 56m 56s, 500 more iterations: 9h 44m 41s. [2025-11-13 06:37:27,533][__main__][INFO] - Starting iteration 553. [2025-11-13 06:37:28,052][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 55 and human policies 1. [2025-11-13 06:37:28,053][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 06:37:48,815][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:48,818][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:48,821][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:48,825][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:48,828][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:48,880][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:48,881][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:48,883][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:48,885][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:48,887][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:48,927][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:48,929][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:48,980][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:48,983][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:48,987][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:49,073][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:49,076][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:49,647][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:37:49,650][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:37:49,653][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:37:49,694][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:49,696][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:49,699][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:49,747][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:49,749][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:49,827][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:49,911][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:37:49,994][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:49,995][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:49,997][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:50,080][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:50,082][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:50,084][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:50,086][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:50,506][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:50,600][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:50,602][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:50,605][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:50,607][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:50,635][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:50,637][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:50,639][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:50,641][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:37:50,643][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:50,675][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:50,767][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:50,770][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:50,855][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:50,857][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:50,859][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:50,862][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:50,944][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:37:50,947][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:50,949][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:51,025][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:51,027][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:37:51,029][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:51,031][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:51,033][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:51,035][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:51,387][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:37:51,493][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:51,497][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:51,499][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:37:51,525][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:37:51,527][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:37:51,530][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:37:51,533][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:51,579][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:51,581][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:51,583][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:51,585][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:51,653][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:51,752][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:51,754][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:51,756][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:37:51,789][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:37:51,844][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:51,847][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:37:51,926][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:37:51,929][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:52,575][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:37:52,603][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:37:54,773][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:54,827][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:54,856][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:54,859][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:54,861][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:54,923][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:54,925][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:54,927][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:55,020][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:55,208][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:55,211][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:55,374][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:55,377][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:55,465][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:55,467][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:55,781][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:55,871][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:37:55,874][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:55,877][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:55,880][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:55,947][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:56,011][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:56,014][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:56,016][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:56,080][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:37:56,083][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:56,270][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:56,272][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:37:56,357][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:56,360][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:56,456][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:56,458][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:56,461][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:37:56,539][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:56,541][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:56,940][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:37:56,948][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:37:57,062][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:57,222][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:57,226][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:37:57,269][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:57,303][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:37:57,307][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:57,499][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:37:57,611][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:58,015][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:58,075][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:58,078][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:58,094][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:37:58,101][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:58,105][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:58,217][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:58,367][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:58,369][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:58,671][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:58,672][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:37:58,678][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:58,768][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:58,770][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:58,771][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:59,238][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:37:59,240][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:37:59,263][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:37:59,290][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:59,327][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:59,331][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:59,399][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:59,488][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:59,490][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:59,583][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:37:59,658][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:59,847][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:37:59,851][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:38:00,425][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:38:00,500][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:38:00,554][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:38:00,684][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:38:00,886][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:38:00,965][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:38:01,055][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:38:01,059][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:38:01,063][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:38:01,066][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:38:01,147][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:38:01,297][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:38:01,632][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:38:01,687][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:38:01,691][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:38:01,718][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:38:01,754][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:38:01,758][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:38:02,038][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:38:02,099][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that the balls are the most valued items, but both you and Bob value them at 1. To maximize your points, you should focus on claiming as many balls as possible while also considering the hats and books, which are valued at 10 by you and 1 by Bob. Here's a proposal that aims to balance these values: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:38:02,102][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:38:02,179][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:38:02,227][mllm.models.large_language_model_local][WARNING] - Response Given the values, you should propose to take the items that you value more to maximize your points. Here, hats and books are valued more by you than by Bob, and balls are valued the same. A strategic proposal would be to take as many of the items you value highly as possible. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:38:02,232][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:38:02,238][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:38:02,502][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:38:02,598][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:38:02,671][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:38:02,675][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:38:03,338][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:38:03,342][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:38:03,345][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:38:03,348][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:38:03,351][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:38:03,372][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:38:03,375][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:38:03,422][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:38:03,427][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:38:03,431][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:38:03,700][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:38:03,720][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:38:03,754][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:38:03,778][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:38:03,837][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:38:03,856][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:38:03,946][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:38:04,142][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric nature of the situation and the values, where both you and Bob have a low preference for balls but a high preference for books, and a moderate preference for hats, a cooperative strategy is likely to yield the best outcome. By proposing to split the items, you encourage a fair distribution that maximizes the total value for both parties. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:38:04,257][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:38:04,261][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:38:04,343][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:38:04,347][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:38:04,361][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:38:04,509][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:38:04,549][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:38:04,591][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:38:04,663][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:38:04,741][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:38:04,837][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:38:04,866][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:38:04,909][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:38:04,947][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:38:04,970][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:38:04,982][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:38:04,985][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:38:05,194][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:38:05,385][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:38:06,232][__main__][INFO] - Number of regex retries in iteration 553: 222 [2025-11-13 06:38:06,233][__main__][INFO] - agents played in iteration 553 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 06:38:07,160][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:38:07,180][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:38:07,200][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:38:07,220][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:38:07,221][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 06:38:07,221][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 06:38:08,012][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 06:38:08,466][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 06:38:08,967][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 06:38:09,464][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 06:38:09,961][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 06:38:10,474][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 06:38:10,973][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 06:38:11,470][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 06:38:11,971][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 06:38:12,468][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 06:38:12,973][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 06:38:13,468][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 06:38:13,968][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 06:38:14,479][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 06:38:14,980][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 06:38:15,478][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 06:38:15,975][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 06:38:16,470][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 06:38:16,975][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 06:38:17,472][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 06:38:17,967][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 06:38:18,466][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 06:38:18,962][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 06:38:19,463][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 06:38:19,963][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 06:38:20,464][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 06:38:20,981][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 06:38:21,483][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 06:38:21,986][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 06:38:22,491][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 06:38:22,994][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 06:38:23,496][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 06:38:24,000][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 06:38:24,501][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 06:38:25,002][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 06:38:25,503][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 06:38:26,004][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 06:38:26,504][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 06:38:27,006][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 06:38:27,506][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 06:38:28,006][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 06:38:28,503][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 06:38:29,003][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 06:38:29,502][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 06:38:30,004][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 06:38:30,504][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 06:38:31,003][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 06:38:31,507][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 06:38:32,004][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 06:38:32,504][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 06:38:33,001][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 06:38:33,503][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 06:38:34,005][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 06:38:34,505][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 06:38:35,001][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 06:38:35,501][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 06:38:36,000][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 06:38:36,501][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 06:38:37,003][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 06:38:37,501][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 06:38:38,002][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 06:38:38,493][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 06:38:38,986][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 06:38:39,480][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 06:38:39,972][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10864 tokens. [2025-11-13 06:38:40,762][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.21%, Current % of VRAM taken: 59.66%, Block Peak % of device VRAM: 62.32%, ΔTime: 00:00:32 [2025-11-13 06:38:41,420][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 06:38:41,422][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 06:38:41,423][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 06:38:42,318][__main__][INFO] - Iteration 554 took 1m 14s (51.41% Gen, 47.38% Train). Generation: 38s, Training: 35s. Estimated remaining time: 53h 24m 19s. Estimated total time: 61h 53m 20s. Time estimates for 10 more iterations: 12m 22s, 100 more iterations: 2h 3m 46s, 500 more iterations: 10h 18m 53s. [2025-11-13 06:38:42,320][__main__][INFO] - Starting iteration 554. [2025-11-13 06:38:42,809][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 55 and human policies 1. [2025-11-13 06:38:42,809][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 06:38:58,525][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:38:58,529][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:38:58,570][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:38:58,572][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:38:58,575][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:38:58,577][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:38:58,580][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:38:58,583][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:38:58,639][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:38:58,641][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:38:58,642][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:38:58,643][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:38:58,663][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:38:58,665][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:38:58,718][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:38:58,719][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:38:58,720][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:38:58,721][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:38:58,722][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:38:58,723][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:38:58,725][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:38:58,802][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:38:58,803][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:38:59,356][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:38:59,359][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:38:59,383][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:38:59,387][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:38:59,390][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:38:59,418][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:38:59,420][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:38:59,423][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:38:59,474][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:38:59,476][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:38:59,478][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:38:59,522][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:38:59,524][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:38:59,607][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:38:59,608][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:38:59,696][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:38:59,697][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:38:59,783][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:38:59,785][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:00,283][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:39:00,337][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:39:00,342][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:00,440][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:39:00,609][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:00,611][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:00,678][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:39:00,683][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:00,685][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:00,687][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:00,690][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:00,693][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:00,761][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:00,764][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:00,766][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:39:00,768][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:00,845][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:00,848][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:00,850][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:00,852][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:01,245][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:01,291][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:01,294][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:01,356][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:01,429][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:01,498][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:01,501][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:01,503][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:01,555][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:01,557][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:01,610][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:39:01,612][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:39:01,615][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:01,695][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:39:01,697][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:39:01,698][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:01,700][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:01,701][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:01,703][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:01,704][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:01,705][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:01,707][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:01,709][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:01,870][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:02,136][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:02,163][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:02,166][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:02,194][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:02,197][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:02,199][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:02,202][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:02,298][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:39:02,300][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:02,302][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:02,303][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:02,393][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:39:02,396][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:02,399][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:02,469][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:39:02,472][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:02,474][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:02,477][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:02,479][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:02,534][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:39:02,541][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:02,613][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:02,615][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:39:02,619][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:39:02,622][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:39:02,624][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:39:02,625][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:39:02,627][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:39:02,783][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:03,359][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:03,361][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:03,364][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:03,367][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:03,370][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:03,427][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:03,431][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:03,485][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:39:03,680][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:03,682][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:39:03,738][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:03,741][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:03,948][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:03,952][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:04,003][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:04,005][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:39:04,006][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:04,008][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:04,010][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:04,454][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:04,502][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:04,504][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:04,588][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:39:04,669][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:04,672][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:04,944][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:05,122][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:05,124][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:05,126][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:05,129][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:05,131][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:05,134][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:05,212][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:39:05,215][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:05,382][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:05,663][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:39:05,665][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:39:05,826][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:05,828][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:05,913][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:06,027][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:06,030][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:06,120][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:06,123][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:06,297][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:39:06,299][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:39:06,301][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:39:06,303][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:39:06,305][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:39:06,385][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:39:06,474][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:06,564][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:06,930][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:06,961][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:07,033][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:07,036][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:07,039][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:07,297][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:07,301][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:07,304][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:07,445][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:07,448][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:07,536][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:07,721][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:07,816][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:39:07,822][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:39:07,829][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:07,987][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:07,989][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:39:08,087][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:08,354][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:39:08,611][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:08,615][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:08,679][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:08,755][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:39:08,787][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:08,893][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:08,986][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:08,989][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:09,286][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:09,289][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:09,293][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:09,297][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:09,490][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:09,547][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:09,637][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:09,726][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:09,815][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:09,909][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:39:09,913][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:10,004][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:39:10,086][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:10,151][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:10,154][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:10,158][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:10,182][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:10,333][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:10,335][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:10,380][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:39:10,382][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:39:10,385][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:10,388][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:10,461][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:10,550][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:10,720][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:10,857][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:10,911][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:10,914][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:10,958][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:10,964][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:11,143][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:39:11,216][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:39:11,219][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:39:11,270][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:11,273][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:11,415][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:11,440][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:39:11,448][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:39:11,452][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:12,268][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:12,403][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:12,408][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:12,609][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:12,731][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:39:12,753][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:12,774][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:12,890][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:39:12,897][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:12,900][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:12,903][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:13,088][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:39:13,227][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:13,273][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:13,417][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:13,502][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:13,506][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:13,546][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:13,715][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:39:13,837][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:39:13,841][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:13,918][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:13,934][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:39:13,958][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:14,048][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:14,074][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:14,113][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:39:14,119][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:39:14,445][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:14,507][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:14,658][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:39:15,438][__main__][INFO] - Number of regex retries in iteration 554: 265 [2025-11-13 06:39:15,439][__main__][INFO] - agents played in iteration 554 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 06:39:16,331][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:39:16,352][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:39:16,372][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:39:16,396][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:39:16,397][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 06:39:16,400][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 06:39:17,177][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 06:39:17,631][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 06:39:18,132][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 06:39:18,629][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 06:39:19,122][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 06:39:19,625][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 06:39:20,121][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 06:39:20,620][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 06:39:21,119][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 06:39:21,619][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 06:39:22,117][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 06:39:22,607][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 06:39:23,102][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 06:39:23,599][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 06:39:24,097][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 06:39:24,596][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 06:39:25,092][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 06:39:25,601][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 06:39:26,102][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 06:39:26,602][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 06:39:27,105][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 06:39:27,610][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 06:39:28,719][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 06:39:30,506][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 06:39:31,012][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 06:39:31,513][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 06:39:32,013][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 06:39:32,511][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 06:39:33,009][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 06:39:33,510][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 06:39:34,009][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 06:39:34,509][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 06:39:35,010][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 06:39:35,509][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 06:39:36,009][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 06:39:36,508][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 06:39:37,007][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 06:39:37,507][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 06:39:38,006][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 06:39:38,506][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 06:39:39,007][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 06:39:39,506][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 06:39:40,006][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 06:39:40,506][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 06:39:41,005][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 06:39:41,505][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 06:39:42,009][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 06:39:42,510][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 06:39:43,012][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 06:39:43,513][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 06:39:44,011][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 06:39:44,510][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 06:39:45,006][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 06:39:45,505][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 06:39:46,999][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 06:39:46,498][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 06:39:46,997][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 06:39:47,494][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 06:39:47,989][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 06:39:48,485][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 06:39:48,982][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 06:39:49,478][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 06:39:49,975][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 06:39:50,467][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 06:39:50,963][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10862 tokens. [2025-11-13 06:39:51,771][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.19%, Current % of VRAM taken: 59.64%, Block Peak % of device VRAM: 62.28%, ΔTime: 00:00:34 [2025-11-13 06:39:52,414][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 06:39:52,416][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 06:39:52,417][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 06:39:53,201][__main__][INFO] - Iteration 555 took 1m 10s (46.35% Gen, 52.53% Train). Generation: 32s, Training: 36s. Estimated remaining time: 50h 9m 27s. Estimated total time: 58h 39m 38s. Time estimates for 10 more iterations: 11m 43s, 100 more iterations: 1h 57m 19s, 500 more iterations: 9h 46m 36s. [2025-11-13 06:39:53,203][__main__][INFO] - Starting iteration 555. [2025-11-13 06:39:53,686][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 55 and human policies 1. [2025-11-13 06:39:53,687][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 06:40:11,754][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:11,777][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:11,779][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:11,782][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:11,786][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:11,790][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:11,792][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:11,795][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:11,798][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:11,800][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:11,803][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:11,806][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:11,809][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:11,855][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:11,858][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:11,859][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:11,861][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:11,862][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:11,864][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:11,866][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:11,869][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:11,921][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:11,922][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:11,923][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:11,925][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:11,926][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:11,928][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:11,930][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:11,931][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:11,933][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:11,980][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:11,981][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:11,983][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:12,484][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:40:12,524][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:40:12,527][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:40:12,529][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:40:12,531][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:40:12,533][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:40:12,535][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:40:12,572][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:12,575][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:40:12,577][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:40:12,579][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:40:12,618][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:12,620][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:12,622][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:12,706][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:12,708][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:40:12,710][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:40:12,787][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:12,789][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:12,790][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:12,792][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:12,794][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:12,870][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:13,350][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:40:13,385][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:40:13,440][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:40:13,444][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:40:13,448][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:40:13,452][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:13,456][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:13,458][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:40:13,460][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:13,518][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:40:13,522][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:13,524][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:13,552][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:13,554][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:13,555][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:13,557][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:13,558][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:40:13,560][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:13,562][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:13,615][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:13,617][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:13,619][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:14,196][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:14,223][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:14,257][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:14,260][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:14,263][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:14,320][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:14,324][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:14,326][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:14,352][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:14,356][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:40:14,361][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:40:14,406][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:14,455][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:14,457][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:14,459][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:14,460][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:14,541][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:14,544][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:14,545][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:14,630][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:40:14,632][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:40:14,635][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:40:14,638][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:40:14,640][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:14,643][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:14,646][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:14,649][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:14,652][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:40:14,654][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:40:14,657][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:14,709][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:14,711][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:14,713][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:14,714][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:14,716][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:14,718][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:15,100][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:40:15,164][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:15,246][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:40:15,282][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:15,325][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:15,329][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:15,332][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:15,334][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:15,337][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:15,376][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:15,379][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:15,382][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:15,440][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:15,442][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:15,444][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:15,503][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:40:15,587][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:40:15,593][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:40:15,595][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:40:15,601][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:40:15,604][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:15,655][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:15,657][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:15,659][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:15,660][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:40:15,662][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:40:15,664][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:40:15,666][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:40:15,742][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:15,744][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:16,044][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:16,047][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:16,156][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:16,214][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:16,218][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:16,244][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:40:16,274][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:40:16,278][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:40:16,398][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:16,400][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:16,664][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:16,729][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:16,732][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:40:16,736][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:40:16,740][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:40:16,745][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:40:17,128][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:17,132][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:17,135][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:17,171][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:40:17,195][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:17,225][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:17,267][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:17,270][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:40:17,275][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:17,278][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:17,280][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:17,282][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:17,284][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:17,320][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:17,323][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:17,326][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:17,329][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:17,348][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:17,351][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:17,353][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:40:17,356][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:17,358][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:17,360][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:17,364][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:17,389][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:17,391][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:17,393][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:17,439][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:17,441][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:17,442][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:17,445][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:17,535][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:17,619][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:40:17,622][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:40:17,626][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:17,628][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:17,717][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:17,719][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:17,722][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:18,068][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:18,130][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:18,231][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:40:18,234][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:18,236][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:18,239][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:18,269][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:18,272][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:18,275][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:18,297][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:40:18,301][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:40:18,303][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:40:18,306][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:40:18,308][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:18,311][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:18,346][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:40:18,427][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:18,429][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:40:18,431][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:40:18,433][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:40:18,519][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:40:18,523][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:18,527][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:40:18,738][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:18,741][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:18,743][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:18,854][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:18,856][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:18,910][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:40:18,912][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:40:18,914][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:19,086][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:19,090][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:19,131][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:19,135][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:19,185][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:19,188][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:19,252][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:40:19,260][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:40:19,276][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:40:19,279][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:40:19,361][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:19,435][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:19,439][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:40:19,445][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:40:19,452][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:40:19,488][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:40:19,492][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:40:19,494][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:19,496][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:19,497][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:19,579][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:19,666][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:19,670][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:19,759][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:19,843][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:19,931][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:19,934][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:19,938][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:19,942][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:20,008][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:40:20,015][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:40:20,018][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:20,021][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:20,128][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:20,131][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:20,265][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:40:20,348][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:20,373][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:20,409][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:20,411][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:20,581][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:20,586][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:40:20,589][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:20,593][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:20,659][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:20,661][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:20,768][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:40:20,771][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:20,774][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:20,868][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:20,957][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:40:20,961][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:40:20,965][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:40:20,968][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:20,972][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:21,046][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:40:21,259][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:40:21,261][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:40:21,303][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:21,331][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:21,334][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:21,336][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:21,460][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:21,504][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:40:21,508][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:21,511][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:21,556][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:21,709][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:21,712][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:21,715][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:21,760][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:40:21,763][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:40:21,767][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:21,921][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:21,924][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:21,926][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:21,930][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:40:22,020][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:22,111][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:22,175][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:22,178][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:22,428][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:22,480][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:22,577][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:22,621][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:22,625][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:22,743][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:40:22,864][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:22,934][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:22,937][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:40:23,003][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:23,005][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:23,064][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:23,067][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:23,070][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:23,134][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:23,407][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:23,529][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:23,534][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:23,537][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:23,573][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:40:23,614][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:23,617][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:23,640][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:23,643][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:23,666][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:23,670][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:23,695][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:40:23,698][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:23,810][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:23,837][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:23,841][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:23,949][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:23,953][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:24,001][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:40:24,004][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:40:24,006][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:24,232][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:24,496][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:40:24,537][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:40:24,540][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:40:24,579][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:40:24,604][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the equal but opposite values for hats and books between you and Bob, a competitive and cooperative approach would be to maximize the items with the highest value differences. Here, hats and books have equal value for you and Bob respectively, but balls have a significant value for you. A strategic proposal would be to take all the balls, all the hats, and split the books equally. In this case: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:24,608][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:40:24,630][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:24,691][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:24,748][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:24,751][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:40:24,753][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:40:24,757][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:24,760][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:24,802][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:24,806][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:24,889][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:24,893][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:25,800][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:25,857][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:25,903][mllm.models.large_language_model_local][WARNING] - Response Given the high value of hats and books for you and the high value of balls for Alice, it's important to ensure that you secure a significant portion of the hats and books while Alice grabs the balls. Here's a strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:25,977][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:26,135][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:40:26,364][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:26,367][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:26,371][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:26,391][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:26,395][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:26,470][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:40:26,474][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:26,564][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:26,568][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:40:26,602][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:40:26,606][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:26,610][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:26,641][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:26,644][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:26,742][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:40:26,748][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:26,821][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:26,824][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:26,940][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:40:26,979][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:27,032][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:40:27,160][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:27,195][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:40:27,324][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:27,353][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:27,437][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:27,453][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:27,495][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:27,527][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:27,543][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:27,684][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:40:27,699][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:27,703][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:27,742][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:27,825][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:40:27,860][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:40:27,891][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:27,904][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:27,916][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:27,919][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:40:27,953][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:40:28,009][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:40:28,086][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:40:28,144][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:40:28,156][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:40:28,168][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:40:29,418][__main__][INFO] - Number of regex retries in iteration 555: 416 [2025-11-13 06:40:29,419][__main__][INFO] - agents played in iteration 555 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 06:40:30,331][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:40:30,356][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:40:30,380][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:40:30,401][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:40:30,402][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 06:40:30,402][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 06:40:31,236][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 06:40:31,691][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 06:40:32,206][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 06:40:32,707][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 06:40:33,211][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 06:40:33,709][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 06:40:34,210][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 06:40:34,711][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 06:40:35,212][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 06:40:35,712][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 06:40:36,213][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 06:40:36,717][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 06:40:37,216][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 06:40:37,717][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 06:40:38,214][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 06:40:38,713][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 06:40:39,215][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 06:40:39,716][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 06:40:40,216][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 06:40:40,717][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 06:40:41,221][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 06:40:41,726][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 06:40:42,228][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 06:40:42,730][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 06:40:43,233][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 06:40:43,730][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 06:40:44,225][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 06:40:44,722][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 06:40:45,220][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 06:40:45,718][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 06:40:46,213][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 06:40:46,712][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 06:40:47,211][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 06:40:47,713][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 06:40:48,215][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 06:40:48,712][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 06:40:49,210][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 06:40:49,705][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 06:40:50,201][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 06:40:50,704][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 06:40:51,197][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 06:40:51,684][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 06:40:52,180][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 06:40:52,669][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 06:40:53,161][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 06:40:53,650][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 06:40:54,141][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 06:40:54,631][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 06:40:55,122][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 06:40:55,611][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 06:40:56,100][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 06:40:56,589][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 06:40:57,091][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 06:40:57,581][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 06:40:58,069][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 06:40:58,561][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 06:40:59,047][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 06:40:59,566][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 06:41:00,058][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 06:41:00,557][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 06:41:01,051][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 06:41:01,545][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 06:41:02,040][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 06:41:02,537][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 06:41:03,030][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10862 tokens. [2025-11-13 06:41:03,829][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.32%, Current % of VRAM taken: 59.77%, Block Peak % of device VRAM: 62.49%, ΔTime: 00:00:32 [2025-11-13 06:41:04,557][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 06:41:04,558][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 06:41:04,560][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 06:41:05,429][__main__][INFO] - Iteration 556 took 1m 11s (49.80% Gen, 48.98% Train). Generation: 35s, Training: 35s. Estimated remaining time: 51h 15m 46s. Estimated total time: 59h 47m 10s. Time estimates for 10 more iterations: 11m 57s, 100 more iterations: 1h 59m 34s, 500 more iterations: 9h 57m 51s. [2025-11-13 06:41:05,431][__main__][INFO] - Starting iteration 556. [2025-11-13 06:41:05,912][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 55 and human policies 1. [2025-11-13 06:41:05,912][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 06:41:17,907][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:17,912][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:17,914][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:17,976][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:17,978][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:17,980][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:17,982][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:17,984][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:17,985][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:17,987][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:17,989][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:17,990][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:17,992][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:17,993][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:17,994][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:17,996][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:17,998][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:17,999][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:18,001][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:18,002][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:18,005][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:18,006][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:18,008][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:18,010][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:18,011][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:18,058][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:18,060][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:18,061][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:18,062][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:18,064][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:18,085][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:18,087][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:18,088][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:18,534][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:18,560][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:18,563][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:18,566][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:18,568][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:18,583][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:18,585][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:18,589][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:18,592][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:18,594][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:18,597][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:18,599][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:18,601][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:18,604][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:18,635][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:18,637][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:18,639][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:18,641][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:18,644][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:18,646][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:18,647][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:18,649][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:18,710][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:18,712][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:18,714][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:18,716][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:18,718][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:18,720][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:18,722][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:18,723][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:18,774][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:18,775][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:18,776][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:18,776][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:19,242][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:19,293][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:19,295][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:19,297][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:19,382][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:19,383][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:19,386][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:19,387][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:19,389][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:19,390][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:19,392][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:19,394][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:19,396][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:41:19,400][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:19,402][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:41:19,405][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:19,407][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:19,408][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:19,453][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:19,454][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:19,455][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:19,457][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:19,458][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:19,460][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:19,475][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:19,476][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:19,478][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:19,479][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:19,837][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:19,862][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:19,865][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:19,899][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:19,902][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:19,905][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:19,908][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:19,910][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:19,944][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:19,948][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:19,999][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:20,034][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:20,087][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:20,090][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:20,092][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:20,096][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:20,098][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:20,116][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:20,119][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:20,122][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:20,125][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:20,128][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:20,131][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:20,134][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:20,193][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:20,194][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:20,196][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:20,197][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:20,199][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:20,201][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:20,202][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:20,204][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:20,206][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:20,208][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:20,253][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:20,255][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:20,256][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:20,257][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:41:20,260][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:20,542][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:20,545][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:20,564][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:20,567][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:20,569][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:20,572][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:20,620][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:20,640][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:20,762][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:20,764][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:41:20,769][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:41:20,773][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:41:20,805][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:20,806][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:20,808][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:20,830][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:20,832][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:20,834][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:20,836][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:20,838][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:41:20,843][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:20,889][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:20,891][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:20,892][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:20,893][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:20,894][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:20,895][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:20,897][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:20,898][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:20,900][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:41:20,942][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:20,943][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:21,275][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:21,309][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:21,350][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:21,377][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:21,381][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:21,383][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:21,386][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:21,479][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:21,504][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:21,507][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:21,541][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:21,544][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:21,546][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:21,548][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:21,551][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:21,553][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:21,586][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:21,587][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:21,589][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:21,591][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:21,593][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:41:21,597][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:41:21,601][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:21,622][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:21,624][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:21,626][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:21,628][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:21,630][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:21,631][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:21,633][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:21,635][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:21,637][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:21,638][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:21,671][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:21,673][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:21,953][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:21,956][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:21,958][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:21,978][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:41:21,984][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:21,988][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:21,990][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:22,000][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:22,026][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:22,029][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:22,051][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:22,054][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:41:22,061][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:22,064][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:22,104][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:22,125][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:22,162][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:22,165][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:22,168][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:22,187][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:22,190][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:22,192][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:22,195][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:41:22,202][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:22,205][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:22,208][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:22,232][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:22,234][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:22,236][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:22,239][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:22,241][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:22,243][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:22,245][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:22,247][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:22,249][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:41:22,300][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:41:22,303][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:41:22,306][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:22,307][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:22,309][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:22,311][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:22,313][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:22,387][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:22,389][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:22,477][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:22,479][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:22,660][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:22,663][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:22,666][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:22,684][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:22,687][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:22,706][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:22,709][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:41:22,731][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:41:22,737][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:22,768][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:22,771][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:22,805][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:22,808][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:22,810][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:22,813][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:22,815][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:22,888][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:22,891][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:22,932][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:41:22,972][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:22,975][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:22,978][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:41:22,984][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:23,024][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:23,027][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:23,030][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:41:23,037][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:41:23,043][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:23,046][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:23,049][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:23,113][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:23,115][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:23,116][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:23,118][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:23,120][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:23,122][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:23,123][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:23,126][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:23,127][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:23,129][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:23,131][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:23,133][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:23,135][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:23,137][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:23,139][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:23,141][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:23,185][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:23,187][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:23,188][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:23,519][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:41:23,526][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:41:23,533][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:41:23,545][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:41:23,551][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:41:23,558][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:23,612][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:23,615][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:23,855][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:41:23,910][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:23,944][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:41:23,950][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:41:23,957][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:41:23,984][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:23,987][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:23,990][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:23,993][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:24,041][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:24,044][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:24,046][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:24,050][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:24,053][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:24,055][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:24,058][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:24,061][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:24,064][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:24,068][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:24,071][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:24,074][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:41:24,080][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:41:24,143][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:24,145][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:24,146][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:24,148][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:24,149][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:24,151][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:24,153][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:24,156][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:24,424][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:24,474][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:24,541][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:24,544][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:24,547][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:24,579][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:24,582][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:24,586][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:24,661][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:24,748][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:24,816][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:24,818][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:24,877][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:24,879][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:24,902][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:24,903][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:24,905][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:24,927][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:24,928][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:24,930][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:24,954][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:24,955][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:24,956][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:41:24,959][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:41:25,018][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:25,021][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:25,023][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:25,026][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:25,028][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:25,058][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:25,060][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:25,061][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:25,062][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:25,064][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:25,065][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:41:25,069][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:25,071][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:25,072][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:25,074][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:25,113][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:25,115][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:25,193][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:25,194][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:25,599][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:25,604][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:25,608][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:25,611][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:25,656][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:25,659][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:25,707][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:25,754][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:25,757][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:25,761][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:25,764][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:25,778][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:41:25,804][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:41:25,809][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:41:25,830][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:41:25,834][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:41:25,839][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:25,841][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:25,842][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:25,856][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:25,858][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:25,859][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:25,887][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:25,889][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:25,892][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:26,071][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:26,074][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:26,076][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:26,079][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:26,139][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:26,141][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:26,143][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:26,144][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:26,245][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:26,410][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:26,441][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:26,445][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:41:26,453][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:41:26,460][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:26,601][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:26,647][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:26,688][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:41:26,695][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:41:26,732][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:26,735][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:26,818][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the fact that proposing to take all items is equally optimal (and fair) given the values, I will submit a proposal that maximizes my points by taking all the items. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:26,858][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:26,908][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:26,912][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:26,915][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:26,918][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:26,921][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:26,931][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:41:26,933][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:26,935][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:26,937][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:26,938][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:26,990][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:27,077][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:27,080][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:27,083][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:27,164][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:27,252][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:27,256][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:27,259][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:27,263][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:27,267][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:27,271][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:27,345][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:27,348][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:27,352][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:27,354][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:27,357][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:27,360][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:27,363][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:27,364][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:27,596][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:27,599][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:27,603][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:27,606][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:27,609][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:27,612][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:27,614][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:27,642][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:27,895][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:27,900][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:27,902][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:27,917][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:27,985][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:27,988][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:27,992][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:28,016][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:28,019][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:41:28,223][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:28,226][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:28,229][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:28,233][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:28,261][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:28,265][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:28,268][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:28,316][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:41:28,319][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:28,472][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:28,561][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:28,564][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:28,601][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:28,605][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:28,609][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:28,612][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:28,818][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:28,822][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:28,907][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:28,912][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:28,987][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:29,016][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:29,019][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:29,022][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:29,075][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:29,078][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:29,124][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:29,175][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:29,179][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:41:29,219][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:29,222][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:29,289][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:29,292][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:29,296][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:29,299][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:29,302][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:29,307][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:29,347][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:29,350][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:29,494][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:29,548][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:29,552][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:29,554][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:29,595][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:29,599][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:29,727][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:29,752][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:29,756][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:29,828][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:41:29,836][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:41:29,854][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:29,856][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:29,859][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:29,872][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:29,876][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:29,998][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:30,043][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:30,046][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:30,050][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:30,638][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:30,646][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:30,925][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:31,049][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:31,050][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:31,077][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:31,078][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:31,538][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:31,540][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:41:31,570][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:31,570][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:31,571][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:31,617][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:32,703][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:32,718][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:32,720][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:32,794][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:32,800][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:32,806][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:32,807][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:32,870][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:32,873][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:32,917][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:32,919][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:33,009][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:33,098][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:33,099][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:33,100][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:33,184][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:41:33,272][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:33,274][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:33,276][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:33,448][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:33,449][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:33,486][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's beneficial to propose a split that acknowledges the high value both you and Alice place on hats and books, while also considering the lower value for balls. However, since both of you value hats and books highly, a more strategic proposal could be to split these items in a way that maximizes the points for both parties. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:33,536][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:33,537][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:33,538][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:33,593][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:33,636][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:33,672][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:33,694][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:41:33,701][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:33,859][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:33,860][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:33,862][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:33,863][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:33,865][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:33,897][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:33,995][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:34,012][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:41:34,017][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:41:34,023][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:34,026][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:34,042][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:34,064][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:34,068][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:34,070][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:34,073][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:34,113][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:41:34,152][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:41:34,221][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:34,225][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:34,316][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:34,320][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:34,323][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:34,326][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:34,378][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:34,382][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:34,387][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:34,417][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:41:34,512][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:41:34,545][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:41:34,550][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:34,618][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:34,652][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:34,670][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:34,674][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:34,677][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:34,678][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:34,775][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:34,779][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:34,782][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:34,822][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:41:34,852][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:34,975][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:34,991][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:41:34,999][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:35,013][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:35,047][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:35,101][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:35,105][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:35,109][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:35,113][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:35,141][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:41:35,232][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:35,236][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:35,248][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:35,325][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:35,360][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:35,412][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:35,416][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:35,443][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:35,446][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:35,450][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:35,571][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:35,617][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:35,714][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:35,735][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:35,738][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:41:35,756][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:35,758][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:35,847][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:41:35,955][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:35,959][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:35,983][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:35,996][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:36,010][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:36,021][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:36,123][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:36,186][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:36,223][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:41:36,299][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:36,323][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:36,388][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:41:36,400][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:36,507][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:36,724][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:36,794][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:41:36,972][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:41:37,746][__main__][INFO] - Number of regex retries in iteration 556: 659 [2025-11-13 06:41:37,747][__main__][INFO] - agents played in iteration 556 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 06:41:38,754][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.62%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:41:38,774][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.62%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:41:38,793][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.62%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:41:38,820][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.62%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:41:38,822][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 06:41:38,823][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 06:41:39,662][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 06:41:40,120][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 06:41:40,633][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 06:41:41,135][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 06:41:41,640][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 06:41:42,145][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 06:41:42,648][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 06:41:43,150][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 06:41:43,654][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 06:41:44,157][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 06:41:44,659][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 06:41:45,159][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 06:41:45,657][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 06:41:46,156][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 06:41:46,653][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 06:41:47,154][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 06:41:47,651][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 06:41:48,152][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 06:41:48,650][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 06:41:49,148][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 06:41:49,648][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 06:41:50,147][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 06:41:50,648][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 06:41:51,147][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 06:41:51,647][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 06:41:52,151][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 06:41:52,650][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 06:41:53,153][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 06:41:53,653][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 06:41:54,151][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 06:41:54,649][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 06:41:55,151][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 06:41:55,648][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 06:41:56,152][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 06:41:56,654][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 06:41:57,152][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 06:41:57,650][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 06:41:58,147][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 06:41:58,646][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 06:41:59,141][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 06:41:59,639][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 06:42:00,133][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 06:42:00,623][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 06:42:01,112][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 06:42:01,603][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 06:42:02,093][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 06:42:02,603][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 06:42:03,092][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 06:42:03,583][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 06:42:04,078][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 06:42:04,569][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 06:42:05,060][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 06:42:05,550][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 06:42:06,041][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 06:42:06,534][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 06:42:07,026][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 06:42:07,518][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 06:42:08,011][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 06:42:08,501][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 06:42:08,993][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 06:42:09,489][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 06:42:09,980][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 06:42:10,472][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 06:42:10,964][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 06:42:11,457][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10877 tokens. [2025-11-13 06:42:12,276][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.29%, Current % of VRAM taken: 59.74%, Block Peak % of device VRAM: 62.49%, ΔTime: 00:00:32 [2025-11-13 06:42:12,916][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 06:42:12,918][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 06:42:12,919][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 06:42:13,713][__main__][INFO] - Iteration 557 took 1m 7s (46.95% Gen, 51.87% Train). Generation: 31s, Training: 35s. Estimated remaining time: 47h 57m 33s. Estimated total time: 56h 30m 5s. Time estimates for 10 more iterations: 11m 18s, 100 more iterations: 1h 53m 0s, 500 more iterations: 9h 25m 0s. [2025-11-13 06:42:13,715][__main__][INFO] - Starting iteration 557. [2025-11-13 06:42:14,218][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 55 and human policies 1. [2025-11-13 06:42:14,218][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 06:42:34,476][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:34,500][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:34,503][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:34,506][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:34,509][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:34,512][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:34,515][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:34,517][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:34,520][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:34,543][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:34,545][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:34,548][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:34,550][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:34,552][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:34,556][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:34,558][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:34,560][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:34,563][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:34,576][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:34,579][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:34,581][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:34,583][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:34,586][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:34,588][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:34,590][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:34,614][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:34,616][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:34,618][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:34,620][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:34,622][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:34,625][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:34,627][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:34,628][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:34,630][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:34,633][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:34,635][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:34,686][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:34,687][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:34,689][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:34,690][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:34,691][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:35,215][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:35,260][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:35,262][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:35,264][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:35,266][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:35,298][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:35,300][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:35,301][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:35,323][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:35,324][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:35,325][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:35,326][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:35,327][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:35,329][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:35,330][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:35,372][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:35,373][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:35,374][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:35,376][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:35,377][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:35,377][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:35,378][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:35,380][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:35,459][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:35,460][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:35,461][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:35,462][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:35,464][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:35,467][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:35,545][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:35,547][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:35,550][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:35,971][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:35,996][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:36,000][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:36,034][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:36,042][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:36,045][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:36,047][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:36,049][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:36,081][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:36,086][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:36,090][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:36,092][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:36,093][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:36,095][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:36,131][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:36,134][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:36,136][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:36,139][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:36,141][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:36,144][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:36,146][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:36,148][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:36,188][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:36,190][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:36,193][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:36,195][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:36,198][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:36,200][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:36,202][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:36,204][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:36,206][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:36,208][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:36,250][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:36,251][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:36,253][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:36,255][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:36,256][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:36,325][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:36,327][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:36,330][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:36,333][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:36,336][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:36,338][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:36,341][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:36,406][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:36,409][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:36,411][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:36,737][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:36,740][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:36,789][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:36,825][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:36,885][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:36,887][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:36,889][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:36,891][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:36,896][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:36,899][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:36,901][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:36,903][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:36,905][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:36,907][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:36,908][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:36,938][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:36,941][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:36,943][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:36,946][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:36,948][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:36,952][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:36,957][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:36,959][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:36,961][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:36,962][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:36,964][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:37,135][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:37,137][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 balls, 10 hats, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:37,139][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:37,142][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 books, 10 balls, 10 hats did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:37,144][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:37,148][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:37,580][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:37,646][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:37,650][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:37,654][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:37,657][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:37,661][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:37,664][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:37,694][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:37,697][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:37,700][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:37,702][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:37,705][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:37,707][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:37,736][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:37,741][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:37,746][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:37,752][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:37,754][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:37,756][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:37,829][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:37,831][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:37,836][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:37,839][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:37,841][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:37,842][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:37,844][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:37,846][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:37,847][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:37,849][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:37,912][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:37,915][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:37,917][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:37,997][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:38,173][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:38,176][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:38,178][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:38,181][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:38,183][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:38,258][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:38,261][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:38,507][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:38,604][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:38,607][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:38,609][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:38,615][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:38,618][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:38,620][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:38,623][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:38,626][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:38,629][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:38,679][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:38,680][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:38,682][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:38,684][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:38,741][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:38,744][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:38,748][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:38,753][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:38,758][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:38,761][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:38,763][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:38,764][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:38,767][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:38,769][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:38,824][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:38,826][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:38,827][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:38,911][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:38,915][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:38,996][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:39,000][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:39,003][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:39,006][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:39,010][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:39,013][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:39,017][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:39,079][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:39,539][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:39,583][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:39,587][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:39,590][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:39,607][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:39,610][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:39,617][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:39,623][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:39,629][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:39,634][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:39,636][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:39,665][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:39,668][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:39,671][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:39,673][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:39,676][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:39,706][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:39,708][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:39,711][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:39,714][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:39,717][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:39,719][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:39,722][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:39,741][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:39,806][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:39,864][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:39,866][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:39,869][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:39,871][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:39,874][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:39,876][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:39,878][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:39,881][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:39,882][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:39,884][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:40,031][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:40,119][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:40,205][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:40,208][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:40,212][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:40,498][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:40,590][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:40,593][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:40,597][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:40,605][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:40,611][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:40,617][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:40,620][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:40,622][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:40,625][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:40,657][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:40,660][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:40,676][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:40,678][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:40,679][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:40,681][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:40,682][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:40,705][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:40,710][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:40,712][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:40,714][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:40,716][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:40,791][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:40,794][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:40,797][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:40,799][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:40,802][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:40,806][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:40,876][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:40,880][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:40,882][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:40,885][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:40,889][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:40,892][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:40,959][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:41,217][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:41,221][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:41,224][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:41,300][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:41,303][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:41,307][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:41,311][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:41,315][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:41,318][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:41,387][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:41,389][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:41,392][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 books, 10 balls, 10 hats did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:41,395][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:41,397][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:41,400][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:41,658][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:41,661][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:41,705][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:41,709][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:41,711][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:41,730][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:41,735][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:41,738][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:41,759][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:41,762][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:41,765][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:41,767][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:41,811][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:41,817][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:41,819][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:41,822][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:41,985][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:42,041][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:42,086][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:42,131][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:42,134][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:42,137][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:42,141][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:42,143][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:42,144][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:42,192][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:42,194][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:42,197][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:42,200][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:42,202][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:42,245][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 books, 10 balls, 10 hats did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:42,248][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:42,250][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:42,252][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:42,255][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:42,257][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 books, 10 balls, 10 hats did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:42,259][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:42,265][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:42,268][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:42,415][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:42,465][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:42,492][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:42,495][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:42,502][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:42,505][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:42,507][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:42,535][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:42,541][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:42,622][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:42,625][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:42,628][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:42,631][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:42,672][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:42,674][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:42,676][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:42,679][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:42,758][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:42,761][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:42,765][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:42,846][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:42,850][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:42,854][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:42,857][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:42,934][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:42,937][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:42,941][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:42,944][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:42,948][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:42,978][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:42,982][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:42,985][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:43,003][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:43,006][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:43,009][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:43,011][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:43,013][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:43,014][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:43,016][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:43,031][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:43,034][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:43,036][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:43,065][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:43,067][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:43,071][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:43,073][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:43,151][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:43,152][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:43,154][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:43,155][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:43,212][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 books, 10 balls, 10 hats did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:43,214][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:43,216][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:43,218][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:43,222][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:43,227][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 books, 10 balls, 10 hats did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:43,231][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:43,235][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:43,237][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:43,239][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:43,241][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:43,459][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:43,467][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:43,697][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:43,700][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:43,845][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:43,848][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:43,852][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:43,869][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:43,871][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:43,872][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:43,874][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:43,878][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:43,880][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:43,883][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:43,885][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:43,905][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:43,909][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:43,911][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:43,912][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:43,950][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:44,007][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:44,013][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:44,016][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:44,019][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:44,023][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:44,026][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:44,066][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:44,069][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:44,072][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:44,075][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:44,077][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:44,080][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:44,085][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:44,090][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:44,095][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:44,291][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:44,552][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:44,555][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:44,641][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:44,732][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:44,800][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:44,804][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:44,807][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:44,810][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:44,814][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:44,817][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:44,932][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:44,965][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:44,969][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:44,972][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:45,007][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:45,015][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:45,022][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:45,055][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:45,061][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:45,067][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:45,072][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:45,077][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:45,126][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:45,192][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:45,198][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:45,233][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:45,236][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:45,238][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:45,439][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:45,508][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:45,512][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:45,569][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:45,573][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:45,646][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:45,650][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:45,653][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:45,736][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:45,741][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:45,743][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:45,838][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:45,842][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:45,924][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:45,928][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:45,932][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:45,939][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:45,945][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:45,948][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:45,951][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:45,955][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:45,958][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:45,970][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:46,069][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:46,072][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:46,108][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:46,110][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:46,112][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:46,227][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:46,267][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:46,270][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:46,272][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:46,305][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:46,396][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:46,483][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:46,485][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:46,486][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:46,487][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:46,556][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:46,641][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:46,702][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:46,706][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:46,776][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:46,778][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:46,783][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:46,785][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:46,788][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:46,790][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:46,866][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:46,869][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:46,942][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:46,945][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:46,948][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:46,954][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:46,960][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:46,965][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:46,995][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:47,018][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:47,070][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:47,072][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:47,144][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:47,147][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:47,150][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:47,152][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:47,154][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:47,156][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:47,236][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:47,239][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:47,241][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:47,242][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:47,243][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:47,245][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:47,246][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:47,249][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:47,250][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:47,323][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:47,325][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:47,408][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:47,412][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:47,653][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:47,657][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:47,661][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:47,699][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:47,828][mllm.models.large_language_model_local][WARNING] - Response Given the high value of hats and books for both you and Alice, and the low value for balls, a strategic split might involve taking most of the items with higher individual values. Here's a revised proposal to maximize points: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:47,832][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:47,834][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:47,876][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:47,970][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:47,975][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:48,043][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:48,049][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:48,051][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:48,054][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:48,057][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:48,109][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:48,110][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:48,194][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:48,201][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:48,209][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:48,212][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:48,215][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:48,217][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:48,251][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:48,254][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:48,555][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:48,559][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:48,561][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:48,562][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:48,563][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:48,565][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:48,567][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:49,108][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:49,109][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:49,111][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:49,243][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:49,246][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:49,286][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:49,288][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:49,290][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:49,292][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:49,447][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:49,472][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:49,475][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:49,477][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:49,479][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:49,515][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:49,517][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:49,519][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:49,570][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:49,572][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:49,654][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:49,655][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:49,656][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:49,659][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:49,661][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:49,663][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:49,769][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:49,926][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:49,930][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:49,955][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:49,957][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:49,960][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:49,962][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:49,964][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:49,986][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:50,049][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:50,052][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:50,105][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:50,108][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:50,110][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:50,199][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:50,276][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:50,278][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:50,337][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:50,426][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:50,428][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:50,517][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:50,519][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:50,521][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:50,603][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:50,606][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:50,607][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:50,700][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:50,703][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:50,705][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:50,882][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:50,886][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:50,931][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:51,108][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:51,111][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:51,152][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:51,154][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:51,155][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:51,157][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:51,159][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:51,178][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:51,180][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:51,181][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:51,433][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:51,436][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:51,439][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:51,442][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:51,444][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:51,707][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:51,710][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:51,713][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:51,720][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:51,724][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:51,726][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:51,758][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:51,761][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:51,765][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:51,769][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:51,773][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:51,777][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:51,779][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:51,782][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:51,784][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:51,787][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:51,805][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:52,043][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:52,222][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:52,309][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:52,317][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:52,321][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:52,323][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:52,480][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:52,482][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:52,483][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:52,485][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:52,486][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:52,487][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:52,488][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:52,490][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:52,570][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:52,571][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:52,573][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:52,574][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:52,596][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:52,598][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:52,601][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:52,684][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:52,702][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:52,703][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:52,724][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:52,745][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:52,765][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:52,767][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:52,768][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:52,904][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:52,906][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:52,907][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:52,936][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:53,013][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:53,283][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:53,363][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:53,367][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:53,370][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:53,373][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:53,376][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:53,379][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:53,382][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:53,384][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:53,410][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:53,412][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:53,415][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:53,431][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:53,433][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:53,506][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:53,524][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:53,543][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:53,564][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:53,584][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:53,587][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:53,590][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:53,735][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:53,776][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:53,778][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:53,780][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:53,781][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:53,783][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:53,785][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:53,786][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:53,831][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:53,849][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:53,852][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:53,854][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:53,936][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:53,939][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:53,940][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:53,987][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:53,989][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:54,072][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:54,091][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:54,151][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:54,152][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:54,420][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:54,424][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:54,427][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:54,441][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:54,444][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:54,447][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:54,450][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:54,604][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:54,686][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:54,690][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:54,694][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:54,696][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:54,697][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:54,698][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:54,699][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:54,764][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:54,770][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:54,850][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:54,854][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:54,857][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:54,928][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:55,199][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:55,201][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:55,203][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:55,204][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:55,205][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:55,207][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:55,208][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:55,210][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:55,212][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:55,213][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:55,214][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:55,216][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:55,217][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:55,220][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:55,235][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:55,307][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:55,323][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:55,338][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:55,413][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:55,415][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:55,474][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:55,477][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:55,522][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:55,538][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:55,541][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:55,544][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:55,546][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:55,559][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:55,563][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:55,566][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:55,570][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:55,573][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:55,577][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:55,581][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:55,583][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:55,585][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:55,586][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:55,588][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:55,589][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:55,615][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:55,648][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:55,765][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:55,768][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:55,805][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:55,818][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:55,834][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:55,836][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:55,839][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:55,878][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:55,922][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:55,925][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:55,927][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:55,930][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:55,932][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:55,933][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:55,934][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:55,975][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:56,109][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:56,114][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:56,152][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:56,166][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:56,181][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:56,182][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:56,198][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:56,199][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:56,200][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:56,201][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:56,202][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:56,214][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:56,215][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:56,216][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:56,424][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:56,426][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:56,428][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:56,429][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:56,438][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:56,439][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:56,454][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:56,456][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:56,459][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:56,460][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:56,466][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:56,467][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:56,629][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:56,640][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:56,655][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:56,847][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:56,859][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:56,865][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:57,035][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:42:57,056][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:57,529][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:57,882][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:57,894][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:42:58,069][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:42:58,790][__main__][INFO] - Number of regex retries in iteration 557: 875 [2025-11-13 06:42:58,791][__main__][INFO] - agents played in iteration 557 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 06:42:59,778][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:42:59,801][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:42:59,824][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:42:59,845][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:42:59,846][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 06:42:59,847][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 06:43:00,671][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 06:43:01,614][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 06:43:03,063][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 06:43:03,559][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 06:43:04,052][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 06:43:04,547][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 06:43:05,044][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 06:43:05,536][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 06:43:06,028][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 06:43:06,520][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 06:43:07,021][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 06:43:07,514][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 06:43:08,005][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 06:43:08,499][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 06:43:08,991][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 06:43:09,486][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 06:43:09,975][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 06:43:10,468][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 06:43:10,960][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 06:43:11,451][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 06:43:11,940][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 06:43:12,436][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 06:43:12,927][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 06:43:13,419][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 06:43:13,913][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 06:43:14,406][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 06:43:14,918][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 06:43:15,413][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 06:43:15,907][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 06:43:16,401][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 06:43:16,893][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 06:43:17,382][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 06:43:17,872][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 06:43:18,362][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 06:43:18,854][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 06:43:19,342][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 06:43:19,831][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 06:43:20,324][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 06:43:20,812][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 06:43:21,303][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 06:43:21,798][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 06:43:22,286][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 06:43:22,774][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 06:43:23,265][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 06:43:23,757][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 06:43:24,246][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 06:43:24,734][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 06:43:25,223][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 06:43:25,711][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 06:43:26,199][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 06:43:26,688][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 06:43:27,178][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 06:43:27,666][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 06:43:28,158][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 06:43:28,647][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 06:43:29,137][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 06:43:29,624][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 06:43:30,112][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 06:43:30,601][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 06:43:31,098][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 06:43:31,588][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 06:43:32,078][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 06:43:32,568][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 06:43:33,058][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 06:43:33,548][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10844 tokens. [2025-11-13 06:43:34,330][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.25%, Current % of VRAM taken: 59.71%, Block Peak % of device VRAM: 62.29%, ΔTime: 00:00:33 [2025-11-13 06:43:35,013][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 06:43:35,014][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 06:43:35,016][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 06:43:36,093][__main__][INFO] - Iteration 558 took 1m 21s (54.44% Gen, 44.24% Train). Generation: 44s, Training: 36s. Estimated remaining time: 59h 39m 53s. Estimated total time: 68h 13m 47s. Time estimates for 10 more iterations: 13m 38s, 100 more iterations: 2h 16m 27s, 500 more iterations: 11h 22m 17s. [2025-11-13 06:43:36,095][__main__][INFO] - Starting iteration 558. [2025-11-13 06:43:36,580][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 55 and human policies 1. [2025-11-13 06:43:36,581][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 06:43:55,109][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:55,130][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:55,132][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:55,134][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:55,135][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:55,137][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:55,160][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:55,162][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:55,164][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:55,166][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:55,167][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:55,169][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:55,171][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:55,172][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:55,174][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:55,204][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:55,206][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:55,208][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:55,209][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:55,210][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:55,212][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:55,213][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:55,214][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:55,216][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:55,217][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:55,218][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:55,218][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:55,219][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:55,260][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:55,262][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:55,263][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:55,264][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:55,265][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:55,266][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:55,267][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:55,268][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:55,269][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:55,271][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:55,272][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:55,273][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:55,314][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:55,315][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:55,317][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:55,318][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:55,319][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:55,320][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:55,321][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:55,322][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:55,323][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:55,325][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:55,326][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:55,327][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:55,328][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:55,329][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:55,331][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:55,332][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:55,333][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:55,334][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:55,335][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:55,364][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:55,366][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:55,367][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:55,369][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:55,370][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:55,372][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:55,373][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:55,682][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:43:55,683][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:43:55,686][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:43:55,687][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:43:55,688][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:55,718][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:55,720][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:55,723][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:55,725][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:43:55,759][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:43:55,760][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:43:55,762][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:43:55,763][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:43:55,765][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:43:55,767][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:43:55,769][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:55,841][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:43:55,843][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:43:55,844][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:43:55,845][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:43:55,847][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:43:55,848][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:43:55,849][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:43:55,850][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:43:55,853][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:55,854][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:55,917][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:43:55,919][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:43:55,920][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:43:55,922][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:43:55,924][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:43:55,925][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:43:55,925][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:43:55,926][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:43:55,928][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:43:55,930][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:43:55,931][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:43:55,933][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:43:55,936][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:43:55,937][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:43:55,938][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:55,940][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:55,941][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:55,942][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:55,943][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:56,358][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:56,405][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:56,407][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:56,409][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:56,410][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:56,412][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:56,413][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:56,414][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:56,416][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:56,471][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:43:56,501][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:43:56,503][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:43:56,506][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:43:56,509][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:43:56,512][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:43:56,514][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:56,515][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:56,517][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:56,519][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:56,521][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:56,552][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:43:56,555][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:43:56,558][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:43:56,560][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:43:56,563][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:43:56,564][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:43:56,565][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:56,567][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:56,568][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:56,570][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:56,635][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:56,637][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:56,638][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:56,705][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:56,707][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:56,708][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:56,709][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:56,711][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:56,712][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:56,796][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:57,142][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:43:57,166][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:57,168][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:57,170][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:57,172][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:57,190][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:57,191][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:57,193][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:57,247][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:57,248][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:57,333][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:43:57,336][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:43:57,339][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:43:57,341][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:43:57,344][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:43:57,347][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:57,349][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:57,417][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:43:57,419][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:43:57,420][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:43:57,422][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:57,423][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:57,424][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:57,425][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:57,426][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:57,428][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:57,429][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:57,431][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:57,433][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:57,434][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:57,436][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:57,437][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:57,438][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:57,439][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:57,502][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:57,504][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:57,507][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:57,509][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:57,511][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:57,512][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:57,587][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:57,590][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:43:58,023][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:43:58,025][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:43:58,028][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:43:58,029][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:43:58,030][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:58,033][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:58,067][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:58,069][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:58,071][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:58,073][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:58,075][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:58,077][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:58,080][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:58,082][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:58,120][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:58,123][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:58,126][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:58,129][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:58,131][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:43:58,135][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:43:58,138][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:58,140][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:58,143][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:58,146][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:58,148][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:58,150][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:58,153][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:58,155][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:58,183][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:58,185][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:58,189][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:58,193][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:43:58,198][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:43:58,201][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:58,264][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:58,266][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:58,269][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:58,353][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:58,433][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:58,435][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:58,437][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:58,439][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:58,440][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:58,519][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:58,521][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:43:58,523][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:58,906][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:58,960][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:58,962][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:59,086][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:43:59,170][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:59,171][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:59,172][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:59,173][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:59,174][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:59,175][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:59,177][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:59,179][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:59,181][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:59,264][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:59,265][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:59,267][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:59,269][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:59,271][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:59,272][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:59,273][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:59,275][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:59,277][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:59,279][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:59,357][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:59,360][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:59,362][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:59,364][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:59,453][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:59,455][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:59,458][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:59,460][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:59,462][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:59,464][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:59,545][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:59,906][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:43:59,934][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:59,937][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:59,940][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:59,942][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:59,956][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:59,959][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:59,963][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:59,981][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:59,984][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:59,986][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:59,989][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:59,991][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:59,993][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:59,996][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:43:59,998][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:00,001][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:00,010][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:00,012][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:00,016][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:00,019][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:00,021][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:00,024][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:00,026][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:00,027][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:00,028][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:00,030][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:00,032][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:00,034][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:00,036][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:00,038][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:00,040][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:00,054][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:00,056][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:00,058][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:00,060][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:00,063][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:00,064][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:00,064][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:00,066][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:00,067][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:00,069][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:00,070][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:00,072][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:00,073][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:00,079][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:00,080][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:00,083][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:00,085][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:00,086][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:00,087][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:00,088][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:00,090][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:00,092][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:00,093][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:00,094][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:00,096][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:00,097][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:00,118][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:00,184][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:00,186][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:00,255][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:00,257][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:00,260][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:00,263][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:00,265][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:00,574][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:00,599][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:00,602][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:00,605][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:00,621][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:00,623][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:00,627][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:00,644][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:00,647][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:00,650][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:00,652][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:00,654][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:00,666][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:00,668][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:00,670][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:00,672][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:00,674][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:00,676][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:00,678][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:00,692][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:00,694][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:00,696][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:00,698][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:00,699][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:00,700][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:00,702][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:00,706][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:00,714][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:00,715][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:00,718][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:00,719][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:00,723][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:00,727][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:00,731][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:00,736][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:00,742][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:00,746][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:00,750][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:00,751][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:00,753][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:00,755][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:00,757][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:00,759][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:00,762][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:00,765][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:00,767][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:00,769][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:00,771][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:00,773][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:00,776][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:00,779][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:00,781][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:00,782][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:00,792][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:00,794][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:00,795][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:00,803][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:00,804][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:00,806][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:00,807][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:00,808][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:00,810][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:00,810][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:00,811][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:00,813][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:00,814][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:00,815][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:00,862][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:00,922][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:01,169][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:01,176][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:01,204][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:01,210][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:01,240][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:01,242][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:01,247][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:01,252][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:01,315][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:01,321][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:01,324][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:01,326][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:01,396][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:01,398][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:01,402][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:01,405][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:01,409][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:01,411][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:01,415][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:01,418][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:01,478][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:01,482][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:01,486][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:01,490][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:01,495][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:01,497][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:01,501][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:01,505][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:01,565][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:01,566][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:01,568][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:01,569][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:01,572][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:01,653][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:01,655][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:01,657][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:01,660][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:01,662][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:01,664][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:01,666][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:01,667][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:01,669][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:01,670][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:01,671][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:01,672][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:01,707][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:01,709][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:01,711][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:01,712][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:01,714][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:01,715][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:01,959][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:02,011][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:02,165][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:02,218][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:02,221][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:02,225][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:02,230][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:02,232][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:02,233][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:02,235][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:02,238][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:02,240][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:02,241][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:02,242][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:02,244][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:02,246][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:02,248][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:02,249][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:02,251][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:02,252][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:02,297][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:02,300][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:02,303][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:02,305][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:02,306][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:02,308][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:02,309][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:02,341][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:02,343][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:02,346][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:02,349][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:02,351][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:02,354][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:02,357][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:02,359][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:02,362][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:02,364][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:02,365][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:02,366][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:02,428][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:02,430][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:02,432][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:02,434][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:02,524][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:02,889][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:02,920][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:02,922][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:02,925][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:02,951][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:02,955][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:02,975][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:02,978][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:03,055][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:03,059][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:03,062][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:03,065][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:03,068][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:03,103][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:03,106][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:03,107][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:03,108][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:03,143][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:03,145][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:03,147][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:03,150][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:03,152][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:03,154][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:03,155][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:03,156][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:03,158][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:03,159][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:03,160][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:03,160][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:03,161][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:03,161][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:03,162][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:03,162][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:03,163][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:03,166][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:03,167][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:03,168][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:03,233][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:03,236][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:03,238][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:03,240][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:03,242][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:03,244][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:03,325][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:03,328][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:03,331][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:03,334][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:03,341][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:03,344][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:03,346][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:03,349][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:03,351][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:03,352][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:03,549][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:03,551][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:04,215][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:04,218][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:04,219][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:04,222][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:04,225][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:04,227][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:04,229][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:04,231][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:04,233][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:04,235][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:04,237][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:04,241][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:04,244][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:04,247][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:04,250][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:04,251][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:04,252][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:04,254][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:04,255][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:04,256][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:04,258][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:04,259][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:04,284][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:04,286][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:04,287][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:04,289][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:04,290][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:04,292][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:04,294][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:04,484][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:04,486][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:04,530][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:04,533][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:04,535][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:04,537][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:04,539][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:04,542][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:04,544][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:04,546][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:04,576][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:04,578][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:04,580][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:04,583][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:04,586][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:04,589][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:04,590][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:04,592][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:04,595][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:05,014][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:05,018][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:05,041][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:05,045][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:05,069][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:05,108][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:05,111][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:05,112][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:05,114][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:05,115][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:05,180][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:05,181][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:05,183][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:05,270][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:05,273][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:05,279][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:05,286][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:05,290][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:05,292][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:05,297][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:05,302][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:05,306][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:05,358][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:05,360][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:05,364][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:05,368][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:05,371][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:05,373][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:05,377][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:05,378][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:05,442][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:05,536][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:05,540][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:05,543][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:05,546][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:05,588][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:05,592][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:05,596][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:05,622][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:05,624][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:05,626][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:05,655][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:05,859][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:06,000][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:06,003][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:06,021][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:06,209][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:06,215][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:06,222][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:06,228][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:06,230][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:06,232][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:06,234][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:06,235][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:06,236][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:06,237][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:06,238][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:06,239][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:06,240][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:06,241][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:06,242][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:06,244][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:06,246][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:06,274][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:06,278][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:06,281][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:06,283][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:06,306][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:06,307][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:06,308][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:06,310][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:06,346][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:06,348][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:06,350][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:06,434][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:06,439][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:06,443][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:06,522][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:06,607][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:06,611][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:06,615][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:06,618][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:06,692][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:06,696][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:06,699][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:06,702][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:06,705][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:06,708][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:06,775][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:06,778][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:06,781][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:06,860][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:06,988][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:06,995][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:07,012][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:07,019][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:07,023][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:07,036][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:07,060][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:07,082][mllm.models.large_language_model_local][WARNING] - Response Given that both you and Bob value the items differently and that the values are quite skewed, a strategic proposal would be to try to allocate items based on their lower valuations. Since both of you value hats and books similarly but balls are more valuable to you, you might consider proposing to take more balls to maximize your points. A balanced approach would be to take as many of the balls as possible while also getting some hats and books. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:07,089][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:07,093][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:07,108][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:07,111][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:07,135][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:07,139][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:07,142][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:07,145][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:07,148][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:07,151][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:07,153][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:07,155][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:07,157][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:07,160][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:07,162][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:07,188][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:07,189][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:07,190][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:07,191][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:07,193][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:07,195][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:07,198][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:07,200][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:07,201][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:07,204][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:07,206][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:07,207][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:07,209][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:07,211][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:07,212][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:07,213][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:07,214][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:07,216][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:07,542][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:07,545][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:07,547][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:07,550][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:07,553][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:07,555][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:07,558][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:07,561][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:07,563][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:07,566][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:07,568][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:07,571][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:07,598][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:07,604][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:07,608][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:07,711][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:07,714][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:07,739][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:07,773][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:07,829][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:07,868][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:07,872][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:07,875][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:07,896][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:07,903][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:07,909][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:07,916][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:07,928][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:07,934][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:07,940][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:07,946][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:07,985][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:07,986][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:07,989][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:07,990][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:07,992][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:07,993][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:07,995][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:07,997][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:07,998][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:07,999][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:08,000][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:08,004][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:08,006][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:08,008][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:08,009][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:08,011][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:08,013][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:08,014][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:08,034][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:08,036][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:08,038][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:08,039][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:08,041][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:08,090][mllm.models.large_language_model_local][WARNING] - Response Given the values, it appears that both hats and balls are highly valued by both players, while books are less valued but still somewhat valued. To maximize points, we should try to secure as many of the highly valued items (hats and balls) as possible, while also ensuring we get some books to secure a good score. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:08,092][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:08,179][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:08,182][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:08,185][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:08,263][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:08,285][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:08,288][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:08,295][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:08,301][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:08,308][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:08,314][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:08,321][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:08,327][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:08,333][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:08,336][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:08,339][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:08,364][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:08,367][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:08,369][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:08,371][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:08,867][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:08,870][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:08,934][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:08,941][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:08,973][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:09,069][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:09,072][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:09,106][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:09,229][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:09,232][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:09,235][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:09,237][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:09,240][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:09,242][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:09,245][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:09,275][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:09,281][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:09,287][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:09,294][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:09,300][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:09,307][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:09,313][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:09,319][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:09,325][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:09,331][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:09,334][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:09,337][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:09,373][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:09,375][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:09,377][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:09,378][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:09,380][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:09,538][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:09,541][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:09,545][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:09,607][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:09,610][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:09,650][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:09,654][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:09,656][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:09,663][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:09,670][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:09,673][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:09,675][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:09,683][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:09,763][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:09,765][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:09,829][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:09,854][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:09,882][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:09,907][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:09,911][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:09,913][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:09,932][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:09,958][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:09,961][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:09,964][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:09,970][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:10,230][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:10,235][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:10,237][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:10,240][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:10,242][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:10,278][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:10,282][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:10,285][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:10,291][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:10,297][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:10,302][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:10,308][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:10,311][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:10,313][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:10,316][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:10,319][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:10,334][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:10,337][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:10,339][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:10,341][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:10,343][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:10,355][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:10,359][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:10,361][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:10,363][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:10,374][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:10,409][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:10,413][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:10,465][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:10,468][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:10,472][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:10,476][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:10,480][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:10,483][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:10,512][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:10,514][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:10,517][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:10,599][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:10,602][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:10,682][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:10,685][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:10,688][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:10,853][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the items to be split, it's optimal to propose a fair split to maximize the points. Since both you and Bob value the hats and balls equally and the books are valued more by you, proposing an equitable distribution is strategic. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:10,995][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:11,034][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:11,036][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:11,039][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:11,041][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:11,043][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:11,045][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:11,047][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:11,072][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:11,075][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:11,077][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:11,080][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:11,082][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:11,136][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:11,142][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:11,147][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:11,150][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:11,153][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:11,155][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:11,171][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:11,192][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:11,241][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:11,244][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:11,246][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:11,248][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:11,249][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:11,251][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:11,343][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:11,346][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:11,348][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:11,687][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:11,690][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:11,724][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:11,731][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:11,752][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:11,755][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:11,758][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:11,801][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:11,804][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:11,841][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:11,844][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:11,877][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:11,880][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:11,883][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:11,886][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:11,889][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:11,891][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:11,894][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:11,897][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:11,899][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:11,902][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:11,940][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:11,943][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:11,946][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:11,948][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:12,113][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:12,120][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:12,123][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:12,198][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:12,249][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:12,253][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:12,281][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:12,284][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:12,288][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:12,290][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:12,296][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:12,302][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:12,308][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:12,311][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:12,314][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:12,317][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:12,319][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:12,322][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:12,325][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:12,356][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:12,359][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:12,366][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:12,369][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:12,371][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:12,482][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:12,486][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:12,489][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:12,505][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:12,508][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:12,511][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:12,513][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:12,530][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's important to maximize the points based on the per-item values. Since the per-item values are high for hats and books but much lower for balls, it's beneficial to keep most of the hats and books and a smaller portion of balls. Proposal: 10 hats, 10 books, 7 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:12,533][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:12,562][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:12,565][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:12,596][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:12,733][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:12,778][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:12,781][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:12,783][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:12,827][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:12,866][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:12,869][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:12,871][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:12,888][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:12,891][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:12,893][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:12,896][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:12,902][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 7 books, 3 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:12,917][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:12,924][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:12,927][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:12,930][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:12,932][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:12,934][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:12,936][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:12,938][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:12,940][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:12,942][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:12,973][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:12,975][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:13,072][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:13,075][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:13,094][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:13,101][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:13,173][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:13,176][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:13,182][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:13,185][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:13,251][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:13,258][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:13,331][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:13,410][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:13,651][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:13,655][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:13,657][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:13,660][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:13,663][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:13,666][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:13,668][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:13,671][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:13,674][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:13,677][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:13,699][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:13,701][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:13,704][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:13,707][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:13,724][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:13,728][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:13,758][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:13,837][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:13,843][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:13,848][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:13,926][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:13,990][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:13,997][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:14,037][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:14,042][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:14,049][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:14,051][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:14,054][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:14,056][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:14,059][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:14,062][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:14,064][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:14,089][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:14,091][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:14,094][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:14,096][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:14,098][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:14,103][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:14,106][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:14,343][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:14,384][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:14,387][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:14,389][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:14,392][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:14,395][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:14,397][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:14,400][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:14,429][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:14,436][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:14,439][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:14,537][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:14,540][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:14,542][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:14,545][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:14,576][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:14,579][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:14,644][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:14,727][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:14,734][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:14,741][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:14,747][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:14,751][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:14,753][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:14,756][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:14,807][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:14,810][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:14,812][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:14,814][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:14,890][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:14,977][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:14,980][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:15,064][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:15,067][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:15,155][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:15,158][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:15,161][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:15,249][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:15,331][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:15,414][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:15,494][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:15,497][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:15,500][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:15,503][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:15,505][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:15,532][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:15,535][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:15,538][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:15,616][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:15,622][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:15,629][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:15,635][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:15,663][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:15,741][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:15,744][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:15,747][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:15,749][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:16,037][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:16,041][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:16,044][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:16,244][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:16,247][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:16,249][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:16,252][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:16,256][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:16,262][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:16,269][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:16,272][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:16,274][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:16,277][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:16,280][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:16,283][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:16,325][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:16,541][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:16,544][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:16,547][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:16,549][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:16,552][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:16,728][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:16,731][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:16,750][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:16,753][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:16,757][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:16,764][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:16,767][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:16,770][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:16,773][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:16,829][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:16,915][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:16,918][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:16,987][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:16,990][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:16,993][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:17,005][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:17,023][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:17,040][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:17,043][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:17,046][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:17,048][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:17,051][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:17,082][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:17,085][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:17,087][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:17,090][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:17,093][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:17,100][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:17,103][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:17,106][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:17,108][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:17,111][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:17,113][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:17,122][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:17,125][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:17,128][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:17,142][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:17,158][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:17,174][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:17,220][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:17,223][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:17,226][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:17,233][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:17,247][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:17,249][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:17,252][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:17,257][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:17,262][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:17,264][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:17,266][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:17,269][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:17,271][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:17,282][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:17,286][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:17,349][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:17,352][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:17,355][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:17,397][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:17,400][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:17,403][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:17,406][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:17,432][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:17,438][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:17,445][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:17,451][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:17,461][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:17,467][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:17,470][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:17,473][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:17,479][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:17,482][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:17,496][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:17,522][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:17,565][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:17,568][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:17,581][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:17,584][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:17,598][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:17,601][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:17,604][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:17,606][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:17,613][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:17,617][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:17,644][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:17,649][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:17,656][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:17,693][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:17,699][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:17,771][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:17,777][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:17,809][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:17,814][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:17,818][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:17,821][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:17,830][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:17,907][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:17,914][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:17,928][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:17,931][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:17,942][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:17,971][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:17,974][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:18,059][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the random nature of the item allocation, it is beneficial to propose a fair split to maximize the points in each round. My proposal will be: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:18,063][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:18,153][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:18,234][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:18,285][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:18,289][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:18,293][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:18,376][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:18,383][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:18,430][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:18,433][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:18,573][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:18,576][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:18,579][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:18,623][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:18,626][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:18,636][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:18,639][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:18,641][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:18,650][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:18,653][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:18,656][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:18,851][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:18,854][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:18,864][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:18,867][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:18,893][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:18,905][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:18,918][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:18,921][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:18,936][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:18,992][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:19,004][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:19,071][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:19,082][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:19,105][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:19,128][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:19,135][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:19,153][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:19,157][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:19,257][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:19,271][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:19,419][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:19,670][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:19,700][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:19,704][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:44:19,870][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:44:20,040][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:44:21,123][__main__][INFO] - Number of regex retries in iteration 558: 1310 [2025-11-13 06:44:21,124][__main__][INFO] - agents played in iteration 558 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 06:44:22,030][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:44:22,050][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:44:22,070][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:44:22,090][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:44:22,090][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 06:44:22,091][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 06:44:22,792][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 06:44:23,238][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 06:44:23,731][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 06:44:24,221][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 06:44:24,724][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 06:44:25,209][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 06:44:25,697][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 06:44:26,184][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 06:44:26,670][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 06:44:27,163][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 06:44:27,650][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 06:44:28,140][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 06:44:28,639][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 06:44:29,129][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 06:44:29,619][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 06:44:30,110][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 06:44:30,599][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 06:44:31,086][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 06:44:31,574][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 06:44:32,061][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 06:44:32,550][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 06:44:33,037][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 06:44:33,525][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 06:44:34,034][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 06:44:34,522][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 06:44:35,011][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 06:44:35,507][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 06:44:35,995][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 06:44:36,483][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 06:44:36,973][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 06:44:37,461][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 06:44:37,952][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 06:44:38,440][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 06:44:38,927][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 06:44:39,430][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 06:44:39,919][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 06:44:40,406][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 06:44:40,903][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 06:44:41,389][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 06:44:41,880][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 06:44:42,366][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 06:44:42,854][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 06:44:43,344][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 06:44:43,835][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 06:44:44,326][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 06:44:44,815][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 06:44:45,305][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 06:44:45,796][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 06:44:46,285][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 06:44:46,784][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 06:44:47,278][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 06:44:47,773][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 06:44:48,268][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 06:44:48,764][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 06:44:49,257][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 06:44:49,753][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 06:44:50,248][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 06:44:50,747][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 06:44:51,244][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 06:44:51,741][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 06:44:52,235][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 06:44:52,731][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 06:44:53,228][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 06:44:53,742][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 06:44:54,241][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10861 tokens. [2025-11-13 06:44:55,112][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.24%, Current % of VRAM taken: 59.69%, Block Peak % of device VRAM: 62.31%, ΔTime: 00:00:32 [2025-11-13 06:44:55,854][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 06:44:55,856][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 06:44:55,857][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 06:44:56,754][__main__][INFO] - Iteration 559 took 1m 20s (55.56% Gen, 43.32% Train). Generation: 44s, Training: 34s. Estimated remaining time: 58h 13m 26s. Estimated total time: 66h 48m 42s. Time estimates for 10 more iterations: 13m 21s, 100 more iterations: 2h 13m 37s, 500 more iterations: 11h 8m 7s. [2025-11-13 06:44:56,755][__main__][INFO] - Starting iteration 559. [2025-11-13 06:44:57,270][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 55 and human policies 1. [2025-11-13 06:44:57,271][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 06:45:09,957][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:09,982][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:09,985][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:09,987][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:09,990][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:09,993][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:09,995][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:09,997][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:10,000][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:10,003][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:10,005][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:10,007][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:10,007][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:10,008][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:10,009][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:10,036][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:10,038][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:10,040][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:10,041][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:10,043][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:10,044][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:10,046][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:10,048][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:10,049][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:10,051][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:10,052][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:10,055][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:10,065][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:10,067][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:10,069][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:10,070][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:10,072][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:10,073][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:10,075][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:10,077][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:10,078][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:10,080][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:10,103][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:10,105][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:10,107][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:10,108][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:10,110][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:10,112][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:10,114][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:10,115][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:10,118][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:10,119][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:10,121][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:10,123][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:10,124][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:10,126][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:10,127][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:10,129][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:10,186][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:10,188][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:10,190][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:10,192][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:10,194][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:10,196][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:10,198][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:10,200][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:10,202][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:10,203][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:10,205][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:10,207][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:10,209][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:10,211][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:10,213][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:10,215][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:10,217][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:10,219][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:10,221][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:10,252][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:10,534][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:10,537][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:10,540][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:10,542][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:10,544][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:10,562][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:10,565][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:10,567][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:10,570][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:10,572][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:10,575][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:10,577][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:10,580][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:10,583][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:10,600][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:10,605][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:10,690][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:10,690][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:10,692][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:10,692][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:10,693][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:10,693][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:10,694][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:10,696][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:10,697][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:10,698][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:10,699][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:10,699][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:10,700][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:10,701][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:10,703][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:10,716][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:10,717][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:10,719][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:10,720][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:10,722][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:10,723][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:10,724][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:10,726][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:10,727][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:10,729][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:10,731][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:10,732][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:10,734][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:10,735][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:10,737][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:10,758][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:11,241][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:11,276][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:11,283][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:11,309][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:11,311][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:11,314][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:11,320][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:11,327][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:11,333][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:11,335][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:11,360][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:11,362][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:11,364][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:11,366][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:11,368][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:11,369][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:11,371][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:11,373][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:11,374][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:11,376][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:11,378][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:11,379][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:11,396][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:11,398][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:11,399][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:11,400][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:11,401][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:11,403][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:11,404][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:11,406][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:11,408][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:11,410][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:11,412][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:11,413][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:11,415][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:11,417][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:11,418][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:11,420][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:11,421][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:11,423][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:11,437][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:11,438][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:11,440][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:11,441][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:11,445][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:11,448][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:11,452][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:11,455][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:11,458][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:11,461][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:11,464][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:11,467][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:11,469][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:11,470][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:11,514][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:11,515][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:11,516][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:11,518][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:11,519][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:11,911][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:11,914][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:11,940][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:11,943][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:11,968][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:11,974][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:11,976][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:11,978][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:11,980][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:11,993][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:11,995][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:11,997][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:11,999][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:12,001][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:12,003][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:12,005][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:12,007][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:12,009][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:12,011][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:12,026][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:12,029][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:12,096][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:12,098][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:12,098][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:12,100][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:12,102][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:12,103][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:12,106][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:12,107][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:12,109][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:12,110][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:12,111][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:12,112][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:12,113][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:12,115][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:12,116][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:12,117][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:12,118][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:12,119][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:12,121][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:12,122][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:12,123][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:12,124][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:12,125][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:12,127][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:12,128][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:12,130][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:12,131][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:12,132][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:12,530][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:12,533][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:12,553][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:12,556][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:12,559][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:12,562][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:12,577][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:12,580][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:12,600][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:12,606][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:12,609][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:12,612][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:12,615][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:12,617][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:12,623][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:12,629][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:12,635][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:12,642][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:12,648][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:12,654][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:12,660][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:12,695][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:12,697][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:12,698][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:12,699][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:12,700][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:12,701][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:12,707][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:12,708][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:12,710][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:12,711][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:12,712][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:12,714][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:12,716][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:12,717][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:12,719][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:12,720][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:12,722][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:12,723][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:12,727][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:12,730][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:12,734][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:12,738][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:12,741][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:12,742][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:12,744][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:12,745][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:12,747][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:12,748][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:12,750][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:12,751][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:12,753][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:12,754][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:12,755][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:12,755][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:12,756][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:12,758][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:12,759][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:12,760][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:12,761][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:12,762][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:12,763][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:12,765][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:12,767][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:12,768][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:12,769][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:12,775][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:12,776][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:12,777][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:12,778][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:12,779][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:12,780][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:12,780][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:12,781][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:12,782][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:12,783][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:12,783][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:13,239][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:13,246][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:13,252][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:13,260][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:13,263][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:13,273][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:13,296][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:13,301][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:13,318][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:13,320][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:13,434][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:13,436][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:13,438][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:13,476][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:13,480][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:13,483][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:13,485][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:13,492][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:13,495][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:13,498][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:13,500][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:13,503][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:13,505][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:13,700][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:13,812][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:13,813][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:13,816][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:14,256][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:14,287][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:14,293][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:14,299][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:14,311][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:14,314][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:14,316][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:14,337][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:14,340][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:14,342][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:14,363][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:14,366][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:14,368][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:14,388][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:14,391][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:14,394][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:14,396][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:14,398][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:14,453][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:14,504][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:14,507][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:14,509][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:14,511][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:14,513][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:14,515][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:14,517][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:14,597][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:14,600][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:14,603][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:14,606][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:14,609][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:14,611][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:14,614][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:14,617][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:14,619][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:14,621][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:14,624][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:14,627][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:14,630][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:14,632][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:14,634][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:14,637][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:14,685][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:14,687][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:14,690][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:14,692][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:14,695][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:14,698][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:14,700][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:14,730][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:14,733][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:14,736][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:14,738][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:14,741][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:14,743][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:14,746][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:14,749][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:14,767][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:14,770][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:14,772][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:14,775][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:14,777][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:14,780][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:14,782][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:14,784][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:14,787][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:14,789][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:14,791][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:14,793][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:14,796][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:14,798][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:14,912][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:14,914][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:14,917][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:14,919][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:14,932][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:14,956][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:14,959][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:14,961][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:14,963][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:14,966][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:14,973][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:14,976][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:14,978][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:14,981][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:15,055][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:15,058][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:15,060][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:15,105][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:15,111][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:15,118][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:15,121][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:15,162][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:15,165][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:15,167][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:15,233][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:15,237][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:15,240][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:15,242][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:15,245][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:15,249][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:15,251][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:15,311][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:15,392][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:15,395][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:15,397][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:15,399][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:15,401][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:15,403][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:15,405][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:15,407][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:15,410][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:15,412][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:15,414][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:15,416][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:15,419][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:15,421][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:15,423][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:15,425][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:15,474][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:15,478][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:15,481][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:15,483][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:15,486][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:15,532][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:15,535][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:15,541][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:15,544][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:15,547][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:15,556][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:15,559][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:15,562][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:15,564][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:15,581][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:15,584][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:15,586][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:15,588][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:15,591][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:15,593][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:15,595][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:15,598][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:15,604][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:15,610][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:15,613][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:15,615][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:15,784][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:15,787][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:15,790][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:15,792][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:15,806][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:15,812][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:15,829][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:16,106][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:16,162][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:16,164][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:16,166][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:16,168][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:16,170][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:16,172][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:16,173][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:16,175][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:16,179][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:16,183][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:16,187][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:16,191][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:16,196][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:16,200][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:16,205][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:16,210][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:16,214][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:16,219][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:16,223][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:16,227][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:16,231][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:16,249][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:16,254][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:16,258][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:16,262][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:16,332][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:16,337][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:16,388][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:16,394][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:16,399][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:16,405][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:16,410][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:16,413][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:16,436][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:16,523][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:16,700][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:16,704][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:16,706][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:16,709][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:16,725][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:16,749][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:16,796][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:16,799][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:16,801][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:16,804][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:16,806][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:16,808][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:16,810][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:16,813][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:16,840][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:16,843][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:16,846][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:16,866][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:16,869][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:16,871][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:16,874][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:16,876][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:16,890][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:16,910][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:16,913][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:16,917][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:16,933][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:16,936][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:16,938][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:16,957][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:17,034][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:17,036][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:17,132][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:17,309][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:17,312][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:17,314][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:17,316][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:17,393][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:17,396][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:17,399][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:17,401][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:17,404][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:17,406][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:17,409][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:17,411][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:17,414][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:17,417][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:17,661][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:17,668][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:17,671][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:17,679][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:17,686][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:17,704][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:17,705][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:17,706][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:17,707][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:17,708][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:17,710][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:17,711][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:17,730][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:17,732][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:17,734][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:17,758][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:17,761][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:17,764][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:17,766][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:17,769][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:17,783][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:17,807][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:17,810][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:17,812][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:17,835][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:17,838][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:17,841][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:17,843][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:17,846][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:17,848][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:17,888][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:17,891][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:17,894][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:17,896][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:17,899][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:17,902][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:17,904][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:17,941][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:17,944][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:17,946][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:17,948][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:17,951][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:17,953][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:17,956][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:17,958][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:17,961][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:17,963][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:17,966][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:17,969][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:17,971][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:17,974][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:17,976][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:17,979][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:17,982][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:17,984][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:18,027][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:18,030][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:18,033][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:18,035][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:18,067][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:18,070][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:18,072][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:18,075][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:18,149][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:18,151][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:18,154][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:18,156][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:18,415][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:18,418][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:18,420][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:18,452][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:18,455][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:18,457][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:18,482][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:18,488][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:18,494][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:18,500][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:18,505][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:18,511][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:18,517][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:18,527][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:18,532][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:18,542][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:18,547][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:18,552][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:18,558][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:18,601][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:18,668][mllm.models.large_language_model_local][WARNING] - Response Given the current values, you should prioritize books since they have the highest value for both you and Alice. However, since you and Alice have very different values for hats and balls, you should take advantage of this imbalance to maximize your points. Here's the proposal: Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:18,670][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:18,676][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:18,681][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:18,730][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:18,733][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:18,736][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:18,742][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:18,749][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:18,812][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:18,814][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:18,903][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:18,970][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:18,973][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:18,976][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:18,978][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:18,981][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:18,983][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:18,986][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:18,988][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:18,991][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:18,993][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:18,996][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:18,998][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:19,001][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:19,004][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:19,007][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:19,010][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:19,012][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:19,214][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:19,217][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:19,219][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:19,222][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:19,224][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:19,227][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:19,229][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:19,231][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:19,233][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:19,235][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:19,372][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:19,375][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:19,419][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:19,426][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:19,432][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:19,444][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:19,447][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:19,449][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:19,513][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:19,815][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:19,818][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:19,820][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:19,821][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:19,825][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:19,895][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:19,898][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:19,904][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:19,910][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:19,917][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:19,923][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:19,929][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:19,936][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:19,942][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:19,948][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:19,955][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:19,962][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:19,967][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:19,973][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:19,978][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:19,983][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:19,988][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:19,991][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:19,993][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:20,004][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:20,006][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:20,009][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:20,011][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:20,026][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:20,029][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:20,031][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:20,033][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:20,036][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:20,052][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:20,055][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:20,057][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:20,059][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:20,061][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:20,064][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:20,066][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:20,068][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:20,070][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:20,072][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:20,075][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:20,217][mllm.models.large_language_model_local][WARNING] - Response Given the valuationwhere hats are highly valued by Bob and books and balls are highly valued by you, a targeted proposal might be more effective. Here’s a proposal that aims to capture more value: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:20,221][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:20,223][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:20,248][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:20,266][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:20,269][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:20,272][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:20,275][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:20,277][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:20,280][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:20,282][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:20,285][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:20,287][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:20,319][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:20,322][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:20,324][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:20,328][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:20,331][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:20,345][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:20,348][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:20,350][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:20,353][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:20,356][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:20,367][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:20,370][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:20,373][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:20,376][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:20,391][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:20,394][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:20,397][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:20,413][mllm.models.large_language_model_local][WARNING] - Response Given the current per-item values and Bob's strategy, it seems that both hats and books are considered low-value items by both of us, while balls are high-value items by our respective valuations. A cooperative approach might still be beneficial, but a cautious proposal would be to take more of the high-value balls and share the other items. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:20,458][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:20,571][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:20,572][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:20,574][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:20,575][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:20,576][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:20,578][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:20,650][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:20,653][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:20,656][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:20,658][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:20,661][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:20,697][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:20,704][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:20,708][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:20,710][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:20,713][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:20,715][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:20,717][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:20,719][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:20,721][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:20,722][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:20,949][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:20,952][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:21,114][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:21,117][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:21,124][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:21,136][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:21,138][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:21,141][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:21,143][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:21,167][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:21,169][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:21,172][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:21,174][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:21,176][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:21,179][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:21,184][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:21,203][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:21,205][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:21,208][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:21,210][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:21,234][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:21,237][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:21,239][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:21,241][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:21,293][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:21,295][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:21,297][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:21,299][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:21,301][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:21,318][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:21,320][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:21,323][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:21,342][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:21,367][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:21,370][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:21,373][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:21,376][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:21,378][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 books, 10 hats, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:21,381][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:21,406][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:21,413][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:21,416][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:21,418][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:21,422][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:21,424][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:21,427][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:21,429][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:21,432][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:21,434][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:21,437][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:21,440][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:21,443][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:21,445][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:21,447][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:21,454][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:21,460][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:21,466][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:21,471][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:21,477][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:21,482][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:21,487][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:21,489][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:21,491][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:21,715][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:21,718][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:21,720][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:21,824][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:21,852][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:21,855][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:21,858][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:21,860][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:21,887][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:21,893][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:21,899][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:21,906][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:21,912][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:21,918][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:21,955][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:21,958][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:21,962][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:21,967][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:21,971][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:21,976][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:21,978][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:22,041][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:22,047][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:22,053][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:22,125][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Bob have a higher valuation for hats and books, with balls being less valuable. A cooperative approach might lead to better outcomes. Here is a proposal that aims to balance the distribution: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:22,128][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:22,134][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:22,141][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:22,147][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:22,153][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:22,210][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:22,216][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:22,223][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:22,296][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:22,384][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:22,387][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:22,390][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:22,393][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:22,396][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:22,486][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:22,489][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:22,574][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:22,577][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:22,580][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:22,582][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:22,585][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:22,587][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:22,590][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:22,592][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:22,594][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:22,597][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:22,599][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:22,604][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:22,606][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:22,658][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:22,743][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:22,745][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:22,747][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:22,844][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:22,847][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:22,850][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:22,852][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:22,942][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:22,945][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:22,948][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:22,951][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:22,954][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:22,956][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:22,959][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:22,961][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:23,043][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:23,046][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:23,053][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:23,059][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:23,192][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:23,195][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:23,244][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:23,440][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:23,775][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:23,778][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:23,783][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:23,790][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:23,797][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:23,803][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:23,809][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:23,816][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:23,821][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:23,827][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:23,832][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:23,838][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:23,843][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:23,848][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:23,851][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:23,853][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:24,514][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:24,517][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:24,520][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:24,523][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:24,566][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:24,569][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:24,572][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:24,575][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:24,577][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:24,580][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:24,583][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:24,586][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:24,588][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:24,591][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:24,594][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:24,596][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:24,691][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:24,698][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:24,882][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:24,885][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:24,888][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:24,910][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:24,913][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:24,945][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:24,948][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:24,950][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:24,952][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:24,954][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:25,083][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:25,086][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:25,088][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:25,091][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:25,093][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:25,096][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:25,174][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:25,177][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:25,180][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:25,183][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:25,186][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:25,188][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:25,224][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:25,227][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:25,230][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:25,249][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Bob value hats and books much less than balls. Since you have a higher value for balls (10) compared to Bob (1), and Bob has a higher value for hats and books, the optimal strategy might involve proposing to keep all the balls while dividing the hats and books proportionally. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:25,256][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:25,274][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:25,277][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:25,280][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:25,282][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:25,300][mllm.models.large_language_model_local][WARNING] - Response Given the value distribution, both you and Alice have high values for hats and books, and low values for balls. Here is a proposal that maximizes the use of high-value items: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:25,303][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:25,327][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:25,330][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:25,333][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:25,336][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:25,338][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:25,341][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:25,345][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:25,383][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:25,389][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:25,392][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:25,395][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:25,460][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:25,462][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:25,463][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:25,465][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:25,467][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:25,468][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:25,635][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:25,638][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:25,972][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:25,975][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:25,978][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:26,018][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:26,042][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:26,045][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:26,156][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:26,159][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:26,162][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:26,164][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:26,166][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:26,169][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:26,171][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:26,217][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:26,220][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:26,222][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:26,225][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:26,227][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:26,286][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:26,289][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:26,291][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:26,428][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:26,431][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:26,434][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:26,483][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:26,489][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:26,492][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:26,725][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:26,728][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:26,731][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:26,734][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:26,736][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:26,739][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:26,742][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:26,745][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:26,747][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:26,750][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:26,752][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:26,755][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:26,757][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:26,760][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:26,766][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:26,772][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:26,779][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:26,782][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:26,784][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:26,814][mllm.models.large_language_model_local][WARNING] - Response Given the values: - You value hats at 10, books at 10, and balls at 1. - Bob values hats at 1, books at 10, and balls at 10. The optimal strategy here is to propose taking all items to maximize your value. Since both of you have the same proposed distribution, the items will likely be allocated proportionally if Bob also takes everything he can. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:26,818][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:26,820][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:26,823][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:26,826][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:26,828][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:26,831][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:26,833][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:26,836][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:26,839][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:26,904][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:26,907][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:26,909][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:26,911][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:26,914][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:26,916][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:26,919][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:26,921][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:27,095][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:27,098][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:27,100][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:27,150][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:27,153][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:27,159][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:27,165][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:27,172][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:27,178][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:27,184][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:27,191][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:27,400][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:27,403][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:27,405][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:27,491][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:27,498][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:27,581][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:27,587][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:27,771][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:27,865][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:27,868][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:27,871][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:27,874][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:27,951][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:27,954][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:28,039][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:28,042][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:28,091][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:28,094][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:28,097][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:28,100][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:28,102][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:28,105][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:28,107][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:28,110][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:28,113][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:28,115][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:28,117][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:28,120][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:28,124][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:28,130][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:28,136][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:28,143][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:28,172][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:28,176][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:28,179][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:28,183][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:28,187][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:28,189][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:28,191][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:28,193][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:28,213][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:28,215][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:28,217][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:28,218][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:28,292][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:28,294][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:28,380][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:28,470][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:28,790][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:28,794][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:28,796][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:28,800][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:28,802][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:28,806][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:28,809][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:28,812][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:28,814][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:28,817][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:28,855][mllm.models.large_language_model_local][WARNING] - Response Given the high value of books and balls for both you and Alice, and the low value of hats, a strategic proposal might involve splitting the books and balls equally while taking all the hats. This ensures you get the full benefit of the high-value items. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:29,208][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:29,211][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:29,214][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:29,217][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:29,220][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:29,223][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:29,226][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:29,228][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:29,298][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:29,301][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:29,351][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:29,358][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:29,365][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:29,372][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:29,378][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:29,384][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:29,391][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:29,397][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:29,409][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:29,415][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:29,421][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:29,427][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:29,472][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:29,474][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:29,477][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:29,479][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:29,626][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:29,630][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:29,632][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:29,635][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:29,637][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:29,640][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:29,642][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:29,673][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:29,675][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:29,677][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:29,679][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:29,681][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:29,683][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:29,685][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:29,687][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:29,723][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:29,725][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:29,727][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:29,728][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:29,730][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:29,732][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:29,802][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:29,804][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:29,867][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:29,870][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:30,110][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:30,151][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:30,154][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:30,157][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:30,159][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:30,207][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:30,210][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:30,213][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:30,220][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:30,226][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:30,233][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:30,240][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:30,292][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:30,294][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:30,377][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:30,380][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:30,382][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:30,384][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:30,447][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:30,450][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:30,452][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:30,455][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:30,474][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:30,497][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:30,500][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:30,503][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:30,506][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:30,512][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:30,518][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:30,524][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:30,536][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:30,539][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:30,542][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:30,545][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:30,548][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:30,551][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:30,553][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:30,556][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:30,559][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:30,561][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:30,564][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:30,566][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:30,569][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:30,572][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:30,575][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:30,726][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:30,728][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:30,771][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:30,774][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:30,777][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:30,796][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and Bob's strategy, it's clear that both hats and books are highly valued by both of us, while balls are valued less but still have some worth. To maximize points, we should ensure we get a fair share of the highly valued items. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:30,803][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:30,806][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:30,809][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:30,812][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:30,850][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:30,853][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:30,856][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:30,859][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:30,876][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:30,878][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:30,881][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:30,884][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:30,987][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:31,067][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:31,070][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:31,073][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:31,076][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:31,151][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:31,154][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:31,157][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:31,159][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:31,162][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:31,165][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:31,168][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:31,171][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:31,173][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:31,176][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:31,179][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:31,181][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:31,246][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:31,252][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:31,259][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:31,302][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:31,305][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:31,308][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:31,346][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:31,366][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:31,373][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:31,379][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:31,393][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:31,396][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:31,399][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:31,402][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:31,428][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:31,433][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:31,438][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:31,442][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:31,447][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:31,451][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:31,456][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:31,458][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:31,460][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:31,462][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:31,464][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:31,466][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:31,468][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:31,470][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:31,472][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:31,477][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:31,479][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:31,529][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:31,531][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:31,562][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:31,587][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:31,590][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:31,593][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:31,609][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:31,613][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:31,615][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:31,659][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:31,663][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:31,715][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:31,921][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:31,952][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:31,955][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:31,958][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:31,961][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:31,963][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:31,966][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:31,968][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:31,971][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:31,974][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:31,980][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:31,984][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:31,986][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:31,989][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:31,992][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:32,020][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:32,088][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:32,093][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:32,097][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:32,186][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:32,354][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:32,361][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:32,368][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:32,404][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:32,407][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:32,409][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:32,412][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:32,472][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:32,479][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:32,485][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:32,488][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:32,491][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:32,494][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:32,497][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:32,499][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:32,502][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:32,560][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:32,562][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:32,614][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:32,706][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:32,713][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:32,719][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:32,787][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:32,793][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:32,800][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:33,122][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:33,382][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:33,385][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:33,388][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:33,391][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:33,393][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:33,474][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:33,481][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:33,488][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:33,495][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:33,499][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:33,502][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:33,506][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:33,509][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:33,512][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:33,515][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:33,518][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:33,519][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:33,521][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:33,563][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:33,664][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:33,777][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:33,781][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:33,806][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:33,809][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:33,812][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:33,814][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:33,817][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:33,861][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:33,868][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:33,874][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:33,881][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:33,887][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:33,894][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:33,900][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:33,906][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:33,910][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:33,912][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:33,915][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:33,917][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:33,920][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:33,923][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:34,223][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:34,298][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:34,301][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:34,304][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:34,307][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:34,310][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:34,313][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:34,316][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:34,318][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:34,325][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:34,391][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:34,392][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:34,394][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:34,466][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:34,469][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:34,471][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:34,581][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:34,584][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:34,587][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:34,589][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:34,673][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:34,676][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:34,678][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:34,681][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:34,765][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:34,769][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:34,775][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:34,781][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:34,788][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:34,794][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:34,932][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:34,935][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:34,938][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:34,941][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:34,943][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:34,946][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:34,949][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:34,951][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:34,954][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:35,024][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:35,091][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:35,094][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:35,192][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:35,213][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:35,254][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:35,257][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:35,260][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:35,263][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:35,270][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:35,276][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:35,287][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:35,290][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:35,293][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:35,309][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:35,312][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:35,315][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:35,317][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:35,319][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:35,321][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:35,323][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:35,324][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:35,325][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:35,326][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:35,327][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:35,328][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:35,493][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:35,544][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:35,547][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:35,550][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:35,556][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:35,562][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:35,568][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:35,574][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:35,580][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:35,586][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:35,589][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:35,591][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:35,593][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:35,658][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:35,739][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:35,741][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:35,842][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:35,900][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:35,906][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:35,940][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:35,947][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:35,950][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:35,953][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:35,955][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:35,958][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:35,961][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:35,963][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:35,966][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:35,969][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:35,971][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:35,974][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:35,976][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:35,979][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:35,982][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:36,010][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:36,044][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:36,138][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:36,142][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:36,145][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:36,158][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:36,161][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:36,245][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:36,248][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:36,253][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:36,259][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:36,266][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:36,269][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:36,272][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:36,274][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:36,277][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:36,280][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:36,283][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:36,286][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:36,288][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:36,291][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:36,366][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:36,371][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 5 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:36,459][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:36,830][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:36,922][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:36,929][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:36,936][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:36,939][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:36,946][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:36,952][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:36,958][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:36,965][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:36,971][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:36,978][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:36,984][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:37,000][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:37,002][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:37,005][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:37,681][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:37,684][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:37,687][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:37,690][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:37,827][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:37,844][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:38,040][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:38,044][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:38,085][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:38,087][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:38,090][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:38,092][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:38,094][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:38,110][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:38,113][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:38,170][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:38,173][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:38,175][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:38,178][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:38,190][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:38,193][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:38,196][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:38,198][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:38,201][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:38,212][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:38,215][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:38,289][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:38,320][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:38,335][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:38,338][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:38,340][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:38,353][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:38,356][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:38,359][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:38,362][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:38,365][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:38,393][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:38,396][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:38,408][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:38,425][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:38,428][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:38,431][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:38,433][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:38,436][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:38,439][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:38,441][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:38,444][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:38,450][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:38,453][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:38,494][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:38,501][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:38,510][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:38,517][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:38,520][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:38,523][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:38,526][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:38,531][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:38,534][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:38,617][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:38,644][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:38,660][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:38,663][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:38,666][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:38,675][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:38,678][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:38,680][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:38,690][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:38,693][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:38,703][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:38,709][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:38,718][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:38,733][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:38,736][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:38,738][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:38,741][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:38,747][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:38,754][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:38,763][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:38,769][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:38,775][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:38,781][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:38,811][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:38,827][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:38,833][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:38,839][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:38,845][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:38,851][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:38,931][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:38,945][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:38,951][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:38,960][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:38,967][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:38,973][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:38,982][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:39,008][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:39,025][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:39,031][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:39,036][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:39,324][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:39,327][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:39,340][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:39,343][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:39,375][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:39,378][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:39,424][mllm.models.large_language_model_local][WARNING] - Response Given our values and Bob's values, it's clear that both of us prioritize balls the most. However, to ensure we get a fair share of all items, we should propose a split that maximizes our share of the items we value highly, while still taking into account Bob's high valuation of hats and books. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:39,492][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:39,496][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:39,499][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:39,536][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:39,539][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:45:39,547][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:39,550][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:39,559][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:39,562][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:39,594][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:39,597][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:39,638][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:39,702][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:39,705][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:39,736][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:39,739][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:45:39,748][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:39,760][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:39,767][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:39,802][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:39,809][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:39,849][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:39,924][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:39,931][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:39,960][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:45:41,258][__main__][INFO] - Number of regex retries in iteration 559: 1689 [2025-11-13 06:45:41,258][__main__][INFO] - agents played in iteration 559 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 06:45:42,338][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:45:42,364][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:45:42,387][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:45:42,410][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.58%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:45:42,410][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 06:45:42,411][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 06:45:43,229][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 06:45:43,679][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 06:45:44,178][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 06:45:44,669][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 06:45:45,164][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 06:45:45,654][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 06:45:46,149][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 06:45:46,658][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 06:45:47,156][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 06:45:47,658][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 06:45:48,155][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 06:45:48,653][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 06:45:49,152][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 06:45:49,649][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 06:45:50,147][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 06:45:50,661][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 06:45:51,158][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 06:45:51,655][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 06:45:52,154][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 06:45:52,653][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 06:45:53,160][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 06:45:53,661][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 06:45:54,161][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 06:45:54,666][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 06:45:55,166][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 06:45:55,667][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 06:45:56,170][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 06:45:56,673][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 06:45:57,178][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 06:45:57,680][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 06:45:58,182][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 06:45:58,686][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 06:45:59,189][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 06:45:59,693][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 06:46:00,195][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 06:46:00,697][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 06:46:01,201][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 06:46:01,702][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 06:46:02,200][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 06:46:02,705][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 06:46:03,207][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 06:46:03,711][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 06:46:04,211][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 06:46:04,710][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 06:46:05,211][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 06:46:05,711][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 06:46:06,210][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 06:46:06,710][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 06:46:07,211][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 06:46:07,711][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 06:46:08,209][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 06:46:08,707][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 06:46:09,205][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 06:46:09,702][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 06:46:10,200][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 06:46:10,698][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 06:46:11,198][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 06:46:11,698][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 06:46:12,197][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 06:46:12,696][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 06:46:13,196][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 06:46:13,696][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 06:46:14,186][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 06:46:14,677][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 06:46:15,171][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10877 tokens. [2025-11-13 06:46:15,990][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.21%, Current % of VRAM taken: 59.66%, Block Peak % of device VRAM: 62.34%, ΔTime: 00:00:32 [2025-11-13 06:46:16,635][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 06:46:16,636][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 06:46:16,638][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 06:46:17,413][__main__][INFO] - Iteration 560 took 1m 20s (54.88% Gen, 44.15% Train). Generation: 43s, Training: 35s. Estimated remaining time: 58h 10m 35s. Estimated total time: 66h 47m 10s. Time estimates for 10 more iterations: 13m 21s, 100 more iterations: 2h 13m 34s, 500 more iterations: 11h 7m 51s. [2025-11-13 06:46:17,415][__main__][INFO] - Starting iteration 560. [2025-11-13 06:46:17,902][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 55 and human policies 1. [2025-11-13 06:46:17,903][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 06:46:39,287][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:39,309][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:39,312][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:39,315][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:39,317][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:39,319][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:39,322][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:39,325][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:39,328][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:39,331][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:39,333][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:39,336][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:39,346][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:39,349][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:39,352][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:39,355][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:39,357][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:39,360][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:39,363][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:39,365][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:39,367][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:39,370][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:39,372][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:39,375][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:39,396][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:39,398][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:39,400][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:39,402][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:39,404][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:39,406][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:39,408][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:39,410][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:39,412][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:39,414][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:39,416][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:39,418][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:39,420][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:39,422][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:39,424][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:39,425][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:39,427][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:39,429][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:39,431][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:39,433][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:39,436][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:39,438][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:39,439][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:39,442][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:39,458][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:39,460][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:39,461][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:39,463][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:39,465][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:39,467][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:39,468][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:39,470][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:39,472][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:39,474][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:39,476][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:39,477][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:39,479][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:39,481][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:39,483][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:39,484][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:39,486][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:39,488][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:39,489][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:39,491][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:39,493][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:39,495][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:39,497][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:39,498][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:39,500][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:39,502][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:39,504][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:39,505][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:39,666][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:39,683][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:39,685][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:39,688][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:39,690][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:39,693][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:39,700][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:39,702][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:39,705][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:39,707][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:39,732][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:39,734][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:39,736][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:39,738][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:39,740][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:39,742][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:39,744][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:39,746][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:39,748][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:39,761][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:39,763][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:39,765][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:39,767][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:39,769][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:39,775][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:39,777][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:39,779][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:39,781][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:39,783][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:39,785][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:39,786][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:39,788][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:39,791][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:39,793][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:39,795][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:39,797][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:39,814][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:39,816][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:39,818][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:39,820][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:39,822][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:39,823][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:39,839][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:39,919][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:40,231][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:46:40,254][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:46:40,260][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:46:40,276][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:46:40,282][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:46:40,288][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:46:40,294][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:46:40,306][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:46:40,311][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:46:40,317][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:40,325][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:40,327][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:46:40,332][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:46:40,338][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:46:40,343][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:46:40,348][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:46:40,373][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:40,375][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:40,377][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:40,378][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:40,379][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:46:40,383][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:46:40,386][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:46:40,390][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:46:40,393][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:46:40,397][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:46:40,400][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:46:40,417][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:46:40,420][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:46:40,423][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:46:40,426][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:46:40,430][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:46:40,433][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:40,435][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:40,436][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:46:40,440][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:46:40,443][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:40,446][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:40,480][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:40,481][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:40,483][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:40,484][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:40,485][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:40,487][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:40,557][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:40,559][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:40,561][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:40,562][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:40,564][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:40,566][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:40,567][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:40,569][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:40,570][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:40,571][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:40,571][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:40,574][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:40,576][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:40,577][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:40,579][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:40,580][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:40,582][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:40,584][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:40,585][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:40,587][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:40,589][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:40,590][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:40,592][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:40,950][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:40,953][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:41,016][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:41,018][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:41,019][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:41,095][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:41,097][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:41,098][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:41,101][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:41,102][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:41,104][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:41,105][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:41,107][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:41,109][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:41,111][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:41,112][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:41,332][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:41,334][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:41,709][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:41,735][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:41,738][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:41,741][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:41,757][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:41,760][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:41,779][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:41,802][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:41,804][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:41,808][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:41,810][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:41,835][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:41,838][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:41,840][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:41,842][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:41,844][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:41,847][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:41,849][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:41,851][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:41,865][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:41,868][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:41,871][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:41,873][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:41,875][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:41,877][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:41,905][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:41,960][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:41,962][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:41,964][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:41,966][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:42,045][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:42,046][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:42,048][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:42,049][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:42,051][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:42,053][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:42,133][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:42,136][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:42,138][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:42,141][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:42,143][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:42,146][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:42,148][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:42,151][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:42,199][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:42,202][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:42,205][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:42,208][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:42,210][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:42,213][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:42,215][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:42,217][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:42,220][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:42,223][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:42,226][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:42,228][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:42,230][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:42,233][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:42,235][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:42,239][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:42,241][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:42,243][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:42,246][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:42,248][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:42,250][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:42,432][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:42,456][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:42,459][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:42,461][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:42,487][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:42,529][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:42,558][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:42,561][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:42,563][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:42,565][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:42,582][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:42,585][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:42,588][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:42,590][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:42,593][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:42,615][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:42,617][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:42,620][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:42,622][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:42,625][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:42,627][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:42,630][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:42,632][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:42,635][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:42,638][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:42,641][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:42,643][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:42,646][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:42,648][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:42,651][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:42,661][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:42,664][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:42,668][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:42,670][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:42,672][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:42,739][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:42,742][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:42,744][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:42,746][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:42,748][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:42,750][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:42,753][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:42,755][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:42,758][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:42,760][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:42,817][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:42,887][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:42,889][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:42,890][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:42,893][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:42,894][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:42,896][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:42,898][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:42,900][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:43,443][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:43,526][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:43,607][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:43,609][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:43,611][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:43,612][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:43,614][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:43,615][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:43,693][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:43,696][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:46:43,702][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:46:43,708][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:46:43,714][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:46:43,720][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:46:43,726][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:46:43,732][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:43,780][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:43,783][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:43,867][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:43,870][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:43,872][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:43,875][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:43,877][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:43,880][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:43,920][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:43,923][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:43,926][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:43,928][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:43,931][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:43,933][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:43,936][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:43,938][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:43,941][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:43,943][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:43,946][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:43,948][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:43,950][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:44,044][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:44,074][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:44,077][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:44,079][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:44,097][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:44,099][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:44,120][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:44,145][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:44,148][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:44,151][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:44,170][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:44,173][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:44,175][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:44,178][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:44,180][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:44,183][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:44,195][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:44,198][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:44,201][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:44,203][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:44,206][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:44,208][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:44,211][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:44,214][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:44,222][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:44,225][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:44,228][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:44,230][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:44,233][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:44,235][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:44,252][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:44,254][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:44,256][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:44,272][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:44,274][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:44,276][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:44,278][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:44,280][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:44,282][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:44,284][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:44,286][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:44,288][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:44,290][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:44,292][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:44,325][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:44,328][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:44,330][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:44,406][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:44,408][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:44,410][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:46:44,480][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:44,481][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:44,483][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:44,484][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:44,486][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:44,487][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:44,562][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:44,564][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:44,566][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:44,569][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:44,571][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:44,640][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:44,643][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:44,646][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:44,648][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:44,651][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:44,653][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:44,656][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:44,658][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:44,954][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:44,975][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:45,011][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:45,014][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:45,059][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:45,112][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:45,115][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:45,117][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:45,138][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:45,141][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:45,144][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:45,146][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:45,148][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:45,151][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:45,168][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:45,171][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:45,173][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:45,247][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:45,250][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:45,253][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:45,255][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:45,257][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:45,260][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:45,263][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:45,318][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:45,321][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:45,323][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:45,326][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:45,400][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:45,607][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:45,609][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:45,611][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:45,613][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:45,757][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:45,760][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:45,762][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:45,765][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:45,767][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:45,769][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:45,772][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:45,775][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:45,777][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:45,840][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:45,843][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:45,845][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:46,014][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:46,349][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:46,352][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:46,434][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:46,436][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:46,439][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:46,514][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:46,517][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:46,519][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:46,522][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:46,525][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:46,562][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:46,564][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:46,567][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:46,570][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:46,573][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:46,576][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:46,682][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:46,684][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:46,687][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:46,746][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:46,749][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:46,751][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:46,754][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:46,756][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:46,759][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:46,761][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:46,763][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:46,766][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:46,768][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:46,771][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:46,830][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:46,913][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:46,915][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:46,918][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:46,921][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:46,924][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:46,926][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:46,929][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:46,995][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:46,998][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:47,000][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:47,003][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:47,006][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:47,009][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:47,012][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:47,014][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:47,017][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:47,019][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:47,078][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:47,103][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:47,106][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:47,135][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:47,138][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:47,205][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:47,242][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:47,245][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:47,248][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:47,260][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:47,263][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:47,266][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:47,268][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:47,296][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:47,299][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:47,301][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:47,304][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:47,306][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:47,309][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:47,338][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:47,341][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:47,344][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:47,373][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:47,376][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:46:47,382][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:47,385][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:47,388][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:47,390][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:47,393][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:47,570][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:47,572][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:47,573][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:47,575][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:47,790][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:47,791][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:47,793][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:47,981][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:48,276][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:48,367][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:46:48,373][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:48,376][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:48,378][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:48,381][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:48,383][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:48,435][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:48,480][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:48,483][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:48,486][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:48,488][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:48,491][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:48,494][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:48,497][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:48,521][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:48,524][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:48,527][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:48,530][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:48,533][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:48,660][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:48,718][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:48,722][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:48,725][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:48,753][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:48,756][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:48,759][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:48,761][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:48,764][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:48,766][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:48,769][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:48,772][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:48,774][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:48,777][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:48,802][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:48,805][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:48,807][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:48,810][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:48,812][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:48,815][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:48,818][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:48,820][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:48,848][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:48,850][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:48,853][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:48,855][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:48,857][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:48,859][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:48,862][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:48,864][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:48,866][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:48,868][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:48,870][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:48,932][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:48,982][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:48,985][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:49,020][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:49,023][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:49,068][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:49,105][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:49,108][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:49,111][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:49,129][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:49,132][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:49,135][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:49,138][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:49,166][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:49,170][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:49,172][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:49,175][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:49,177][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:49,180][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:49,182][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:49,185][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:49,217][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:49,219][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:49,222][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:49,225][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:49,298][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:46:49,305][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:46:49,311][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:46:49,317][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:46:49,324][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:46:49,330][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:49,515][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:49,592][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:49,594][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:49,596][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:49,598][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:49,819][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:49,899][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:49,902][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:50,007][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:50,494][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:50,586][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:50,589][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:50,592][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:50,595][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:50,598][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:50,674][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:50,678][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:50,680][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:50,683][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:50,686][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:50,688][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:50,692][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:50,764][mllm.models.large_language_model_local][WARNING] - Response Given the values, it is beneficial to secure the items with the highest personal value, which are hats and balls. Books have a low value for both of us. Proposal: 10 hats, 10 balls, 0 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:50,851][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:50,943][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:50,946][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:51,029][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:51,032][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:51,035][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:51,038][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:51,041][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:51,043][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:51,046][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:51,118][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:51,121][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:51,124][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:51,126][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:51,129][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:51,132][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:51,135][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:51,137][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:51,166][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:51,169][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:51,172][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:51,174][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:51,177][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:51,180][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:51,183][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:51,185][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:51,188][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:51,190][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:51,193][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:51,196][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:51,203][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:51,225][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:51,228][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:51,231][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:51,254][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:51,291][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:51,294][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:51,323][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:51,325][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:51,357][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:51,360][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:51,363][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:51,366][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:51,391][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:51,394][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:51,396][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:51,399][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:51,402][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:51,404][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:51,407][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:51,443][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:51,446][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:51,449][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:51,451][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:51,454][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:51,522][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:51,525][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:51,528][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:51,530][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:51,533][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:51,536][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:51,583][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:51,745][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the equal likelihood of assignment, a cooperative approach seems beneficial. Since both you and Bob value hats and books highly, and balls less so, splitting them evenly should help maximize the total points for both of you. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:51,782][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:51,846][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:51,849][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:51,852][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:51,931][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:51,934][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:51,936][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:51,938][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:51,940][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:51,942][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:51,944][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:52,015][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:52,018][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:52,021][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:52,023][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:52,026][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:52,028][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:52,031][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:52,034][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:52,100][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:46:52,107][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:46:52,113][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:52,116][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:52,119][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:52,121][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:52,187][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:52,190][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:52,192][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:52,195][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:52,197][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:52,200][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:52,203][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:52,262][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:52,265][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:52,267][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:52,270][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:53,206][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:53,296][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:53,348][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:53,352][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:53,383][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:53,388][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:53,391][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:53,394][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:53,524][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:53,527][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:53,530][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:53,532][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:53,535][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:53,572][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:53,575][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:53,721][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:46:53,728][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:46:53,733][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:46:53,740][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:46:53,744][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:46:53,749][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:46:53,755][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:46:53,761][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:46:53,766][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:46:53,770][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:46:53,773][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:46:53,777][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:46:53,812][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:46:53,816][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:46:53,820][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:46:53,824][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:46:53,827][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:46:53,831][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:46:53,835][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:46:53,900][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:46:53,905][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:46:53,909][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:46:53,914][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:53,917][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:54,094][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:54,098][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:54,181][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:54,273][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:54,277][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:54,280][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:54,284][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:54,287][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:54,355][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:54,358][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:54,362][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:54,365][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:54,367][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:54,370][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:54,443][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:54,446][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:54,450][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:54,453][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:54,530][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:54,533][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:54,536][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:54,540][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:54,543][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:54,546][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:54,549][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:54,624][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:54,885][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:54,889][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:55,053][mllm.models.large_language_model_local][WARNING] - Response Given the values, proposing an equal split might not be optimal since I value hats and books less compared to Bob, but balls more. To maximize my points, I should aim to get more of the items I value higher. Here's a strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:55,308][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:55,312][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:55,315][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:55,319][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:55,322][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:55,393][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:55,397][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:55,401][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:55,404][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:55,410][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:55,480][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:55,482][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:55,484][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:55,486][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:55,488][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:55,735][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that hats and books are more valuable to me, while balls are more valuable to Alice. To maximize my points, I should focus on keeping the items that are more valuable to me. Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:56,659][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:56,662][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:56,665][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:56,668][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:56,671][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:56,674][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:56,675][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:56,677][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:56,679][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:56,682][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:56,684][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:56,725][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:56,728][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:56,732][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:56,735][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:56,991][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:57,085][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:57,089][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:57,093][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:57,096][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:57,098][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:57,099][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:57,103][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:57,106][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:57,109][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:57,112][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:57,115][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:57,118][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:57,121][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:57,124][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:57,127][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:57,130][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:57,132][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:57,135][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:57,137][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:57,140][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:57,141][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:57,143][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:57,144][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:57,172][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:57,174][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:57,178][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:57,180][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:57,258][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:57,343][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:57,347][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:57,350][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:57,354][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:57,357][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:57,360][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:57,363][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:57,427][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:57,430][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:57,433][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:57,511][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:57,514][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:57,597][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:57,600][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:57,767][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:57,770][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:57,849][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:57,853][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:57,856][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:57,858][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:57,931][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:57,935][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:57,939][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:58,016][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:58,113][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:58,116][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:58,441][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:58,445][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:58,448][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:58,529][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:58,533][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:58,537][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:58,538][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:58,540][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:58,543][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:58,629][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:58,632][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:58,636][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:59,080][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both hats and balls are highly valuable to you and only moderately valuable to Alice. Books are highly valuable to Alice but only moderately valuable to you. To maximize your points, it makes sense to propose a distribution that keeps the high-value items (hats and balls) for yourself while giving Alice the books. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:59,084][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:59,207][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:59,211][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:59,214][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:59,218][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:59,220][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:59,223][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:59,226][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:59,229][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:59,232][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:59,235][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:59,237][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:59,240][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:59,241][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:59,243][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:59,245][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:59,287][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:59,291][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:59,293][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:59,295][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:59,297][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:59,298][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:59,301][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:59,303][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:59,305][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:59,307][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:59,642][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:59,646][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:59,731][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:59,736][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:59,739][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:59,741][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:59,746][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:59,749][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:59,752][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:59,754][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:59,757][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:59,760][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:46:59,763][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:59,766][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:59,825][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:59,829][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:59,833][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:46:59,836][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:00,087][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:00,173][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:00,177][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:00,180][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:00,184][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:00,187][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:00,191][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:00,194][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:00,258][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:00,261][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:00,340][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:00,344][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:00,347][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:00,351][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:00,355][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:00,357][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:00,359][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:00,359][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:00,361][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:00,364][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:00,426][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:00,675][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:00,678][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:00,681][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:00,757][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:01,020][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:01,210][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:47:01,217][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:47:01,224][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:47:01,231][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:47:01,238][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:47:01,244][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:47:01,251][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:47:01,256][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:47:01,261][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:47:01,264][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:47:01,266][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:01,268][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:01,647][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:01,650][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:01,687][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:01,691][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:01,694][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:01,698][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:01,701][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:01,738][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:01,742][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:01,745][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:47:01,748][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:47:01,751][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:47:01,753][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:47:01,755][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:47:01,757][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:47:01,761][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:47:01,764][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:47:01,767][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:47:01,769][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:47:01,772][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:01,775][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:47:01,777][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:47:01,778][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:47:01,780][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:47:01,782][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:01,785][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:01,787][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:01,835][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:01,838][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:01,841][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:01,842][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:01,845][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:01,924][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:01,926][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:01,928][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:01,929][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:47:01,931][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:01,932][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:01,933][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:01,935][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:02,011][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:47:02,014][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:47:02,015][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:47:02,016][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:02,411][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:02,414][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:02,568][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:02,569][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:02,729][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:02,732][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:02,968][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:02,969][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:03,050][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:03,051][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:47:03,053][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:47:03,136][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:47:03,137][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:47:03,139][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:47:03,140][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:47:03,142][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:03,143][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:47:03,147][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:47:03,228][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:47:03,230][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:47:03,231][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:47:03,235][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:47:03,237][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:47:03,240][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:47:03,243][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:47:03,245][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:47:03,247][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:47:03,249][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:47:03,252][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:47:03,255][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:47:03,256][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:47:03,258][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:47:03,260][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:47:03,263][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:47:03,265][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:47:03,266][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:47:03,268][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:03,269][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:03,270][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:03,271][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:03,273][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:03,274][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:03,275][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:03,276][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:03,277][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:03,770][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:03,771][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:03,772][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:03,774][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:47:03,815][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:03,817][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:03,818][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:03,820][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:03,821][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:47:03,823][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:47:03,825][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:47:03,826][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:47:03,828][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:47:03,829][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:47:03,831][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:47:03,833][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:47:03,834][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:47:03,837][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:47:03,840][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:47:03,843][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:47:03,846][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:47:03,900][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:47:03,904][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:47:03,908][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:47:03,911][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:47:03,914][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:47:03,916][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:47:03,918][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:47:03,919][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:47:03,921][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:47:03,922][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:47:03,923][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:47:03,924][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:47:03,925][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:47:03,927][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:47:04,226][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:04,227][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:04,307][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:04,628][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:04,788][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:04,869][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:05,031][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:05,113][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:06,430][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:06,676][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:06,679][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:06,683][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:06,686][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:06,689][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:06,690][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:06,693][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:06,696][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:06,698][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:06,701][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:06,709][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:06,711][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:06,713][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:06,715][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:07,112][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:07,113][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:07,114][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:07,115][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:07,197][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:07,199][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:07,200][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:07,202][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:07,203][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:07,204][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:07,206][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:07,444][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:07,447][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:07,449][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:47:07,751][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:07,753][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:07,832][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:07,834][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:07,913][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:09,109][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:09,113][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:09,115][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:09,146][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:09,204][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:09,258][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:09,259][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:09,323][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:09,324][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:09,335][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:09,336][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:09,337][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:47:09,338][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:47:09,340][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:47:09,348][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:47:09,402][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:47:09,455][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:47:09,457][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:47:09,520][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:47:09,532][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:47:09,534][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:47:09,536][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:47:09,539][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:47:09,543][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:47:09,547][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:47:09,602][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:47:09,658][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:47:09,725][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:47:09,737][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:47:09,739][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:47:09,794][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the values and the previous outcomes, it seems that proposing to take all the items appears to be a fair and competitive strategy. However, we should consider that such a proposal might lead to a proportional distribution if both agents do the same. To maximize the points, let's try a different approach by acknowledging Bob's higher value for balls and your higher value for hats and books. Proposal: 10 hats, 3 books, 7 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:10,015][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:47:10,132][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:47:10,190][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:47:10,307][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:47:10,490][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:47:11,221][__main__][INFO] - Number of regex retries in iteration 560: 1218 [2025-11-13 06:47:11,222][__main__][INFO] - agents played in iteration 560 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 06:47:12,189][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:47:12,215][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:47:12,239][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:47:12,260][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:47:12,261][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 06:47:12,262][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 06:47:13,045][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 06:47:13,494][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 06:47:13,988][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 06:47:14,485][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 06:47:14,974][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 06:47:15,465][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 06:47:15,969][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 06:47:16,461][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 06:47:16,952][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 06:47:17,452][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 06:47:17,944][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 06:47:18,435][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 06:47:18,924][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 06:47:19,413][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 06:47:19,904][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 06:47:20,399][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 06:47:20,895][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 06:47:21,390][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 06:47:21,884][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 06:47:22,375][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 06:47:22,866][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 06:47:23,356][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 06:47:23,859][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 06:47:24,352][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 06:47:24,842][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 06:47:25,341][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 06:47:25,829][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 06:47:26,318][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 06:47:26,808][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 06:47:27,297][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 06:47:27,790][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 06:47:28,279][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 06:47:28,767][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 06:47:29,265][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 06:47:29,753][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 06:47:30,242][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 06:47:30,731][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 06:47:31,220][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 06:47:31,708][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 06:47:32,195][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 06:47:32,687][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 06:47:33,180][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 06:47:33,669][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 06:47:34,158][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 06:47:34,649][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 06:47:35,136][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 06:47:35,628][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 06:47:36,117][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 06:47:36,605][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 06:47:37,095][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 06:47:37,585][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 06:47:38,074][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 06:47:38,562][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 06:47:39,050][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 06:47:39,540][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 06:47:40,029][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 06:47:40,519][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 06:47:41,010][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 06:47:41,499][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 06:47:41,989][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 06:47:42,478][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 06:47:42,967][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 06:47:43,472][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 06:47:43,964][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 06:47:44,451][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10841 tokens. [2025-11-13 06:47:45,226][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.20%, Current % of VRAM taken: 59.65%, Block Peak % of device VRAM: 62.18%, ΔTime: 00:00:32 [2025-11-13 06:47:45,967][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 06:47:45,968][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 06:47:45,970][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 06:47:49,718][__main__][INFO] - Iteration 561 took 1m 31s (58.07% Gen, 37.85% Train). Generation: 53s, Training: 34s. Estimated remaining time: 67h 52m 38s. Estimated total time: 76h 30m 46s. Time estimates for 10 more iterations: 15m 18s, 100 more iterations: 2h 33m 1s, 500 more iterations: 12h 45m 7s. [2025-11-13 06:47:49,722][__main__][INFO] - Starting iteration 561. [2025-11-13 06:47:50,216][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 56 and human policies 1. [2025-11-13 06:47:50,217][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 06:48:09,219][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:09,241][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:09,244][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:09,245][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:09,246][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:09,249][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:09,251][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:09,272][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:09,273][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:09,275][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:09,276][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:09,278][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:09,279][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:09,281][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:09,282][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:09,301][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:09,303][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:09,305][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:09,307][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:09,308][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:09,310][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:09,312][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:09,313][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:09,314][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:09,315][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:09,317][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:09,318][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:09,320][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:09,321][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:09,324][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:09,326][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:09,327][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:09,328][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:09,329][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:09,330][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:09,332][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:09,333][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:09,334][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:09,335][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:09,336][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:09,336][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:09,337][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:09,347][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:09,349][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:09,350][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:09,351][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:09,352][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:09,353][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:09,355][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:09,355][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:09,356][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:09,356][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:09,357][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:09,357][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:09,359][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:09,360][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:09,361][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:09,362][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:09,363][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:09,364][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:09,366][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:09,367][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:09,369][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:09,370][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:09,371][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:09,372][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:09,373][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:09,380][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:09,382][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:09,383][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:09,384][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:09,385][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:09,386][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:09,387][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:09,388][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:09,389][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:09,389][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:09,390][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:09,392][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:09,393][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:09,394][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:09,395][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:09,396][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:09,397][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:09,618][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:09,634][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:09,636][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:09,637][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:09,638][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:09,639][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:09,652][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:09,654][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:09,656][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:09,658][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:09,661][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:09,663][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:09,665][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:09,690][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:09,692][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:09,693][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:09,695][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:09,697][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:09,699][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:09,700][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:09,702][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:09,703][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:09,704][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:09,706][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:09,730][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:09,733][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:09,735][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:09,736][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:09,738][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:09,739][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:09,741][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:09,743][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:09,745][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:09,746][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:09,748][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:09,749][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:09,753][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:09,754][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:09,754][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:09,756][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:09,756][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:09,758][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:09,759][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:09,760][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:09,762][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:09,763][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:09,764][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:09,765][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:09,766][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:09,770][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:09,771][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:09,772][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:09,773][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:09,773][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:09,774][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:09,775][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:09,775][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:09,775][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:09,776][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:09,778][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:09,786][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:09,788][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:09,805][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:09,807][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:09,808][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:10,058][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:10,089][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:10,093][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:10,096][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:10,098][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:10,101][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:10,105][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:10,110][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:10,131][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:10,133][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:10,136][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:10,138][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:10,141][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:10,143][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:10,145][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:10,147][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:10,148][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:10,151][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:10,157][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:10,159][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:10,161][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:10,163][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:10,166][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:10,168][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:10,200][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:10,203][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:10,205][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:10,207][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:10,209][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:10,212][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:10,214][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:10,216][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:10,218][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:10,220][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:10,223][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:10,248][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:10,251][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:10,254][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:10,256][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:10,258][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:10,261][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:10,263][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:10,266][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:10,268][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:10,270][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:10,272][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:10,274][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:10,276][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:10,278][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:10,280][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:10,282][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:10,297][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:10,298][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:10,299][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:10,300][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:10,302][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:10,303][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:10,304][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:10,305][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:10,342][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:10,343][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:10,344][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:10,347][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:10,348][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:10,349][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:10,350][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:10,352][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:10,353][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:10,354][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:10,738][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:10,740][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:10,743][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:10,745][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:10,747][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:10,748][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:10,750][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:10,780][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:10,782][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:10,784][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:10,786][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:10,788][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:10,861][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:10,862][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:11,024][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:11,027][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:11,110][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:11,112][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:11,114][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:11,116][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:11,119][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:11,122][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:11,123][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:11,124][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:11,126][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:11,127][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:11,560][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:11,563][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:11,566][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:11,570][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:11,590][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:11,593][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:11,634][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:11,709][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:11,712][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:11,714][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:11,717][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:11,719][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:11,720][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:11,721][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:11,722][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:11,723][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:11,725][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:11,727][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:11,793][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:11,795][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:11,797][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:11,799][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:11,801][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:11,803][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:11,805][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:11,807][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:11,808][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:11,811][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:11,813][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:11,815][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:11,817][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:11,819][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:11,821][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:11,823][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:11,824][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:11,826][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:11,828][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:11,830][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:11,832][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:11,834][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:11,836][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:11,837][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:11,839][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:11,841][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:11,842][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:11,844][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:11,846][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:11,851][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:11,853][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:11,855][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:11,857][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:11,859][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:11,861][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:11,862][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:11,864][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:11,865][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:11,866][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:11,867][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:11,868][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:11,880][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:11,882][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:11,883][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:11,885][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:11,886][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:11,887][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:11,889][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:11,890][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:11,892][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:11,893][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:11,894][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:11,901][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:11,903][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:11,904][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:11,907][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:11,911][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:11,913][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:11,916][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:11,919][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:11,921][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:11,922][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:12,168][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:12,172][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:12,175][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:12,177][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:12,180][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:12,190][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:12,194][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:12,198][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:12,216][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:12,219][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:12,221][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:12,224][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:12,226][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:12,228][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:12,239][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:12,241][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:12,243][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:12,245][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:12,246][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:12,248][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:12,249][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:12,252][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:12,253][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:12,256][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:12,258][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:12,259][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:12,290][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:12,292][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:12,295][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:12,298][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:12,301][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:12,303][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:12,304][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:12,306][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:12,308][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:12,309][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:12,310][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:12,312][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:12,314][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:12,315][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:12,316][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:12,347][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:12,348][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:12,350][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:12,353][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:12,354][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:12,355][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:12,356][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:12,359][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:12,361][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:12,363][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:12,365][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:12,367][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:12,369][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:12,371][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:12,373][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:12,375][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:12,377][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:12,379][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:12,382][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:12,383][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:12,385][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:12,420][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:12,422][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:12,424][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:12,430][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:12,434][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:12,436][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:12,439][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:12,441][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:12,443][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:12,445][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:12,446][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:12,464][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:12,467][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:12,485][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:12,487][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:12,489][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:12,710][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:12,717][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:12,724][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:12,728][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:12,734][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:12,735][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:12,737][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:12,738][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:12,748][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:12,770][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:12,774][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:12,824][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:12,828][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:12,908][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:12,911][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:12,914][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:12,916][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:12,919][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:12,921][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:12,923][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:12,926][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:12,928][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:12,932][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:12,935][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:12,937][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:12,939][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:12,941][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:12,943][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:12,946][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:12,948][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:12,950][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:12,952][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:12,955][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:12,958][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:12,992][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:12,995][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:12,998][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:13,001][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:13,003][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:13,007][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:13,009][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:13,012][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:13,015][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:13,059][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:13,062][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:13,064][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:13,065][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:13,066][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:13,068][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:13,106][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:13,110][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:13,114][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:13,116][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:13,118][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:13,120][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:13,464][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:13,471][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:13,477][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:13,484][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:13,490][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:13,524][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:13,526][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:13,529][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:13,536][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:13,539][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:13,542][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:13,544][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:13,546][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:13,549][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:13,551][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:13,554][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:13,556][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:13,558][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:13,560][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:13,605][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:13,608][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:13,610][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:13,612][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:13,615][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:13,617][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:13,619][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:13,622][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:13,623][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:13,685][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:13,688][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:13,690][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:13,692][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:13,694][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:13,696][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:13,699][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:13,701][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:13,703][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:13,708][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:13,781][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:13,855][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:13,857][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:13,935][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:13,938][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:13,942][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:13,944][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:13,946][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:14,340][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:14,344][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:14,377][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:14,379][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:14,381][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:14,383][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:14,385][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:14,390][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:14,459][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:14,462][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:14,468][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:14,470][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:14,473][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:14,476][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:14,478][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:14,480][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:14,483][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:14,485][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:14,545][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:14,547][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:14,550][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:14,552][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:14,555][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:14,557][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:14,560][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:14,562][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:14,568][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:14,571][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:14,573][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:14,576][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:14,578][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:14,580][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:14,583][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:14,586][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:14,588][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:14,591][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:14,593][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:14,596][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:14,598][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:14,601][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:14,603][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:14,606][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:14,612][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:14,615][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:14,617][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:14,619][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:14,622][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:14,624][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:14,627][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:14,629][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:14,632][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:14,639][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:14,642][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:14,644][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:14,647][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:14,649][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:14,652][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:14,654][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:14,657][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:14,659][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:14,713][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:14,715][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:14,718][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:14,720][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:14,722][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:14,724][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:14,727][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:14,729][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:14,732][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:14,799][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:14,802][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:14,805][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:14,808][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:14,810][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:14,813][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:14,815][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:14,821][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:14,824][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:14,827][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:14,879][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:14,882][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:14,885][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:14,888][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:14,894][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:14,900][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:14,906][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:14,909][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:15,317][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 balls, 10 books, 0 hats did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:15,360][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:15,362][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:15,378][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:15,815][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:15,816][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:16,001][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:16,007][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:16,238][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 balls, 10 books, 0 hats did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:16,307][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:16,311][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:16,336][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:16,360][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:16,363][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:16,365][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:16,389][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:16,392][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:16,395][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:16,397][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:16,400][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:16,419][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:16,422][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:16,424][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:16,427][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:16,430][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:16,433][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:16,435][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:16,438][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:16,440][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:16,442][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:16,452][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:16,455][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:16,457][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:16,460][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:16,463][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:16,466][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:16,474][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:16,477][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:16,479][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:16,482][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:16,484][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:16,486][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:16,489][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:16,491][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:16,493][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:16,496][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:16,498][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:16,499][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:16,501][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:16,509][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:16,511][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:16,513][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:16,515][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:16,517][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:16,519][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:16,521][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:16,523][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:16,525][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:16,527][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:16,529][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:16,531][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:16,533][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:16,535][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:16,537][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:16,539][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:16,550][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:16,552][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:16,554][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:16,555][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:16,557][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:16,559][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:16,561][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:16,563][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:16,565][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:16,566][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:16,568][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:16,570][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:16,572][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:16,574][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:16,575][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:16,576][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:16,578][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:16,580][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:16,582][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:16,584][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:16,586][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:16,587][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:16,589][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:16,591][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:16,597][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:16,600][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:16,601][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:16,603][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:16,605][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:16,607][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:16,608][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:16,610][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:16,612][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:16,614][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:16,677][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:16,741][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:16,747][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:16,754][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:16,761][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:16,776][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:16,779][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:16,811][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:16,814][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:16,817][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:16,819][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:16,822][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:16,851][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:16,854][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:16,856][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:16,894][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:16,897][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:16,900][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:16,902][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:16,904][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:16,907][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:16,910][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:16,945][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:16,947][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:16,950][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:16,952][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:16,955][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:16,957][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:16,960][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:16,968][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:16,971][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:16,973][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:16,976][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:16,978][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:16,981][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:16,983][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:16,985][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:16,988][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:16,990][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:16,998][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:17,000][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:17,003][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:17,005][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:17,008][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:17,010][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:17,013][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:17,015][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:17,017][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:17,024][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:17,027][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:17,029][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:17,031][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:17,033][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:17,036][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:17,038][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:17,045][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:17,047][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:17,049][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:17,051][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:17,053][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:17,055][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:17,057][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:17,059][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:17,062][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:17,064][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:17,066][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:17,072][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:17,074][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:17,076][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:17,081][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:17,086][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:17,088][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:17,090][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:17,092][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:17,094][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:17,096][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:17,097][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:17,098][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:17,101][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:17,109][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:17,111][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:17,115][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:17,117][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:17,119][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:17,121][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:17,122][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:17,124][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:17,126][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:17,127][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:17,129][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:17,131][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:17,241][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:17,265][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:17,270][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:17,310][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:17,316][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:17,321][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:17,328][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:17,335][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:17,395][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:17,401][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:17,408][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:17,414][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:17,466][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:17,473][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:17,479][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:17,485][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:17,491][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:17,498][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:17,504][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:17,520][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:17,526][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:17,532][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:17,539][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:17,545][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:17,555][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:17,560][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:17,565][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:17,570][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:17,575][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:17,580][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:17,589][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:17,594][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:17,598][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:17,603][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:17,607][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:17,611][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:17,616][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:17,624][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:17,628][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:17,632][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:17,636][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:17,640][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:17,644][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:17,648][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:17,652][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:17,658][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:17,662][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:17,666][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:17,669][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:17,673][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:17,677][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:17,680][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:17,718][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:17,722][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:17,725][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:17,730][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:17,734][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:17,739][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:17,743][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:17,748][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:17,752][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:17,767][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:17,772][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:17,776][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:17,827][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:17,832][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:17,836][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:17,841][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:17,845][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:17,850][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:17,854][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:17,859][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:17,863][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:17,879][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:17,883][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:17,887][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:17,891][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:17,894][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:17,898][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:17,902][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:17,906][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:17,910][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:17,912][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:17,914][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:18,228][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:18,231][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:18,234][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:18,991][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:19,085][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:19,241][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:19,268][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:19,307][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:19,310][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:19,313][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:19,315][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:19,395][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:19,398][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:19,401][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:19,459][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:19,462][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:19,464][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:19,467][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:19,470][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:19,472][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:19,517][mllm.models.large_language_model_local][WARNING] - Response Given the updated per-item values where both you and Alice value hats and books at 10 and 1, respectively, but Alice values balls at 10 while you value them at 1, it makes sense to assume that Alice will prioritize balls. To maximize your points, you should propose to take all the items that you value highly and leave the balls to Alice. Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:19,519][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:19,522][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:19,538][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:19,541][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:19,543][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:19,566][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:19,568][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:19,571][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:19,573][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:19,576][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:19,578][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:19,581][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:19,599][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:19,602][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:19,604][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:19,607][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:19,609][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:19,611][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:19,614][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:19,616][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:19,619][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:19,621][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:19,624][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:19,626][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:19,633][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:19,636][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:19,638][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:19,640][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:19,642][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:19,644][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:19,646][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:19,648][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:19,651][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:19,653][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:19,655][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:19,673][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:19,676][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:19,678][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:19,680][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:19,683][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:19,686][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:19,688][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:19,690][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:19,693][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:19,712][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:19,715][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:19,718][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:19,735][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:19,738][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:19,756][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:19,759][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:19,761][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:19,782][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:19,785][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:19,787][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:19,790][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:19,802][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:19,805][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:19,808][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:19,811][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:19,813][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:19,816][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:19,819][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 balls, 10 books, 0 hats did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:19,827][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric and overlapping values between our per-item valuations, it's clear that proposing a fair split will likely result in the best outcome for both parties. Therefore, I will propose the following: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:19,833][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:19,836][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:19,838][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:19,841][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:19,908][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:19,911][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:19,914][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:20,003][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:20,077][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:20,080][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:20,082][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:20,085][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:20,087][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:20,090][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:20,157][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:20,160][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:20,163][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:20,197][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:20,200][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:20,202][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:20,219][mllm.models.large_language_model_local][WARNING] - Response Given the values, it seems that the optimal strategy is to try and secure the items with the highest personal value while also considering Bob's values to avoid overproposing which could lead to proportional allocation if the total exceeds the item quantity. Here, hats and balls have my highest value, and books have low value for both of us compared to their relative values in other categories. However, since Bob values books more highly, we need to be careful not to overpropose books. Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:20,222][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:20,225][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:20,227][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:20,230][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:20,233][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:20,242][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:20,245][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:20,248][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:20,251][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:20,253][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:20,256][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:20,268][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:20,270][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:20,273][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:20,275][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:20,278][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:20,280][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:20,283][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:20,285][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:20,288][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:20,290][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:20,293][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:20,295][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:20,298][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:20,300][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:20,303][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:20,305][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:20,308][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:20,310][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:20,313][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:20,326][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:20,328][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:20,330][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:20,333][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:20,335][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:20,337][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:20,339][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:20,340][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:20,343][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:20,345][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:20,347][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:20,349][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:20,354][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:20,356][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:20,367][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:20,370][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:20,372][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:20,388][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:20,393][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:20,396][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:20,398][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:20,410][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:20,413][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:20,415][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:20,417][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:20,420][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:20,431][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:20,434][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:20,437][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:20,439][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:20,455][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:20,457][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:20,460][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:20,475][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:20,556][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:20,638][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:20,640][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:20,643][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:20,646][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:20,715][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:20,788][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:20,791][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:20,794][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:20,800][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:20,806][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:20,813][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:20,819][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:20,825][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:20,837][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:20,843][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:20,850][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:20,861][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:20,867][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:20,873][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:20,883][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:20,886][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:20,890][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:20,895][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:20,900][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:20,909][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:20,913][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:20,918][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:20,922][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:20,924][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:20,927][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:20,935][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:20,938][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:20,942][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:20,946][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:20,950][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:20,954][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:20,960][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:20,964][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:20,968][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:20,972][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:20,975][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:20,979][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:20,982][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:20,986][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:20,990][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:20,996][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:20,999][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:21,003][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:21,006][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:21,009][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:21,012][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:21,018][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:21,021][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:21,024][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:21,028][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:21,031][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:21,034][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:21,037][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:21,040][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:21,043][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:21,046][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:21,049][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:21,052][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:21,057][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:21,060][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:21,067][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:21,070][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:21,073][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:21,076][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 balls, 10 books, 0 hats did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:21,100][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:21,103][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:21,106][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:21,109][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:21,147][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:21,150][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:21,153][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:21,156][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:21,160][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:21,286][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:21,289][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:21,346][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:21,405][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 books, 10 balls, 10 hats did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:21,493][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:21,496][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:21,662][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:21,751][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:22,682][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:22,931][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:22,934][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:23,012][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:23,015][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:23,018][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:23,020][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:23,046][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:23,125][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:23,128][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:23,131][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:23,211][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:23,214][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:23,216][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:23,298][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:23,302][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:23,388][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:23,391][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:23,394][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:23,439][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:23,443][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:23,446][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:23,478][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:23,481][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:23,483][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:23,503][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:23,506][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:23,509][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:23,530][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:23,533][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:23,535][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:23,538][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:23,541][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:23,544][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:23,546][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:23,550][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:23,552][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:23,555][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:23,578][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:23,581][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:23,584][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:23,586][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:23,589][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:23,592][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:23,595][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:23,597][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:23,621][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:23,624][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:23,626][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:23,629][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:23,632][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:23,635][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:23,638][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:23,640][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:23,643][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:23,652][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:23,654][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:23,656][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:23,658][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:23,658][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:23,659][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:23,660][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:23,665][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:23,667][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:23,669][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:23,676][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:23,679][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:23,701][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:23,703][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:23,706][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:23,708][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:23,743][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:23,745][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:23,747][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:23,748][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:23,750][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:23,752][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:23,851][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in our per-item values and Alice's strategy in previous rounds, it seems reasonable to assume that Alice will propose to take the items that she values more and leave the others to be split equally. Since both of us value hats and books similarly, and balls are highly valued by both of us, we should propose to take all the hats and balls and split the books. Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:24,027][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:24,032][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:24,035][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:24,038][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:24,040][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:24,073][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:24,075][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:24,078][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:24,080][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:24,082][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:24,137][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:24,140][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:24,143][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:24,221][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:24,224][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:24,227][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:24,230][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:24,232][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:24,304][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:24,308][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:24,310][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:24,388][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:24,391][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:24,394][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:24,471][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:24,474][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:24,477][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:24,480][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:24,482][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:24,485][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:24,487][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:24,490][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:24,493][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:24,495][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:24,553][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:24,556][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:24,559][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:24,561][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:24,564][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:24,566][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:24,569][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:24,572][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:24,635][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:24,638][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:24,640][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:24,643][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:24,646][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:24,716][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:24,719][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:24,722][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:24,725][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:24,727][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:24,730][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:24,732][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:24,735][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:24,737][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:24,797][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:24,800][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:24,802][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:24,805][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:24,807][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:24,810][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:24,813][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:24,962][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:25,038][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the situation and the equal per-item values that are likely to be similar, a fair and strategic proposal would be to suggest splitting the items evenly. This maximizes the potential points for both Alice and Bob without risking a proportional distribution if the total proposed exceeds the available items. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:25,045][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:25,048][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:25,197][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:25,276][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:25,280][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:25,283][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:25,518][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 balls, 0 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:25,603][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:25,687][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 books, 10 hats, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:25,690][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:26,229][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Alice place high value on books and low value on balls. To maximize your points, you should propose to take as many books and hats as possible, while Alice might take the balls. Here's the optimal proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:26,301][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:26,373][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:26,377][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:26,403][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric nature of this round and the fact that both you and Alice value hats and books relatively high, and balls relatively low, the optimal strategy involves splitting the items evenly to ensure both parties receive a fair share based on their high values for hats and books. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:26,407][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:26,410][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:26,412][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:26,419][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:26,422][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:26,424][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:26,427][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:26,491][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:26,565][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:26,568][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:26,570][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:26,572][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:26,652][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 books, 10 hats, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:26,655][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:26,746][mllm.models.large_language_model_local][WARNING] - Response Given the current per-item values, the optimal strategy would be to prioritize the books, as they have the highest value for both of us. Here's a revised proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:26,749][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:26,752][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:26,829][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:26,832][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:26,835][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:26,838][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:26,840][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:26,843][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:26,846][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:26,849][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:26,851][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:26,917][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:26,920][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:26,922][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:26,925][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:27,003][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric nature of the per-item values and the equal distribution of items, proposing to keep all the items remains a reasonable strategy. However, to ensure a fair split and maximize points, let's consider a slightly more strategic approach by valuing items that we hold at a higher perceived value. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:27,006][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:27,009][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:27,088][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:27,091][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:27,094][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:27,097][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:27,175][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:27,178][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:27,181][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:27,184][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:27,186][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:27,261][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:27,264][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:27,267][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:27,269][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:27,272][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:27,275][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:27,277][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:27,280][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:27,282][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:27,345][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:27,347][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:27,350][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:27,353][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:27,355][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:27,359][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:27,428][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:27,431][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:27,435][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:27,438][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:27,595][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:27,598][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:27,601][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:27,603][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:27,606][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:27,608][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 balls, 10 books, 0 hats did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:27,678][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:27,681][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:27,684][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:27,843][mllm.models.large_language_model_local][WARNING] - Response Given the high value of hats and books for both you and Alice, and the high value of balls for you, while Alice values them equally, the proposal remains competitive. Here is the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:27,930][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:27,935][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:27,939][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:27,942][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:27,944][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:28,007][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the high value that both you and Alice place on hats and books, and the low value on balls, a cooperative strategy might be beneficial. However, to ensure you get your fair share, you might want to mirror Alice's proposal or slightly adjust it to ensure a fair split while maximizing your points. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:28,009][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:28,012][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:28,013][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:28,087][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:28,089][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:29,223][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:29,226][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:29,229][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:29,235][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:29,239][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:29,257][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:29,260][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:29,266][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:29,268][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:29,271][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:29,273][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:29,276][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:29,278][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:29,281][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:29,284][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:29,286][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:29,288][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:29,291][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:29,342][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:29,347][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:29,428][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:29,430][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:29,432][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:29,435][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:29,438][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:29,440][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:29,443][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:29,521][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:29,524][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:29,528][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:29,530][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:29,533][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:29,536][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:29,620][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:29,623][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:29,625][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:29,628][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:29,631][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:29,634][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:29,636][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:29,639][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:29,707][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:29,710][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:29,799][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:29,802][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:29,805][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:29,891][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:29,894][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:29,896][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:29,900][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:29,902][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:29,905][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:29,970][mllm.models.large_language_model_local][WARNING] - Response Given that both you and Alice have similar valuations for hats and balls, but differing valuations for books, it might be strategic to focus on items where you have a significant advantage. Here’s a balanced proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:29,973][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:29,976][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:29,979][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:29,981][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:29,984][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:29,986][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:29,989][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:29,991][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:30,039][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:30,042][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:30,045][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:30,071][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:30,074][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:30,077][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:30,080][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:30,082][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:30,085][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:30,093][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:30,097][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:30,139][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:30,142][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:30,145][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:30,148][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:30,150][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:30,242][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:30,271][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:30,274][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:30,280][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:30,287][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:30,293][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:30,300][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:30,302][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:30,305][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:30,308][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:30,311][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:30,313][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:30,316][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:30,319][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:30,322][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:30,325][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:30,327][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:30,330][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:30,332][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:30,336][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:30,339][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:30,341][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:30,355][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:30,657][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:30,748][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:30,751][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:30,752][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:30,755][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:30,757][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:30,759][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:30,761][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:30,763][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:30,765][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:30,803][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:30,806][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:30,808][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:30,827][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:30,830][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:30,833][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:30,835][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:30,850][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:30,853][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:30,856][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:30,858][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:30,897][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:30,900][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:30,902][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:30,905][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:30,908][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:30,969][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:30,996][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:30,998][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:31,001][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:31,004][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:31,007][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:31,022][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:31,026][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:31,032][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:31,039][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:31,046][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:31,052][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:31,058][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:31,064][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:31,070][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:31,100][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:31,104][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:31,106][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:31,108][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:31,110][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:31,112][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:31,114][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:31,116][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:31,118][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:31,120][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:31,121][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:31,123][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:31,125][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:31,131][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:31,133][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:31,135][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:31,137][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:31,139][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:31,174][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:31,176][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:31,178][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:31,180][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:31,182][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:31,184][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:31,185][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:31,187][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:31,190][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:31,192][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:31,271][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:31,273][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:31,276][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:31,279][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:31,281][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:31,284][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:31,286][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:31,289][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:31,291][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:31,294][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:31,349][mllm.models.large_language_model_local][WARNING] - Response Given the high value of balls for both of us and the equal distribution of other items, a strategic split would be to maximize the value of balls while ensuring we both receive some hats and books. Here is a balanced proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:31,352][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:31,428][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:31,435][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:31,441][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:31,448][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:31,454][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:31,460][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:31,467][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:31,473][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:31,479][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:31,584][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:31,590][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:31,597][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:31,664][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:31,670][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:31,677][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:31,683][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:31,742][mllm.models.large_language_model_local][WARNING] - Response Given the new valuation, to maximize my points, I should prioritize the items where my value is higher compared to Alice's. Here, my value for books is 10 and for hats is 1, while for balls, it is 1. Similarly, Alice values books at 10 and balls at 10. To ensure I get more of the items I value higher, a strategic proposal would be to take as many of the items I value more highly (books and hats) as possible, while still proposing a fair share of the items Alice values more. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:31,745][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:31,752][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:31,758][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:31,764][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:31,902][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:31,909][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:31,915][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:31,921][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:31,928][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:32,136][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:32,216][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:32,218][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:32,221][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:32,224][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:32,226][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:32,294][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:32,456][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:32,459][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:32,462][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:32,465][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:32,527][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:32,605][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:32,611][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:32,618][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:32,624][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:32,631][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:32,683][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:32,762][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:32,842][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:32,922][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:33,003][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:33,006][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:33,009][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:33,085][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:33,088][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:33,166][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:33,169][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:33,172][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:33,174][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:33,249][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:33,252][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:33,254][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:33,346][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:33,349][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 books, 10 hats, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:33,352][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:33,355][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:33,357][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:33,408][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:33,411][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:33,430][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:33,455][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:33,458][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:33,460][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:33,463][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:33,477][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:33,480][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:33,483][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:33,485][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:33,488][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:33,525][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:33,661][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:33,810][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 books, 10 hats, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:33,816][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:33,821][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:33,879][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:33,885][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:33,961][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:33,964][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:33,967][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:33,969][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:33,972][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:33,975][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:33,978][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:33,980][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:33,983][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:33,986][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:33,988][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:33,991][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:33,994][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:33,997][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:34,016][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:34,021][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:34,027][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:34,032][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:34,038][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:34,043][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:34,048][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:34,054][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:34,059][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:34,064][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:34,069][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:34,074][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:34,078][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:34,083][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:34,099][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:34,103][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:34,106][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:34,110][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:34,114][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:34,163][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:34,167][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:34,244][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:34,250][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:34,325][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:34,331][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:34,338][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:34,344][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:34,412][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:34,419][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:34,425][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:34,681][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:34,684][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:34,686][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:34,689][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:34,692][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:34,694][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:34,697][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:34,700][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:34,702][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:34,756][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:34,759][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:34,762][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:34,765][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:34,767][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:34,782][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:34,807][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:34,809][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:34,813][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:34,816][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:34,881][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:34,884][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:34,887][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:34,889][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:34,908][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:34,911][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:35,102][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both you and Alice value balls the most. However, you value books more than hats, while Alice values books the least. To maximize your points, you should propose to take the majority of the balls while also securing a portion of the books. Here's a strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:35,178][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:35,181][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:35,187][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:35,189][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:35,195][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:35,276][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:35,277][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:35,278][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:35,280][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:35,281][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:35,282][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:35,283][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:35,285][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:35,286][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:35,348][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:35,441][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:35,445][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:35,447][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:35,534][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:35,538][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:35,560][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:35,563][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:35,566][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:35,583][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:35,586][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:35,588][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:35,591][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:35,605][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:35,608][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:35,628][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:35,631][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:35,634][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:35,694][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:35,696][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:35,759][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:35,780][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:35,783][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:35,786][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:35,804][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:35,807][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:35,831][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:35,834][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:35,836][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:35,839][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:35,842][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:35,844][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:35,875][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:35,878][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:35,880][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:35,897][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:35,900][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:35,903][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:35,906][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:35,908][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:35,911][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:35,913][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:35,916][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:35,919][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:35,921][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:35,923][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:35,926][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:35,928][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:35,930][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:35,938][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:35,941][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:35,944][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:35,947][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:35,950][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:35,953][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:35,956][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:35,959][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:35,962][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:35,964][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:35,967][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:35,973][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:35,978][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:35,984][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:35,990][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:35,995][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:36,000][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:36,006][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:36,014][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:36,018][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:36,023][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:36,034][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:36,040][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:36,066][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:36,071][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:36,076][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:36,084][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:36,089][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:36,094][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:36,099][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:36,107][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:36,111][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:36,127][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:36,130][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:36,132][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:36,188][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:36,191][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:36,242][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:36,259][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:36,263][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:36,265][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:36,278][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:36,281][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:36,297][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:36,300][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:36,306][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:36,313][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:36,319][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:36,325][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:36,339][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:36,341][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:36,344][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:36,346][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:36,349][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:36,351][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:36,363][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:36,366][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:36,368][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:36,371][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:36,373][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:36,375][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:36,378][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:36,380][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:36,383][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:36,385][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:36,388][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:36,394][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:36,397][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:36,400][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:36,402][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:36,404][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:36,407][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:36,409][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:36,412][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:36,414][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:36,416][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:37,004][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:37,233][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:37,292][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:37,298][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:37,304][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:37,378][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:37,384][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:37,464][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:37,471][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:37,477][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:37,503][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:37,509][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:37,525][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:37,571][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:37,577][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:37,584][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:37,590][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:37,597][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:37,616][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:37,621][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:37,627][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:37,633][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:37,638][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:37,644][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:37,649][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:37,662][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:37,667][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:37,672][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:37,676][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:37,681][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:37,685][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:37,690][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:37,694][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:37,698][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:37,703][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:37,707][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:37,711][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:37,715][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:37,720][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:37,724][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:37,726][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:37,728][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:37,730][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:37,732][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:37,733][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:37,735][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:37,737][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:37,739][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:37,741][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:37,743][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:37,745][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:37,747][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:37,749][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:37,751][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:37,753][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:37,763][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:37,765][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:37,767][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:37,769][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:37,771][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:37,773][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:37,774][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:37,931][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:37,934][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:38,054][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:38,056][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:38,156][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:38,159][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:38,162][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:38,165][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:38,168][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:38,187][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:38,189][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:38,192][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:38,194][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:38,197][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:38,200][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:38,202][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:38,205][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:38,207][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:38,210][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:38,213][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:38,216][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:38,219][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:38,221][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:38,224][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:38,227][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:38,229][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:38,232][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:38,235][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:38,237][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:38,263][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:38,266][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:38,295][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:38,298][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:38,310][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:38,313][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:38,343][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:38,346][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:38,385][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:38,400][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:38,403][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:38,406][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:38,417][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:38,448][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:38,451][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:38,463][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:38,476][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:38,483][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:38,489][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:38,492][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:38,498][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:38,504][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:38,511][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:38,517][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:38,523][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:38,530][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:38,540][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:38,545][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:38,551][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:38,556][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:38,561][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:38,567][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:38,569][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:38,574][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:38,579][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:38,584][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:38,589][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:38,594][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:38,599][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:38,601][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:38,609][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:38,611][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:38,613][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:38,617][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:38,621][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:38,623][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:38,652][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:38,654][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:38,707][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:38,723][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:38,726][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:38,732][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:38,741][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:38,770][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:38,773][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:38,816][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:38,819][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:38,872][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:38,897][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:38,904][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:38,910][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:38,917][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:38,929][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:38,935][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:38,941][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:38,947][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:38,970][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:39,003][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:39,017][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:39,045][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:39,051][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:39,089][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:39,096][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:39,153][mllm.models.large_language_model_local][WARNING] - Response Given the values, you and Alice both have high value for books and low value for hats and balls. To maximize your points, it's important to recognize the importance of books and slightly less for hats and balls. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:39,181][mllm.models.large_language_model_local][WARNING] - Response Given Alice's per-item values (hats=1, books=1, balls=10) and Bob's per-item values (hats=1, books=10, balls=1), the optimal strategy would be to ensure that Alice receives the items with higher value to her. Proposing to take all the balls and splitting the hats and books proportionally might be a balanced strategy. Proposal: 10 hats, 10 books, 10 balls However, since the total proposed amount exceeds the available items, the allocation will be proportional. Given Alice's high value for balls, it's crucial to secure as many balls as possible. Here's a refined proposal: Proposal: 5 hats, 5 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:39,244][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:39,247][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:39,294][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:39,297][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:39,416][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:39,429][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:39,436][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:39,439][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:39,491][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:39,494][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:39,540][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:39,543][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:39,599][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:39,612][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:39,659][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:39,672][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:39,688][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:39,691][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:39,777][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:39,784][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:39,912][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:39,919][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:39,973][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:39,986][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:40,105][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:40,117][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:40,191][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:40,718][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:48:40,888][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:48:41,060][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:48:42,082][__main__][INFO] - Number of regex retries in iteration 561: 2030 [2025-11-13 06:48:42,082][__main__][INFO] - agents played in iteration 561 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 06:48:42,921][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:48:42,943][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:48:42,966][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:48:42,987][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:48:42,987][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 06:48:42,988][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 06:48:43,730][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 06:48:44,175][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 06:48:44,668][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 06:48:45,165][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 06:48:45,655][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 06:48:46,143][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 06:48:46,633][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 06:48:47,122][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 06:48:47,612][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 06:48:48,102][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 06:48:48,589][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 06:48:49,097][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 06:48:49,584][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 06:48:50,071][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 06:48:50,565][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 06:48:51,052][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 06:48:51,539][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 06:48:52,030][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 06:48:52,522][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 06:48:53,013][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 06:48:53,502][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 06:48:53,994][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 06:48:54,484][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 06:48:54,976][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 06:48:55,470][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 06:48:55,999][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 06:48:56,487][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 06:48:56,986][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 06:48:57,484][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 06:48:57,978][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 06:48:58,474][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 06:48:58,970][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 06:48:59,482][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 06:48:59,977][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 06:49:00,476][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 06:49:00,975][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 06:49:01,473][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 06:49:01,980][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 06:49:02,479][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 06:49:02,982][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 06:49:03,482][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 06:49:03,987][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 06:49:04,488][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 06:49:04,982][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 06:49:05,477][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 06:49:05,996][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 06:49:06,491][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 06:49:06,991][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 06:49:07,493][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 06:49:07,992][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 06:49:08,494][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 06:49:08,992][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 06:49:09,488][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 06:49:09,997][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 06:49:10,493][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 06:49:10,990][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 06:49:11,490][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 06:49:11,989][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 06:49:12,493][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 06:49:12,991][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 06:49:13,488][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 06:49:14,004][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 06:49:14,503][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 06:49:14,993][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 06:49:15,485][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10789 tokens. [2025-11-13 06:49:16,279][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.19%, Current % of VRAM taken: 59.64%, Block Peak % of device VRAM: 62.31%, ΔTime: 00:00:32 [2025-11-13 06:49:17,012][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 06:49:17,013][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 06:49:17,015][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 06:49:18,018][__main__][INFO] - Iteration 562 took 1m 27s (59.07% Gen, 39.79% Train). Generation: 51s, Training: 34s. Estimated remaining time: 64h 30m 34s. Estimated total time: 73h 10m 10s. Time estimates for 10 more iterations: 14m 38s, 100 more iterations: 2h 26m 20s, 500 more iterations: 12h 11m 41s. [2025-11-13 06:49:18,020][__main__][INFO] - Starting iteration 562. [2025-11-13 06:49:18,491][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 56 and human policies 1. [2025-11-13 06:49:18,492][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 06:49:37,539][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:37,563][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:37,565][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:37,568][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:37,571][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:37,573][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:37,576][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:37,578][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:37,580][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:37,591][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:37,593][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:37,596][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:37,598][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:37,601][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:37,603][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:37,606][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:37,609][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:37,611][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:37,613][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:37,616][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:37,618][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:37,621][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:37,630][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:37,632][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:37,635][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:37,637][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:37,639][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:37,642][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:37,644][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:37,646][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:37,648][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:37,650][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:37,653][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:37,655][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:37,657][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:37,659][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:37,661][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:37,676][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:37,678][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:37,679][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:37,681][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:37,683][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:37,685][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:37,687][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:37,689][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:37,690][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:37,692][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:37,694][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:37,696][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:37,698][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:37,699][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:37,701][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:37,703][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:37,705][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:37,707][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:37,709][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:37,710][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:37,711][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:37,714][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:37,716][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:37,717][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:37,719][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:37,720][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:37,722][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:37,724][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:37,725][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:37,727][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:37,729][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:37,730][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:37,732][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:37,734][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:37,735][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:37,737][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:37,739][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:37,740][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:37,742][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:37,743][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:37,745][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:37,747][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:37,748][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:37,750][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:37,753][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:37,755][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:37,756][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:37,758][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:37,872][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:37,890][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:37,892][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:37,895][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:37,897][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:37,899][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:37,915][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:37,917][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:37,919][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:37,922][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:37,924][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:37,926][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:37,928][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:37,930][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:37,936][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:37,939][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:37,941][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:37,943][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:37,945][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:37,948][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:37,954][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:37,957][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:37,959][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:37,961][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:37,963][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:37,965][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:37,967][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:37,986][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:37,989][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:37,992][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:37,995][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:37,997][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:38,999][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:38,002][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:38,009][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:38,020][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:38,022][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:38,025][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:38,027][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:38,037][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:38,039][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:38,042][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:38,044][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:38,047][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:38,049][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:38,051][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:38,058][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:38,061][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:38,063][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:38,065][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:38,067][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:38,070][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:38,072][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:38,078][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:38,080][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:38,082][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:38,084][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:38,086][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:38,088][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:38,090][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:38,096][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:38,098][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:38,101][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:38,103][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:38,105][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:38,107][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:38,109][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:38,120][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:38,121][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:38,123][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:38,125][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:38,126][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:38,128][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:38,130][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:38,138][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:38,140][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:38,141][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:38,143][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:38,145][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:38,147][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:38,235][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:38,305][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:38,311][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:38,315][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:38,322][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:38,327][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:38,332][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:38,935][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:39,196][mllm.models.large_language_model_local][WARNING] - Response Proposal: 5 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:39,199][mllm.models.large_language_model_local][WARNING] - Response Proposal: 5 hats, 5 books, 5 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:39,225][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:39,255][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:39,293][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:39,295][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:39,298][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:39,301][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:39,303][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:39,306][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:39,308][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:39,338][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:39,341][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:39,343][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:39,344][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:39,346][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:39,348][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:39,350][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:39,352][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:39,415][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:39,418][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:39,420][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:39,422][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:39,425][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:39,427][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:39,429][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:39,431][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:39,434][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:39,497][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:39,500][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:39,503][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:39,506][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:39,508][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:39,511][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:39,513][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:39,570][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:39,573][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:39,576][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:39,578][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:39,581][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:39,584][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:39,586][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:39,589][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:39,591][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:39,622][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:39,624][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:39,627][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:39,630][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:39,632][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:39,635][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:39,637][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:39,639][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:39,642][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:39,644][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:39,679][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:39,682][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:39,684][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:39,687][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:39,689][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:39,691][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:39,694][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:39,696][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:39,699][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:39,701][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:39,704][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:39,706][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:39,709][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:39,711][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:39,713][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:39,740][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:39,742][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:39,819][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:39,822][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:39,825][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:39,827][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:39,829][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:39,854][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:39,857][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:39,898][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:39,901][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:39,903][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:39,905][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:39,908][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:39,916][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:39,918][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:39,973][mllm.models.large_language_model_local][WARNING] - Response Proposal: 5 hats, 10 books, 5 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:39,992][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:39,995][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:40,040][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:40,042][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:40,073][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:40,104][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:40,142][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:40,212][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:40,213][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:40,215][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:40,775][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:41,236][mllm.models.large_language_model_local][WARNING] - Response Proposal: 5 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:41,279][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:41,281][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:41,324][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:41,326][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:41,370][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:41,373][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:41,376][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:41,378][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:41,381][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:41,384][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:41,387][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:41,389][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:41,391][mllm.models.large_language_model_local][WARNING] - Response Proposal: 5 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:41,432][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:41,435][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:41,438][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:41,441][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:41,444][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:41,446][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:41,448][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:41,517][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:41,520][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:41,523][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:41,525][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:41,527][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:41,600][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:41,603][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:41,606][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:41,609][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:41,611][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:41,613][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:41,669][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:41,671][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:41,674][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:41,676][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:41,679][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:41,682][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:41,750][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:41,752][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:41,755][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:41,757][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:41,760][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:41,762][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:41,764][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:41,767][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:41,769][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:41,772][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:41,774][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:41,776][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:41,830][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:41,833][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:41,836][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:41,838][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:41,911][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:41,915][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:41,917][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:41,920][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:41,922][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:41,991][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:41,994][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:41,997][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:42,999][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:42,002][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:42,005][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:42,007][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:42,009][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:42,012][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:42,070][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:42,072][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:42,075][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:42,078][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:42,149][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:42,152][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:42,154][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:42,157][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:42,159][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:42,162][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:42,164][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:42,166][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:42,170][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:42,173][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:42,175][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:42,226][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:42,229][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:42,231][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:42,234][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:42,236][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:42,303][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:42,380][mllm.models.large_language_model_local][WARNING] - Response Proposal: 5 hats, 10 books, 5 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:42,461][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:42,489][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:42,507][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:42,510][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:42,512][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:42,538][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:43,526][mllm.models.large_language_model_local][WARNING] - Response Proposal: 5 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:43,592][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:43,595][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:43,597][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:43,599][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:43,601][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:43,603][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:43,604][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:43,675][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:43,768][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:43,771][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:43,825][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:43,928][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:43,931][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:43,934][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:43,937][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:43,940][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:43,942][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:43,945][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:43,973][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:43,976][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:43,978][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:43,981][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:43,983][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:44,048][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:44,050][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:44,054][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:44,056][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:44,059][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:44,061][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:44,064][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:44,102][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:44,105][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:44,108][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:44,111][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:44,114][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:44,157][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:44,160][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:44,163][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:44,165][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:44,212][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:44,215][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:44,218][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:44,221][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:44,264][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:44,267][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:44,269][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:44,272][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:44,274][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:44,276][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:44,279][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:44,282][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:44,284][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:44,318][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:44,321][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:44,323][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:44,326][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:44,396][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:44,399][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:44,402][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:44,405][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:44,447][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:44,450][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:44,452][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:44,455][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:44,457][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:44,460][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:44,463][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:44,498][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:44,501][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:44,504][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:44,575][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:44,578][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:44,580][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:44,583][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:44,585][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:44,617][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:44,619][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:44,622][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:44,624][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:44,627][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:44,630][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:44,632][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:44,666][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:44,726][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:44,728][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:44,730][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:44,732][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:44,734][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:44,736][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:44,741][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:44,743][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:44,803][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:44,809][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:44,812][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:44,885][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:44,888][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:44,891][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:44,926][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:45,924][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:45,927][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:45,930][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:45,932][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:45,935][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:46,013][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:46,082][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:46,084][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:46,086][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:46,155][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:46,158][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:46,160][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:46,198][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:46,245][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:46,249][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:46,251][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:46,254][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:46,275][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:46,278][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:46,281][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:46,283][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:46,286][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:46,296][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:46,297][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:46,298][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:46,299][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:46,300][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:46,302][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:46,319][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:46,321][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:46,322][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:46,323][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:46,325][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:46,326][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:46,343][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:46,345][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:46,346][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:46,348][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:46,349][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:46,368][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:46,369][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:46,371][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:46,390][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:46,392][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:46,393][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:46,395][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:46,397][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:46,398][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:46,400][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:46,411][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:46,413][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:46,415][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:46,417][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:46,419][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:46,420][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:46,422][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:46,434][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:46,436][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:46,438][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:46,441][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:46,456][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:46,458][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:46,461][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:46,463][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:46,465][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:46,468][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:46,470][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:46,472][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:46,475][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:46,481][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:46,484][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:46,486][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:46,488][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:46,503][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:46,505][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:46,508][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:46,510][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:46,513][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:46,516][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:46,518][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:46,520][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:46,523][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:46,525][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:46,532][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:46,535][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:46,537][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:46,539][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:46,541][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:46,543][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:46,545][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:46,557][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:46,559][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:46,561][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:46,563][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:46,565][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:46,567][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:46,576][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:46,578][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:46,581][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:46,583][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:46,652][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:46,657][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:46,663][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:46,733][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:46,884][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:46,887][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:46,890][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:46,892][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:46,895][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:46,897][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:46,962][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:46,965][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:46,967][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:46,970][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:46,972][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:47,040][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:47,043][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:47,045][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:47,047][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:47,050][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:47,053][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:47,055][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:47,117][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:47,120][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:47,122][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:47,125][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:47,195][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:47,198][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:47,200][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:47,203][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:47,272][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:47,275][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:47,277][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:47,280][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:47,350][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:47,352][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:47,355][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:47,358][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:47,360][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:47,363][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:47,427][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:47,429][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:47,432][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:47,434][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:47,437][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:47,439][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:47,442][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:47,504][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:47,507][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:47,510][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:47,581][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:47,584][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:47,587][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:47,589][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:47,591][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:47,594][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:47,596][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:47,599][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:47,658][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:47,661][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:47,663][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:47,666][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:47,735][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:47,738][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:47,740][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:47,743][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:47,745][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:47,748][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:47,751][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:47,812][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:47,815][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:47,818][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:47,820][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:47,823][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:47,825][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:47,831][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:47,837][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:47,844][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:47,894][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:47,897][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:48,200][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:49,399][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:49,406][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:49,412][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:49,419][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:49,425][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:49,456][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:49,459][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:49,464][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:49,469][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:49,474][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:49,480][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:49,486][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:49,491][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:49,496][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:49,501][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:49,506][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:49,511][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:49,516][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:49,520][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:49,525][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:49,530][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:49,534][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:49,538][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:49,543][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:49,547][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:49,551][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:49,556][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:49,560][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:49,562][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:49,564][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:49,565][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:49,585][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:49,587][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:49,588][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:49,590][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:49,591][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:49,593][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:49,594][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:49,596][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:49,618][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:49,620][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:49,622][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:49,623][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:49,625][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:49,627][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:49,700][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:49,702][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:49,704][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:49,707][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:49,785][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:49,788][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:49,791][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:49,794][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:49,796][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:49,799][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:49,801][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:49,866][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:49,869][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:49,946][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:49,949][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:50,025][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:50,105][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, I should prioritize the items where the benefit is higher for me. Since I value hats and books significantly higher than balls, and Alice values balls more, I should propose to take more hats and books while giving Alice the balls. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:50,107][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:50,110][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:50,112][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:50,115][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:50,184][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:50,263][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:50,266][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:50,343][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:50,346][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:50,349][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:50,429][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:50,432][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:50,435][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:50,518][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:50,520][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:50,603][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:50,606][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:50,679][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:50,699][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:50,701][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:50,704][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:50,762][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:50,765][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:50,793][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:50,935][mllm.models.large_language_model_local][WARNING] - Response Given the new values where you have a higher valuation for balls (10) compared to hats and books (1 each), and Bob values hats the most, it makes sense to allocate more balls to you to maximize your points. Here's the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:51,201][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:51,203][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:51,206][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:51,207][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:51,247][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:51,249][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:51,330][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:51,332][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:51,334][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:51,336][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:51,339][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:51,341][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:51,343][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:51,345][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:51,350][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:51,352][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:51,355][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:51,357][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:51,359][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:51,361][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:51,364][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:51,366][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:51,368][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:51,370][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:51,372][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:51,374][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:51,376][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:51,379][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:51,381][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:51,383][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:51,385][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:51,387][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:51,389][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:51,392][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:51,394][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:51,396][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:51,398][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:51,404][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:51,406][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:51,408][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:51,411][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:51,413][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:51,415][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:51,417][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:51,419][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:51,421][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:51,423][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:51,426][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:51,428][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:51,430][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:51,434][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:51,437][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:51,486][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:51,567][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:51,570][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:51,572][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:51,716][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:51,719][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:51,721][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:51,743][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:51,746][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:51,792][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:51,795][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:51,797][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:51,813][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 balls, 10 books, 10 hats did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:51,820][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:51,823][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:51,837][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:51,840][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:51,843][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:51,856][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:51,858][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:51,861][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:51,863][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:51,877][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:51,880][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:51,900][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:51,903][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:51,906][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:51,922][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:51,925][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:51,928][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:51,930][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:51,933][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:51,935][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:52,024][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:52,027][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:52,044][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:52,124][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:52,127][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:52,129][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:52,132][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:52,135][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:52,137][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:52,140][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:52,146][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:52,152][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:52,159][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:52,165][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:52,171][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:52,177][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:52,184][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:52,190][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:52,196][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:52,202][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:52,207][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:52,213][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:52,218][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:52,223][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:52,229][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:52,234][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:52,239][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:52,243][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:52,248][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:52,252][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:52,257][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:52,261][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:52,266][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:52,270][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:52,275][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:52,280][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:52,284][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:52,288][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:52,292][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:52,296][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:52,300][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:52,304][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:52,308][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:52,311][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:52,315][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:52,319][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:52,323][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:52,326][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:52,330][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:52,333][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:52,337][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:52,341][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:52,344][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:52,360][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:52,363][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:52,366][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:52,369][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:52,446][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:52,605][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:52,608][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:52,610][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:52,686][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:52,689][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:52,765][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:52,768][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:52,771][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:52,845][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:52,848][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:52,851][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:52,854][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:52,856][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:52,929][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:52,932][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:53,019][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:53,022][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:53,025][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:53,111][mllm.models.large_language_model_local][WARNING] - Response Given theper-item values for this round, I should try to capture more value where my valuation is higher. Here are the per-item values: - Hats: 10 (mine) vs 10 (Alice's) - Books: 10 (mine) vs 1 (Alice's) - Balls: 1 (mine) vs 10 (Alice's) To maximize my points, I should propose to take most of the hats and books, and a minimal amount of balls. Here is my proposal: Proposal: 10 hats, 10 books, 1 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:53,114][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:53,116][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:53,119][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:53,121][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:53,124][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:53,126][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:53,448][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:56,020][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:56,029][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:56,083][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:56,087][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:56,094][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:56,101][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:56,128][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:56,131][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:56,133][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:56,135][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:56,138][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:56,141][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:56,143][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:56,145][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:56,148][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:56,149][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:56,151][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:56,153][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:56,155][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:56,158][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:56,161][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:56,163][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:56,166][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:56,169][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:56,171][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:56,174][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:56,176][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:56,178][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:56,179][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:56,181][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:56,183][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:56,186][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:56,188][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:56,190][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:56,192][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:56,202][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:56,203][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:56,204][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:56,206][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:56,207][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:56,255][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:56,257][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:56,258][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:56,259][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:56,260][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:56,261][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:56,263][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:56,352][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:56,355][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:56,358][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:56,362][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:56,366][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:56,369][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:56,371][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:56,437][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:56,441][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:56,445][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:56,447][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:56,518][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:56,520][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:56,522][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:56,525][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:56,528][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:56,531][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:56,611][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:56,614][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:56,618][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:56,621][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:56,690][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:56,693][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:56,697][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:56,774][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:56,778][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:56,855][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:56,933][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:56,937][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:56,940][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:57,014][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:57,017][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:57,020][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:57,189][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:57,191][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:57,273][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:57,277][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:57,280][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:57,359][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:57,362][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:57,363][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:57,364][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:57,553][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:57,557][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:57,634][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:57,638][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:57,641][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:58,509][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:58,545][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:58,547][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:58,627][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:58,631][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:58,634][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:58,637][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:58,639][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:58,642][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:58,645][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:58,647][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:58,649][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:58,650][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:58,652][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:58,653][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:58,655][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:58,657][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:58,658][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:58,660][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:58,661][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:58,663][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:58,665][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:58,668][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:58,670][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:58,671][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:58,673][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:58,675][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:58,676][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:58,677][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:58,679][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:58,680][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:58,682][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:58,684][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:58,685][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:58,687][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:58,689][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:58,691][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:58,693][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:58,695][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:58,698][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:58,699][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:58,700][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:58,702][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:58,704][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:58,705][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:58,707][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:58,709][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:58,710][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:58,712][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:58,712][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:58,713][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:58,713][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:58,718][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:59,002][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:59,038][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:59,099][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:59,185][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:59,188][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:59,192][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:59,195][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:59,199][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:59,202][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:59,317][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:59,320][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:59,323][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:59,343][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:59,347][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:59,350][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:59,352][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:59,391][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:59,394][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:59,438][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:59,442][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:59,481][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:59,484][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:59,487][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:59,489][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:59,525][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:59,532][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:59,599][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:59,603][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:59,607][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:59,611][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:59,614][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:59,618][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:59,768][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:59,772][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:59,775][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:59,778][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:49:59,781][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:59,789][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:59,795][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:59,799][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:59,801][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:59,804][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:59,806][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:59,833][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:59,838][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:59,842][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:59,846][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:59,849][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:59,852][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:59,854][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:59,857][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:59,860][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:59,862][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:59,865][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:59,867][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:59,870][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:59,872][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:59,875][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:59,878][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:59,880][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:59,883][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:59,885][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:59,888][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:59,890][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:59,893][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:59,895][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:59,897][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:59,900][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:59,902][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:59,904][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:59,907][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:59,908][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:59,910][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:59,912][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:59,914][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:59,916][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:59,918][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:59,920][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:59,923][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:59,925][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:49:59,927][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:59,945][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:49:59,947][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:00,028][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:00,029][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:00,032][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:00,111][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:00,355][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:00,359][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:00,362][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:00,365][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:02,843][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it appears that Alice values books more and I value hats and balls more. To maximize my points, I should focus on the items I value more. Proposal: 10 hats, 10 books, 1 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:03,480][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:03,566][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:03,729][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:03,731][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:03,737][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:03,741][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:03,839][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:03,842][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:03,846][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:03,849][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:03,852][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:03,856][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:03,933][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:03,937][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:03,940][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:03,943][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:03,945][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:03,947][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:03,950][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:04,019][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:04,022][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:04,025][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:04,027][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:04,030][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:04,031][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:04,033][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:04,036][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:04,038][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:04,040][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:04,043][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:04,113][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:04,117][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:04,120][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:04,205][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:04,209][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:04,210][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:04,212][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:04,214][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:04,215][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:04,271][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:04,273][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:04,275][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:04,276][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:04,277][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:04,278][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:04,279][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:04,364][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:04,365][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:04,367][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:04,367][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:04,369][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:04,370][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:04,455][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:04,456][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:04,458][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:04,460][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:04,461][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:04,549][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:04,552][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:04,554][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:04,556][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:04,557][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:04,559][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:04,636][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:04,639][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:04,641][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:04,643][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:04,644][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:04,723][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:04,726][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:04,729][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:04,730][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:04,816][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:04,819][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:04,822][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:04,824][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:04,826][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:04,910][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:04,914][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:04,917][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:05,084][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:05,086][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:05,087][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:05,089][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:05,171][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:05,257][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:05,258][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:05,261][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:05,262][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:05,346][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:05,349][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:05,351][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:05,353][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:05,355][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:05,358][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:05,359][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:05,362][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:05,696][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:07,683][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:07,688][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:07,690][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:07,734][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:07,736][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:07,738][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:07,740][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:07,741][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:07,742][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:07,744][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:07,745][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:07,746][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:07,747][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:07,748][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:07,748][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:07,749][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:07,749][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:07,751][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:07,753][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:07,754][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:07,756][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:07,757][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:07,758][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:08,010][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:08,012][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:08,013][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:08,014][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:08,015][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:08,080][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:08,082][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:08,083][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:08,084][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:08,085][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:08,122][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:08,124][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:08,125][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:08,126][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:08,128][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:08,130][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:08,131][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:08,133][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:08,134][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:08,135][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:08,136][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:08,152][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:08,153][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:08,155][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:08,156][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:08,183][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:08,184][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:08,185][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:08,187][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:08,188][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:08,188][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:08,189][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:08,210][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:08,212][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:08,214][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:08,215][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:08,217][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:08,218][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:08,219][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:08,239][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:08,240][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:08,241][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:08,243][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:08,244][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:08,246][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:08,265][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:08,266][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:08,267][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:08,269][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:08,270][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:08,290][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:08,292][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:08,293][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:08,294][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:08,313][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:08,314][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:08,341][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:08,343][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:08,366][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:08,369][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:08,370][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:08,451][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:08,453][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:08,455][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:08,458][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:08,461][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:08,463][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:08,466][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:08,468][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:08,471][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:08,473][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:08,475][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:08,477][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:08,480][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:08,482][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:08,484][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:08,486][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:08,488][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:08,490][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:08,492][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:08,494][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:08,536][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:08,537][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:08,539][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:08,540][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:08,712][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:08,715][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:08,880][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:08,883][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:08,885][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:08,964][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:08,967][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:08,968][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:08,969][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:08,972][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:08,975][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:09,053][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:09,055][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:09,057][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:09,059][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:09,062][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:09,144][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:09,146][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:09,148][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:09,150][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:09,152][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:09,154][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:09,156][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:09,158][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:09,160][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:09,161][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:09,235][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:09,239][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:09,241][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:09,242][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:09,322][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:09,323][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:09,325][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:09,327][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:09,328][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:09,330][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:09,331][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:09,413][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:09,416][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:09,418][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:09,421][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:09,423][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:09,424][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:09,425][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:09,426][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:09,501][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:09,503][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:09,505][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:09,507][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:09,509][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:09,511][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:09,592][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:09,594][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:09,596][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:09,597][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:09,678][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:09,680][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:09,681][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:09,683][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:09,762][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:09,850][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:09,852][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:10,501][mllm.models.large_language_model_local][WARNING] - Response Given Bob's valuation of books and balls, and your own high valuation of books, it makes sense to maximize the distribution of books and balls. Here is a strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:15,591][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:15,594][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:15,921][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:15,925][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:16,007][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:16,011][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:16,014][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:16,177][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:16,256][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:16,342][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:16,345][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:16,347][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:16,350][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:16,353][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:16,356][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:16,427][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:16,431][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:16,434][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:16,438][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:16,441][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:16,445][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:16,595][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:16,597][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:16,679][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:16,683][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:16,771][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:16,855][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:16,938][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:17,023][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:17,026][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:17,030][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:17,033][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:17,037][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:17,040][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:17,105][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:17,108][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:17,111][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:17,191][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:17,194][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:17,352][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:17,356][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:17,359][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:17,434][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:17,437][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:17,439][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:17,441][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:17,442][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:17,444][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:17,447][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:17,449][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:17,452][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:17,456][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:17,536][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:17,539][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:17,619][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:17,623][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:17,782][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:17,786][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:17,862][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:17,865][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:17,869][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:18,028][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:18,032][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:18,035][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:18,039][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:18,042][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:18,116][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:18,117][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:18,212][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:18,292][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:18,451][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:18,610][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:18,688][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:18,766][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:18,925][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:18,928][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:19,048][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:19,068][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:19,111][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:19,169][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:19,172][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:19,250][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:19,269][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:19,308][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:19,310][mllm.models.large_language_model_local][WARNING] - Response Proposal: 5 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:19,312][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:19,315][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:19,333][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:19,335][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:19,338][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:19,340][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:19,342][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:19,343][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:19,345][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:19,347][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:19,349][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:19,353][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:19,355][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:19,356][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:19,358][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:19,360][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:19,361][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:19,363][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:19,365][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:19,367][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:19,369][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:19,371][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:19,373][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:19,375][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:19,377][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:19,379][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:19,381][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:19,382][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:19,384][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:19,386][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:19,388][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:19,390][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:19,392][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:19,394][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:19,396][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:19,397][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:19,399][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:19,401][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:19,403][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:19,405][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:19,407][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:19,409][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:19,410][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:19,412][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:19,413][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:19,414][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:19,416][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:19,418][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:19,420][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:19,421][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:19,424][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:19,426][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:19,428][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:19,430][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:19,432][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:19,434][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:19,436][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:19,438][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:19,441][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:19,443][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:19,445][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:19,447][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:19,448][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:19,450][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:19,452][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:19,454][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:19,455][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:19,457][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:19,459][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:19,462][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:19,463][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:20,226][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:20,246][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:20,286][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:20,338][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:20,340][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:20,398][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:20,435][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:20,453][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:20,472][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:20,479][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:20,486][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:20,497][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:20,500][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:20,506][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:20,527][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:20,533][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:20,539][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:20,546][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:20,552][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:20,558][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:20,564][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:20,570][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:20,575][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:20,580][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:20,586][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:20,591][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:20,596][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:20,601][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:20,606][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:20,611][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:20,615][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:20,617][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:20,620][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:20,622][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:20,624][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:20,628][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:20,634][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:20,639][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:20,643][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:20,646][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:20,650][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:20,654][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:20,657][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:20,661][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:20,665][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:20,668][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:20,672][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:20,675][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:20,679][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:20,682][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:20,686][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:20,689][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:20,692][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:20,696][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:20,699][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:20,702][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:20,705][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:20,709][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:20,712][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:20,715][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:20,718][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:20,721][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:20,724][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:20,727][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:20,731][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:20,734][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:20,737][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:20,740][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:20,743][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:20,746][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:20,748][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:20,752][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:20,754][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:20,757][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:20,760][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:20,763][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:20,766][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:20,769][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:20,780][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:20,783][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:21,040][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:21,046][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:21,307][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:21,480][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:21,674][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:22,090][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:22,405][mllm.models.large_language_model_local][WARNING] - Response Given the values, proposing an even split might not be optimal as it allows Bob to grab more of the items he values highly. Instead, we should aim to grab as much of the high-value items (books and balls) as possible. Here's a strategic proposal: Proposal: 10 hats, 10 books, 1 ball did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:22,504][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:22,508][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:22,524][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:22,527][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:22,604][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:22,608][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:22,611][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:22,614][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:22,687][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:22,690][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:22,693][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:22,768][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:22,771][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:22,774][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:22,777][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:22,848][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it seems optimal to propose the full allocation of items to maximize the points, as both you and Bob value hats and books highly, and balls less. Here's the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:23,002][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:23,161][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:23,164][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:23,167][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:23,170][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:23,173][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:23,176][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:23,178][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:23,248][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:23,251][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:23,254][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:23,257][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:23,260][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:23,263][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:25,122][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:25,132][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:50:25,294][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:25,305][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:50:25,466][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:25,476][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:50:26,617][__main__][INFO] - Number of regex retries in iteration 562: 1689 [2025-11-13 06:50:26,618][__main__][INFO] - agents played in iteration 562 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 06:50:27,527][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:50:27,547][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:50:27,567][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:50:27,587][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:50:27,588][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 06:50:27,588][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 06:50:28,318][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 06:50:28,767][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 06:50:29,265][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 06:50:29,758][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 06:50:30,248][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 06:50:30,742][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 06:50:31,236][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 06:50:31,725][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 06:50:32,215][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 06:50:32,724][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 06:50:33,211][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 06:50:33,700][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 06:50:34,200][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 06:50:34,689][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 06:50:35,177][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 06:50:35,663][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 06:50:36,148][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 06:50:36,637][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 06:50:37,123][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 06:50:37,609][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 06:50:38,100][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 06:50:38,587][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 06:50:39,073][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 06:50:39,560][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 06:50:40,049][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 06:50:40,540][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 06:50:41,028][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 06:50:41,517][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 06:50:42,020][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 06:50:42,506][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 06:50:42,995][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 06:50:43,484][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 06:50:43,974][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 06:50:44,464][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 06:50:44,951][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 06:50:45,441][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 06:50:45,933][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 06:50:46,423][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 06:50:46,910][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 06:50:47,402][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 06:50:47,891][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 06:50:48,381][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 06:50:48,873][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 06:50:49,362][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 06:50:49,850][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 06:50:50,339][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 06:50:50,827][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 06:50:51,329][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 06:50:51,817][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 06:50:52,304][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 06:50:52,797][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 06:50:53,285][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 06:50:53,774][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 06:50:54,262][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 06:50:54,750][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 06:50:55,240][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 06:50:55,729][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 06:50:56,218][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 06:50:56,708][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 06:50:57,198][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 06:50:57,687][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 06:50:58,179][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 06:50:58,672][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 06:50:59,165][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 06:50:59,658][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10574 tokens. [2025-11-13 06:51:00,491][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.27%, Current % of VRAM taken: 59.72%, Block Peak % of device VRAM: 62.35%, ΔTime: 00:00:32 [2025-11-13 06:51:01,115][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 06:51:01,117][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 06:51:01,118][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 06:51:01,918][__main__][INFO] - Iteration 563 took 1m 43s (65.87% Gen, 33.36% Train). Generation: 1m 8s, Training: 34s. Estimated remaining time: 77h 30m 1s. Estimated total time: 86h 11m 21s. Time estimates for 10 more iterations: 17m 14s, 100 more iterations: 2h 52m 22s, 500 more iterations: 14h 21m 53s. [2025-11-13 06:51:01,920][__main__][INFO] - Starting iteration 563. [2025-11-13 06:51:02,406][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 56 and human policies 1. [2025-11-13 06:51:02,406][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 06:51:14,236][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:14,260][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:14,263][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:14,265][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:14,268][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:14,270][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:14,273][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:14,288][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:14,291][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:14,294][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:14,297][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:14,299][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:14,302][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:14,305][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:14,307][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:14,310][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:14,312][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:14,314][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:14,316][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:14,318][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:14,320][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:14,322][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:14,324][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:14,325][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:14,327][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:14,330][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:14,332][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:14,333][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:14,334][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:14,336][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:14,338][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:14,340][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:14,342][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:14,343][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:14,345][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:14,360][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:14,363][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:14,364][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:14,366][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:14,367][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:14,369][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:14,370][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:14,371][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:14,372][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:14,373][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:14,374][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:14,375][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:14,376][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:14,377][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:14,379][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:14,381][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:14,382][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:14,384][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:14,385][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:14,387][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:14,390][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:14,391][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:14,391][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:14,393][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:14,394][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:14,396][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:14,397][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:14,399][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:14,400][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:14,401][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:14,402][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:14,403][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:14,405][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:14,406][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:14,408][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:14,410][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:14,411][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:14,413][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:14,414][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:14,415][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:14,416][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:14,417][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:14,419][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:14,421][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:14,422][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:14,424][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:14,428][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:14,430][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:14,431][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:14,432][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:14,434][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:14,434][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:14,435][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:14,437][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:14,601][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:14,613][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:14,615][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:14,617][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:14,619][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:14,622][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:14,624][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:14,630][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:14,631][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:14,633][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:14,635][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:14,636][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:14,650][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:14,653][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:14,655][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:14,657][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:14,659][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:14,661][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:14,663][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:14,666][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:14,668][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:14,670][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:14,683][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:14,685][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:14,687][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:14,689][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:14,691][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:14,693][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:14,694][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:14,696][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:14,698][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:14,713][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:14,714][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:14,716][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:14,721][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:15,519][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:15,521][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:15,526][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:15,530][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:15,533][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:15,535][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:15,537][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:15,543][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:15,547][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:15,550][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:15,554][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:15,556][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:15,574][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:15,575][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:15,577][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:15,579][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:15,596][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:15,597][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:15,599][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:15,602][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:15,604][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:15,606][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:15,608][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:15,610][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:15,612][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:15,613][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:15,615][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:15,616][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:15,617][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:15,619][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:15,620][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:15,621][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:15,681][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:15,683][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:15,686][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:15,688][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:15,691][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:15,693][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:15,695][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:15,697][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:15,699][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:15,702][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:15,705][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:15,708][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:15,709][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:15,711][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:15,729][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:15,731][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:15,732][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:15,734][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:15,736][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:15,738][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:15,740][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:15,743][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:15,745][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:15,769][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:15,771][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:15,774][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:15,776][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:15,779][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:15,781][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:15,783][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:15,785][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:15,788][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:15,943][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:15,962][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:15,964][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:15,965][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:15,967][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:15,968][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:15,970][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:15,996][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:15,997][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:15,999][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:16,000][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:16,039][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:16,040][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:16,042][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:16,043][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:16,044][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:16,074][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:16,077][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:16,079][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:16,081][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:16,083][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:16,093][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:16,109][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:16,152][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:16,155][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:16,157][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:16,159][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:16,182][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:17,016][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:17,018][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:17,021][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:17,044][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:17,049][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:17,051][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:17,052][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:17,053][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:17,056][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:17,058][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:17,086][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:17,088][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:17,089][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:17,091][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:17,107][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:17,109][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:17,110][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:17,111][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:17,113][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:17,116][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:17,136][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:17,141][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:17,144][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:17,146][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:17,147][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:17,148][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:17,150][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:17,152][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:17,154][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:17,156][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:17,158][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:17,160][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:17,162][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:17,164][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:17,165][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:17,167][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:17,168][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:17,174][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:17,176][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:17,178][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:17,179][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:17,182][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:17,183][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:17,185][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:17,188][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:17,189][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:17,192][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:17,197][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:17,198][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:17,200][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:17,202][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:17,203][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:17,205][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:17,207][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:17,213][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:17,214][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:17,215][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:17,217][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:17,218][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:17,220][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:17,221][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:17,223][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:17,236][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:17,237][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:17,238][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:17,240][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:17,242][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:17,243][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:17,245][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:17,246][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:17,337][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:17,340][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:17,358][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:17,361][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:17,362][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:17,364][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:17,374][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:17,376][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:17,377][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:17,377][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:17,395][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:17,398][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:17,400][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:17,402][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:17,404][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:17,414][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:17,416][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:17,417][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:17,418][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:17,420][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:17,422][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:17,432][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:17,434][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:17,436][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:17,438][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:17,451][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:17,453][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:17,456][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:17,458][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:17,460][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:17,462][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:17,473][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:17,477][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:17,480][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:17,484][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:17,489][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:17,491][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:17,493][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:17,494][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:17,497][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:17,499][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:17,525][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:17,527][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:17,529][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:17,531][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:17,533][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:17,535][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:17,603][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:17,606][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:17,609][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:17,612][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:17,615][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:17,617][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:17,619][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:17,621][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:17,623][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:17,624][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:17,625][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:17,626][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:17,627][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:17,672][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:17,675][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:17,678][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:17,681][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:17,683][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:17,686][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:17,688][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:17,689][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:17,689][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:17,690][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:17,691][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:17,693][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:17,695][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:17,698][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:17,700][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:17,702][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:17,704][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:17,705][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:17,707][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:17,709][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:17,712][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:17,714][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:17,719][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:17,721][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:17,723][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:17,725][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:17,726][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:17,728][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:17,729][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:17,730][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:17,812][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:17,815][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:17,834][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:17,837][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:17,840][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:17,842][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:17,850][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:17,853][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:17,854][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:17,855][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:17,869][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:17,871][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:17,874][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:17,876][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:17,887][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:17,890][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:17,892][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:17,894][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:17,896][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:17,898][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:17,901][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:17,907][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:17,912][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:17,917][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:17,921][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:17,927][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:17,931][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:17,935][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:17,940][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:17,947][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:17,951][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:17,955][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:17,987][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:18,035][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:18,038][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:18,041][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:18,044][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:18,046][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:18,051][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:18,054][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:18,058][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:18,061][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:18,065][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:18,069][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:18,071][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:18,074][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:18,118][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:18,122][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:18,127][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:18,131][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:18,141][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:18,145][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:18,150][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:18,154][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:18,158][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:18,162][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:18,165][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:18,167][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:18,170][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:18,173][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:18,177][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:18,180][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:18,183][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:18,186][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:18,189][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:18,194][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:18,197][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:18,200][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:18,203][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:18,205][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:18,208][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:18,211][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:18,214][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:18,216][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:18,219][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:18,296][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:18,300][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:18,319][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:18,322][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:18,407][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:18,412][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:18,417][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:18,421][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:18,426][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:18,429][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:18,438][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:18,443][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:18,447][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:18,451][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:18,462][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:18,466][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:18,470][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:18,473][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:18,478][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:18,486][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:18,489][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:18,513][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:18,557][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:18,896][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:18,961][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:18,961][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:18,962][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:18,982][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:18,983][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:19,118][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:19,118][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:19,119][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:19,184][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:19,185][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:19,185][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:19,186][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:19,186][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:19,252][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:19,254][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:19,255][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:20,786][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:20,829][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:20,830][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:20,833][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:20,834][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:20,854][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:20,866][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:20,866][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:20,867][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:20,868][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:20,898][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:20,899][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:20,900][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:20,902][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:20,903][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:20,903][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:20,928][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:20,930][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:20,932][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:20,933][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:20,934][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:20,962][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:20,963][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:20,963][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:20,964][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:20,964][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:20,965][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:20,965][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:20,966][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:20,967][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:20,968][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:20,968][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:20,969][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:20,969][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:20,972][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:20,974][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:20,975][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:20,987][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:20,988][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:20,989][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:20,990][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:20,991][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:21,015][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:21,017][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:21,018][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:21,019][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:21,043][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:21,044][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:21,045][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:21,047][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:21,048][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:21,062][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:21,063][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:21,063][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:21,066][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:21,066][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:21,067][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:21,068][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:21,068][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:21,069][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:21,069][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:21,084][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:21,085][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:21,086][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:21,086][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:21,088][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:21,089][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:21,090][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:21,090][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:21,091][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:21,150][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:21,171][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:21,172][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:21,173][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:21,192][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:21,193][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:21,193][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:21,212][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:21,213][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:21,214][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:21,215][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:21,231][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:21,231][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:21,232][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:21,232][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:21,235][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:21,250][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:21,251][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:21,253][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:21,254][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:21,255][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:21,269][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:21,270][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:21,271][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:21,272][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:21,273][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:21,288][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:21,289][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:21,289][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:21,290][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:21,306][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:21,308][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:21,311][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:21,313][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:21,315][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:21,317][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:21,345][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:21,347][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:21,349][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:21,351][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:21,390][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:21,392][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:21,394][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:21,396][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:21,398][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:21,400][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:21,402][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:21,403][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:21,404][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:21,405][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:21,405][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:21,406][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:21,407][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:21,408][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:21,409][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:21,410][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:21,453][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:21,455][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:21,456][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:21,457][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:21,458][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:21,481][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:21,482][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:21,484][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:21,485][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:21,503][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:21,504][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:21,504][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:21,505][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:21,506][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:21,523][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:21,525][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:21,526][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:21,527][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:21,528][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:21,529][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:21,530][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:21,531][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:21,532][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:21,552][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:21,554][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:21,555][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:21,556][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:21,558][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:21,559][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:21,559][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:21,560][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:21,560][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:21,618][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:21,637][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:21,638][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:21,640][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:21,658][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:21,660][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:21,661][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:21,675][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:21,676][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:21,678][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:21,679][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:21,694][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:21,695][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:21,697][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:21,698][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:21,699][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:21,717][mllm.models.large_language_model_local][WARNING] - Response Given the values, it appears that both you and Bob have significant preferences for hats and books, but you have a higher value for balls compared to hats and books. Here's a proposal that maximizes your points while considering the values: Proposal: 10 hats, 10 books, 10 balls This proposal keeps all items, which should maximize your points based on your high values for hats and balls. Since both you and Bob value hats and books highly, proposing to take all items should be reasonable and maximize your points. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:21,719][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:21,723][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:21,725][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:21,726][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:21,727][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:21,736][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:21,740][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:21,743][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:21,746][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:21,749][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:21,758][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:21,761][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:21,765][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:21,768][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:21,772][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:21,805][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:21,856][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:21,860][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:21,864][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:21,866][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:21,871][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:21,875][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:21,879][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:21,882][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:21,886][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:21,889][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:21,939][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:21,943][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:21,946][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:21,949][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:21,951][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:21,984][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:21,988][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:21,993][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:21,998][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:22,008][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:22,012][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:22,016][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:22,021][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:22,025][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:22,036][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:22,038][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:22,041][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:22,044][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:22,048][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:22,051][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:22,055][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:22,058][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:22,060][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:22,067][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:22,070][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:22,073][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:22,076][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:22,079][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:22,082][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:22,084][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:22,087][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:22,090][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:22,152][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:22,180][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:22,184][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:22,188][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:22,205][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:22,210][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:22,215][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:22,230][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:22,234][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:22,238][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:22,242][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:22,254][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:22,257][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:22,258][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:22,262][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:22,265][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:22,291][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's crucial to maximize the points based on your higher valuation for hats and balls. Here's the optimal proposal: Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:22,293][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:22,295][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:22,300][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:22,303][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:22,307][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:22,320][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:22,402][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:22,404][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:22,406][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:22,407][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:22,409][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:22,413][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:22,430][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:22,433][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:22,435][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:22,437][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:22,438][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:22,440][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:22,464][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:22,467][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:22,470][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:22,471][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:22,473][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:22,475][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:23,260][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:23,267][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:23,305][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:23,309][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:23,311][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:23,338][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:23,340][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:23,344][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:23,346][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:23,386][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:23,393][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:23,397][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:23,400][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:23,403][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:23,406][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:23,408][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:23,417][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:23,420][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:23,425][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:23,427][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:23,429][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:23,430][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:23,447][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:23,450][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:23,452][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:23,454][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:23,457][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:23,471][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:23,473][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:23,475][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:23,477][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:23,478][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:23,481][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:23,483][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:23,509][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:23,511][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:23,513][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:23,516][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:23,519][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:23,521][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:23,524][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:23,541][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:23,544][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:23,546][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:23,547][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:23,550][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:23,552][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:23,553][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:23,555][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:23,568][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:23,570][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:23,572][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:23,574][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:23,575][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:23,577][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:23,579][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:23,581][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:23,583][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:23,592][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:23,594][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:23,596][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:23,598][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:23,600][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:23,601][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:23,616][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:23,618][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:23,620][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:23,664][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:23,686][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:23,688][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:23,690][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:23,708][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:23,711][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:23,730][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:23,733][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:23,737][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:23,739][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:23,751][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:23,753][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:23,756][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:23,757][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:23,771][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:23,773][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:23,774][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:23,775][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:23,789][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:23,791][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:23,792][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:23,793][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:23,833][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:23,835][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:23,837][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:23,838][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:23,852][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:23,856][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:23,871][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:23,874][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:23,876][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:23,878][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:23,879][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:23,881][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:23,883][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:23,885][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:23,888][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:23,924][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:23,926][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:23,929][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:23,931][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:23,934][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:24,007][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:24,013][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:24,015][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:24,018][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:24,020][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:24,023][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:24,025][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:24,034][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:24,037][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:24,040][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:24,042][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:24,045][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:24,047][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:24,060][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:24,063][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:24,066][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:24,069][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:24,071][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:24,074][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:24,077][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:24,079][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:24,094][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:24,097][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:24,099][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:24,102][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:24,105][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:24,107][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:24,109][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:24,119][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:24,121][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:24,124][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:24,127][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:24,129][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:24,131][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:24,134][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:24,148][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:24,151][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:24,197][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:24,219][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:24,223][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:24,225][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:24,238][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:24,241][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:24,258][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:24,261][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:24,264][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:24,266][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:24,279][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:24,282][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:24,285][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:24,287][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:24,301][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:24,303][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:24,306][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:24,309][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:24,311][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:24,314][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:24,316][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:24,324][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:24,330][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:24,337][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:24,342][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:24,349][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:24,354][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:24,360][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:24,365][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:24,374][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and considering Bob's values, it's beneficial to allocate items where both have a high value to each other. Here's the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:24,377][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:24,392][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:24,476][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:24,479][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:24,482][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:24,485][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:24,572][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:24,578][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:24,585][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:24,591][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:24,597][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:24,604][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:24,610][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:24,616][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:24,631][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:24,637][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:24,642][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:24,648][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:24,653][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:24,658][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:24,663][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:24,668][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:24,673][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:24,678][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:24,682][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:24,687][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:24,691][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:24,704][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:24,708][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:24,712][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:24,715][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:24,719][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:24,723][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:24,727][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:24,731][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:24,734][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:24,738][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:24,744][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:24,751][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:24,755][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:24,758][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:24,778][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:24,781][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:24,804][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:24,808][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:24,811][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:24,815][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:24,830][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:24,834][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:24,838][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:24,842][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:24,857][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:24,862][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:24,866][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:24,870][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:24,885][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:24,888][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:24,890][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:24,912][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:24,958][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:24,961][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:25,008][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:25,010][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:25,013][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:25,016][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:25,019][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:25,021][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:25,024][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:25,026][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:25,034][mllm.models.large_language_model_local][WARNING] - Response Given the new per-item values where both you and Alice value hats and books highly, and balls less, a strategic approach would be to propose a split that maximizes the mutual benefit while ensuring you get a fair share of the high-value items. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:25,037][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:25,039][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:25,042][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:25,045][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:25,047][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:25,049][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:25,052][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:25,054][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:25,061][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:25,064][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:25,480][mllm.models.large_language_model_local][WARNING] - Response Given the values, you should prioritize balls as they are most valuable to you, followed by hats and then books. Since Bob values balls the least, you can afford to take all the balls, all the hats, and some books to maximize your points. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:25,684][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both hats and books are less valuable to you compared to balls. To maximize your points, you should try to get as many balls as possible while splitting the hats and books fairly. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:25,803][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:25,810][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:25,816][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:25,823][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:25,840][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:25,843][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:25,858][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:25,863][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:25,866][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:25,868][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:25,870][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:25,885][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:25,887][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:25,889][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:25,895][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:25,935][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:25,938][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:25,940][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:25,943][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:25,945][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:25,947][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:25,950][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:25,952][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:25,966][mllm.models.large_language_model_local][WARNING] - Response Given the new values where you have a higher valuation for hats and books, and Alice values them more highly than balls, a strategic proposal would be to propose dividing the items in a way that maximizes your expected value. Here's the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:25,968][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:25,971][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:25,973][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:25,976][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:25,978][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:25,980][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:25,983][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:25,985][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:25,993][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:25,995][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:25,998][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:26,022][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:26,025][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:26,028][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:26,030][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:26,045][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:26,048][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:26,051][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:26,070][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:26,073][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:26,075][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:26,077][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:26,080][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:26,094][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:26,097][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:26,100][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:26,103][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:26,106][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:26,109][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:26,118][mllm.models.large_language_model_local][WARNING] - Response Given the current values, it's clear that both you and Bob place a high value on different items. You value hats and balls highly and books moderately, while Bob values books and balls highly and hats moderately. To maximize your points, you should propose to keep all items where you have a high or moderate value, and be prepared to give up books, which Bob values a lot. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:26,120][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:26,123][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:26,125][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:26,128][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:26,130][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:26,145][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:26,148][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:26,150][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:26,153][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:26,155][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:26,157][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:26,169][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:26,172][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:26,175][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:26,177][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:26,180][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:26,183][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:26,185][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:26,193][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:26,196][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:26,219][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:26,222][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:26,225][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:26,238][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:26,241][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:26,259][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:26,300][mllm.models.large_language_model_local][WARNING] - Response Given the values, it appears that both you and Bob have similar but not equal valuations for the items. You value hats and books at 1, and balls at 10, while Bob values hats and books at 1, and balls at 10. In this case, proposing to take all items might still be beneficial because it ensures you get the full value of the balls, which are highly valued by both of you. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:26,303][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:26,306][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:26,308][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:26,311][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:26,314][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:26,329][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:26,332][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:26,334][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:26,336][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:26,339][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:26,404][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:26,410][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:26,415][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:26,483][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:26,490][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:26,496][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:26,502][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:26,509][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:26,515][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:26,521][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:26,527][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:26,563][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:26,570][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:26,576][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:26,582][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:26,589][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:26,595][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:26,601][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:26,631][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:26,638][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:26,641][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:26,643][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:26,660][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:26,663][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:26,666][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:26,684][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's beneficial to maximize the items with higher personal value. Since both you and Bob value books at 1 and balls at 1, and you value books more than you value hats or balls, while Bob values hats more than books or balls, the best strategy is to propose keeping all the books and a fair share of the hats and balls. Proposal: 5 hats, 10 books, 5 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:26,687][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:26,689][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:26,692][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:26,707][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:26,710][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:26,713][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:26,715][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:26,718][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:26,733][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:26,736][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:26,739][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:26,756][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:26,759][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:26,761][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:26,764][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:26,766][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:26,769][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:26,772][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:26,774][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:26,782][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:26,785][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:26,787][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:26,790][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:26,792][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:26,795][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:27,508][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:27,515][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:27,518][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:27,521][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:27,523][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:27,562][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:27,565][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:27,567][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:27,570][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:27,608][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:27,611][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:27,639][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:27,642][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:27,662][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:27,692][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:27,710][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:27,760][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:27,811][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:27,814][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:27,817][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:27,823][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:27,830][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:27,836][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:27,840][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:28,001][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:28,005][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:28,009][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:28,080][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:28,086][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:28,104][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:28,110][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:28,117][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:28,123][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:28,135][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:28,141][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:28,147][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:28,153][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:28,159][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:28,169][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:28,174][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:28,178][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:28,188][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:28,193][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:28,198][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:28,202][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:28,207][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:28,215][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:28,220][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:28,224][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:28,228][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:28,233][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:28,237][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:28,241][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:28,245][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:28,249][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:28,256][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:28,260][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:28,265][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:28,289][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:28,293][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:28,296][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:28,300][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:28,348][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:28,352][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:28,437][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:28,444][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:28,528][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:28,621][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:28,705][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:28,708][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:28,797][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:28,886][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:28,964][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, the optimal strategy would be to propose the distribution that maximizes your points. Since hats and balls have higher values for you, while books have a higher value for Bob, you should try to keep as many hats and balls as possible, and let Bob take the books. Proposal: 10 hats, 10 books, 10 balls However, since this exact proposal might lead to a proportional split if Bob also fully claims books, let's propose slightly less books to avoid such risk and maximize our share of high-value items. Proposal: 10 hats, 10 books, 9 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:28,967][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:28,970][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:28,973][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:28,975][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:28,978][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:28,985][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:28,988][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:28,991][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:28,993][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:28,996][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:28,999][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:29,001][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:29,004][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:29,006][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:29,009][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:29,011][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:29,014][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:29,042][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:29,045][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:29,048][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:29,051][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:29,053][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:29,056][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:29,059][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:29,144][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:29,723][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:30,026][mllm.models.large_language_model_local][WARNING] - Response Given that Alice values hats and books less than I do, but values balls less than I do, a cooperative strategy might work well here. We can propose to split the items equally, which should maximize our individual gains. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:30,262][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Alice place a high value on balls. Since the items are split equally and the values are symmetrical in a way that both of you value balls highly, a strategic move would be to ensure you both get an equal share to maximize your total points. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:30,265][mllm.models.large_language_model_local][WARNING] - Response Given the significant value discrepancies for the items and the symmetric nature of the game, it's crucial to maximize the value from the items you highly value more than Bob does. Here, books and balls are more valuable to you, while hats are more valuable to Bob. Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:30,507][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:30,510][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:30,512][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:30,515][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:30,518][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:30,520][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:30,523][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:30,526][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:30,528][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:30,531][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:30,534][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:30,537][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:30,540][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:30,543][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:30,564][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:30,567][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:30,569][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:30,571][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:30,574][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:30,576][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:30,578][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:30,580][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:30,589][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:30,591][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:30,594][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:30,597][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:30,599][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:30,602][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:30,604][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:30,607][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:30,609][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:30,611][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:30,614][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:30,673][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:30,676][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:30,678][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:30,681][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:30,683][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:30,685][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:30,688][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:30,690][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:30,693][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:30,695][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:30,735][mllm.models.large_language_model_local][WARNING] - Response Given the values, you have a significant advantage in的价值在书籍和球上，而鲍勃在帽子和书籍上更有价值。为了最大化你的得分，你需要尽可能多地获取书籍和球，同时也要确保不至于分配超出物品总数导致不能获得满分。 因此，合理的提案是分配大部分物品给你自己价值较高的书籍和球，同时留下足够的帽子给鲍勃以确保他不会完全拒绝分配。 Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:30,742][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:30,745][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:30,758][mllm.models.large_language_model_local][WARNING] - Response Given the high value of balls for both of us and the equal distribution of other items, a collaborative approach seems beneficial. However, since we have to submit our proposals simultaneously and knowing Bob values hats and books more than balls, it might be strategic to slightly lag our proposal for balls to ensure we get a fair share. Proposal: 10 hats, 10 books, 9 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:30,761][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:30,764][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:30,767][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:30,783][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:30,806][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:30,809][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:30,866][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and the symmetric nature of the situation, a strategic proposal would be to allocate the items in a way that maximizes both our and Bob's values proportionally. Since both of our per-item value distributions favor hats and balls over books, and Bob's values favor books and balls over hats, proposing a fair but slightly advantageous split might be a good approach. Proposal: 6 hats, 10 books, 14 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:30,869][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's crucial to consider the strategic importance of each item. Books and balls are particularly valuable to you, while hats are relatively less valuable. Here's a proposal that tries to maximize your points by keeping the most valuable items: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:30,875][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:30,878][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:30,881][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:30,916][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:30,919][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:30,921][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:30,924][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:30,927][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:30,929][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:30,932][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:30,942][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:30,944][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:30,947][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:30,965][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:30,968][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:30,971][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:30,973][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:30,976][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:30,979][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:31,010][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:31,013][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:31,016][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:31,047][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:31,050][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:31,053][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:31,059][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:31,065][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:31,077][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:31,083][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:31,089][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:31,094][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:31,100][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:31,103][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:31,105][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:31,107][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:31,110][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:31,112][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:31,114][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:31,125][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:31,127][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:31,129][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:31,132][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:31,134][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:31,136][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:31,138][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:31,140][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:31,142][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:31,145][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:31,147][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:31,149][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:31,151][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:31,153][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:31,155][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:31,158][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:31,160][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:31,162][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:31,164][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:31,169][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:31,171][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:31,174][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:31,176][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:31,178][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:31,180][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:31,182][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:31,184][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:31,192][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:31,194][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:31,220][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:31,223][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:31,225][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:31,255][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:31,277][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:31,280][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:31,297][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:31,317][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:31,320][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:31,323][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:31,339][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:31,360][mllm.models.large_language_model_local][WARNING] - Response Given the current incentives, it makes sense to propose an equal split to ensure both Alice and you receive the items, as this maximizes the chances of receiving a higher value item from Alice's perspective. Here is the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:31,363][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:31,365][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:31,368][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:31,371][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:31,373][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:31,376][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:31,448][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:31,451][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:31,457][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:31,460][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:31,529][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:31,532][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:31,534][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:31,537][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:31,540][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:31,618][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:31,698][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:31,701][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:31,704][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:31,864][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:31,871][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:32,124][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:32,131][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:32,138][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:32,144][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:32,150][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:32,153][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:32,156][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:32,206][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:32,211][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:32,216][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:32,222][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:32,227][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:32,232][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:32,237][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:32,243][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:32,248][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:32,253][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:32,259][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:32,264][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:32,269][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:32,274][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:32,279][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:32,283][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:32,288][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:32,292][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:32,297][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:32,311][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, the optimal strategy would be to ensure that each agent claims items they value highly while avoiding overclaiming to prevent proportional reduction in case of disputes. Here, both you and Alice value balls the highest at 10, hats slightly less at 1, and books the least at 1. A fair and potentially maximizing proposal would be to split the items evenly but with a slight advantage to claiming the more valued items. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:32,313][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:32,317][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:32,321][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:32,325][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:32,328][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:32,332][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:32,336][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:32,340][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:32,344][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:32,348][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:32,350][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:32,378][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:32,382][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:32,385][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:32,469][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:32,556][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:32,562][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:32,639][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:32,726][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:32,732][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:32,738][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:32,814][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:32,937][mllm.models.large_language_model_local][WARNING] - Response Given the expectation of symmetric and potentially competitive values, proposing an equal share could maximize the point distribution. Here is my proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:33,233][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric and valuable nature of the items, and considering that we both value hats the same and balls the same, it's optimal to claim all available items to maximize our points. Here is the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:33,340][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Alice have a mutual interest in balls and books, while hats are less valuable to you. Here's a strategic proposal to maximize your points: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:33,348][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:33,351][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:33,410][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Bob place high value on balls and low value on hats and books. However, since the total number of items is 30 and we need to propose allocations that do not exceed this total, a strategy of splitting the items equally or nearly equally is a good approach to ensure both parties receive a fair share. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:33,413][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:33,415][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:33,419][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:33,425][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:33,431][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:33,434][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:33,437][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:33,439][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:34,106][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:34,209][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:34,254][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:34,257][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:34,275][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:34,278][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:34,281][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:34,301][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:34,304][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:34,330][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:34,360][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:34,363][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:34,366][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:34,369][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:34,372][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:34,394][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Bob place a high value on different items. You value hats and balls highly, while you and Bob both value books moderately. A fair and strategic proposal would be to split the items so that both parties receive a reasonable amount of the items they value highly. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:34,397][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:34,400][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:34,424][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:34,427][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:34,431][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:34,433][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:34,436][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:34,514][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the fact that both you and Alice value balls the most, it's strategically optimal to assume that Alice will also propose to take all the balls in this round. Therefore, to maximize your points from hats and books, you should also propose to take all of them. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:34,518][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:34,520][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:34,524][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:34,526][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:34,529][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:34,532][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:34,609][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:34,612][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:34,615][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:34,618][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:34,622][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:34,624][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:34,627][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:34,629][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:34,632][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:34,635][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:34,700][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:34,703][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:34,706][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:34,709][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:34,712][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:34,715][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:34,717][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:34,720][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:34,722][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:34,725][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:34,728][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:34,783][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:34,786][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:34,789][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:34,792][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:34,794][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:34,797][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:34,800][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:34,868][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:34,872][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:34,950][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:34,953][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:34,956][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:35,036][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:35,039][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:35,042][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:35,045][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:35,048][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:35,051][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:35,053][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:35,133][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:35,136][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:35,139][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:35,141][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:35,145][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:35,148][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:35,186][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:35,189][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:35,222][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:35,304][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:35,307][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:35,384][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:35,386][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:35,389][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:35,463][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:35,466][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:35,513][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:35,554][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:35,556][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:35,559][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:35,562][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:35,564][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:35,567][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:35,941][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:35,944][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:35,951][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:35,957][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:35,964][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:35,970][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:35,976][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:35,983][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:35,989][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:35,992][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:35,994][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:35,997][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:36,000][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:36,002][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:36,005][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:36,008][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:36,010][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:36,013][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:36,015][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:36,018][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:36,020][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:36,023][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:36,026][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:36,029][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:36,031][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:36,033][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:36,036][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:36,038][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:36,040][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:36,043][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:36,045][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:36,047][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:36,050][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:36,052][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:36,054][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:36,057][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:36,059][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:36,061][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:36,063][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:36,066][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:36,069][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:36,071][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:36,090][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:36,092][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:36,094][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:36,095][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:36,097][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:36,099][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:36,101][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:36,102][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:36,104][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:36,106][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:36,108][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:36,110][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:36,111][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:36,113][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:36,445][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:36,448][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:36,568][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:36,570][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:36,593][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:36,633][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:36,670][mllm.models.large_language_model_local][WARNING] - Response Given the high value of balls for both of us and the equal distribution of values for hats and books, I will propose an allocation that maximizes the points for both items where I have a significant value advantage. Here is my proposal: Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:36,674][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:36,762][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:36,894][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:36,898][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:36,900][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:36,907][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:36,914][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:36,920][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:36,927][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:36,933][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:36,940][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:36,946][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:36,952][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:36,974][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:36,978][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:36,982][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:36,987][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:36,991][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:36,995][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:37,000][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:37,005][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:37,010][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:37,014][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:37,018][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:37,022][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:37,027][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:37,031][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:37,035][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:37,039][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:37,043][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:37,047][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:37,051][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:37,055][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:37,059][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:37,063][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:37,066][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:37,068][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:37,070][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:37,073][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:37,077][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:37,081][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:37,084][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:37,088][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:37,091][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:37,095][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:37,098][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:37,102][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:37,105][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:37,108][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:37,112][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:37,115][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:37,117][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:37,118][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:37,127][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:37,128][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:37,130][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and the distribution of values between you and Bob, proposing to take all the items is a strategic move since it maximizes the points based on your per-item values. Here’s the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:37,131][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:37,133][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:37,135][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:37,136][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:37,138][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it is optimal to propose a distribution that aligns with maximizing your own value since you have a higher valuation for hats and balls compared to Bob, and a higher valuation for books compared to Bob. Here is the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:37,175][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:37,177][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:37,261][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:37,264][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:37,266][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:37,269][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:37,272][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:37,274][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:37,277][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:37,587][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:37,590][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:37,628][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:37,630][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:37,633][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:37,651][mllm.models.large_language_model_local][WARNING] - Response Given the current per-item values, it's clear that both hats and books are low-value items for you, while balls are high-value. However, Bob values hats and books more highly than balls. To maximize your points, you should prioritize taking as many high-value balls as possible while splitting the low-value items (hats and books) equally or in a way that minimizes your loss. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:37,654][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:37,657][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:37,663][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:37,774][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:37,776][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:38,050][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:38,052][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:38,055][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:38,059][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:38,061][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:38,063][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:38,064][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:38,066][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:38,068][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:38,070][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:38,073][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:38,075][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:38,226][mllm.models.large_language_model_local][WARNING] - Response Given that both you and Bob have significantly different valuations for the items, a strategic proposal would be to propose an allocation that maximizes the value you receive based on your per-item values. Here's the proposal: Proposal: 10 hats, 10 books, 10 balls This proposal assumes an equal split of items, which may not be optimal given the valuations. Since you value books and balls much higher than hats, and Bob values hats and books more, it might be better to propose a different split. A more optimal proposal would be to give you the items you value the most and let Bob take the items he values more. Given your valuations, you should propose to take all the balls and books, and split the hats. Proposal: 10 hats, 10 books, 10 balls -> 10 hats, 10 books, 10 balls -> 10 balls, 10 books, 0 hats Since the system might not accept fractional items, we propose: Proposal: 10 books, 10 balls, 0 hats did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:38,230][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:38,231][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:38,325][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:38,332][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:38,338][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:38,341][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:38,344][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:38,346][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:38,349][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:38,502][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:38,506][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:38,589][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both hats and books have low value for you, while balls have a much higher value. Therefore, it's crucial to secure as many balls as possible while splitting the hats and books evenly. Here’s a revised proposal: Proposal: 5 hats, 5 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:38,592][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:38,673][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:38,760][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:38,763][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:38,767][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:38,950][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:39,040][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:39,043][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:39,130][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:39,133][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:39,136][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:39,221][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:39,224][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:39,311][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:39,314][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:39,317][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:39,320][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:39,406][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:39,409][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:39,412][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:39,415][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:39,496][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:39,499][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:39,501][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:39,504][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:39,507][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:39,511][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:39,513][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:39,600][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:39,603][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:39,606][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:39,609][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:39,612][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:39,614][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:39,703][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:39,707][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:39,710][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:39,713][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:39,716][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:39,718][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:39,721][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:39,724][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:39,726][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:39,729][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:39,731][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:39,734][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:39,833][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and the symmetry in the item distribution, it's advantageous to take the items with higher personal value. Here, my per-item values are highest for balls (10), followed by hats (1) and books (1), while Alice values hats highest (10), followed by balls (1) and books (1). Proposing a strategy that captures the high-value items for myself: Proposal: 10 hats, 10 books, 10 balls However, this might be seen as inefficient since it doesn't maximize the proportional value. A better approach might be to propose to take the items that have high value to me, ensuring I get a significant share. Proposal: 10 hats, 10 books, 10 balls This proposal maximizes my points as much as possible given the high personal values. Let me refine it to: Proposal: 10 hats, 10 books, 10 balls This proposal ensures that I am taking all items, which aligns with maximizing points based on my personal values. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:39,962][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the high value of balls for Bob and the low value of balls for me, I should propose a strategy that maximizes my expected payoff while considering Bob's likely counter-proposal. Here's my proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:40,087][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:40,090][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:40,093][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:40,096][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:40,149][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:40,152][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:40,156][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:40,158][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:40,161][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:40,164][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:40,167][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:40,170][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:40,889][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:40,892][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:40,894][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:40,897][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:40,900][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:40,902][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:40,905][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:40,911][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:40,918][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:40,920][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:40,923][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:40,926][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:40,929][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:40,931][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:40,934][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:40,937][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:40,939][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:40,942][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:40,945][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:41,037][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:41,041][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:41,043][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:41,046][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:41,048][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:41,051][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:41,053][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:41,055][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:41,058][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:41,060][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:41,062][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:41,065][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:41,334][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:41,337][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:41,340][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:41,343][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:41,346][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:41,348][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:41,351][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:41,390][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:41,441][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:41,470][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:41,473][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:41,475][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:41,478][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:41,481][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:41,483][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:41,681][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both of us have a higher value for hats and books compared to balls. However, Alice values balls much higher than hats and books. To maximize points, we should try to allocate hats and books in a way that both of us get a fair share while also considering Alice's high value for balls. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:41,684][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:41,688][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:41,691][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:41,693][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:41,696][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:41,699][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:41,702][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:41,705][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:41,708][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:41,755][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:41,758][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:41,760][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:41,764][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:41,843][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:41,846][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:41,849][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:41,852][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:41,854][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:41,857][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:41,860][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:41,863][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:41,866][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:41,868][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:41,871][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:41,947][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:41,950][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:41,953][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:41,959][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:41,966][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:41,972][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:41,979][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:41,986][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:41,992][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:41,998][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:42,005][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:42,011][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:42,018][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:42,023][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:42,029][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:42,034][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:42,039][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:42,236][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:42,238][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:42,322][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:42,325][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:42,328][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:42,330][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:42,333][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:42,336][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:42,339][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:42,411][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:42,570][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:42,655][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:42,662][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:42,669][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:42,675][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:42,682][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:42,685][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:43,484][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:43,566][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:43,743][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:43,746][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:43,836][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:43,840][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:44,110][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and the symmetry in the game, a reasonable approach is to propose a split that leverages the higher values of the items. Since both you and Bob value hats and balls relatively highly, and books are valued similarly by both, a strategic proposal would be to ensure you get as many of the highly valued items as possible. Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:44,539][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:44,885][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:44,888][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:44,890][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:44,893][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:44,896][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:44,898][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:44,901][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:44,903][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:44,906][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:44,908][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:44,941][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:44,943][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:44,945][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:44,947][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:44,949][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:44,951][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:45,412][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:45,416][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:45,418][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:45,422][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:45,424][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:45,655][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Alice have similar valuations for hats and balls, but Alice values books much higher than you do. To maximize your points, you should aim to get the items you value highly. Since Alice values books more, you should propose keeping all the balls and a significant portion of the books. Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:45,658][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:45,661][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:45,737][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:45,740][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:45,815][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:45,895][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:45,898][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:45,901][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:46,053][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:46,135][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that hats and books are highly valued by me and balls are highly valued by Bob. To maximize points, I should try to take as many hats and books as possible while considering Bob's valuation for balls. Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:46,142][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:46,145][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:46,148][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:46,150][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:46,153][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:46,156][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:46,243][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:46,246][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:46,473][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:46,476][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:46,479][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:46,482][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:46,484][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:46,634][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:46,637][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:46,640][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:46,642][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:46,645][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:46,648][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:46,651][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:46,653][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:46,656][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:46,670][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:46,673][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:46,676][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:46,679][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:46,682][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:46,685][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:46,688][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:46,690][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:46,693][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:46,696][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:46,699][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:46,701][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:46,708][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:46,711][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:46,714][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:46,716][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:46,719][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:46,722][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:46,724][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:46,727][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:46,730][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:46,732][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:46,735][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:46,737][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:46,739][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:46,742][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:46,753][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:46,756][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:47,106][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:47,121][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:47,124][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:47,127][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:47,130][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:47,133][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:47,366][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:47,369][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:47,460][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:47,463][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:47,466][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:47,469][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:47,525][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:47,529][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:47,531][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:47,534][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:47,536][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:47,539][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:47,542][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:47,544][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:47,621][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:47,628][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:47,635][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:47,642][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:47,645][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:47,647][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:47,650][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:47,656][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:47,663][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:47,669][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:47,676][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:47,682][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:47,689][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:47,695][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:47,701][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:47,707][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:47,712][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:47,718][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:47,723][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:47,728][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:47,733][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:47,739][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:48,952][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:48,955][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:49,019][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:49,022][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:49,090][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:49,104][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:49,107][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:49,110][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:49,122][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:49,124][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:49,127][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:49,219][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:49,233][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:49,393][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric nature of the game and the values provided, it seems rational to assume that proposing an equal distribution of the items would maximize the points for both players, given that both players have high values for different items, but low values for the same items as each other. Here is the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:49,503][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:49,515][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:49,615][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:49,629][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:49,632][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:49,711][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:49,723][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:49,812][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:49,824][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:49,827][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:49,830][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:49,832][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:49,910][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:49,922][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:50,018][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:50,029][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:50,032][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:50,035][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:50,042][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:50,234][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:50,241][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:50,352][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:50,355][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:50,467][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:50,478][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:50,481][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:51:50,550][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:50,552][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:50,650][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:50,662][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:50,664][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:51:50,734][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:50,741][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:50,840][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:50,852][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:50,858][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:51:51,917][__main__][INFO] - Number of regex retries in iteration 563: 2094 [2025-11-13 06:51:51,918][__main__][INFO] - agents played in iteration 563 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 06:51:52,828][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:51:52,850][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:51:52,874][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:51:52,894][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:51:52,895][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 06:51:52,895][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 06:51:53,638][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 06:51:54,083][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 06:51:54,579][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 06:51:55,071][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 06:51:55,560][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 06:51:56,048][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 06:51:56,547][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 06:51:57,041][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 06:51:57,533][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 06:51:58,025][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 06:51:58,517][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 06:51:59,011][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 06:51:59,504][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 06:51:59,997][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 06:52:00,491][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 06:52:00,990][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 06:52:01,487][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 06:52:01,985][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 06:52:02,487][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 06:52:02,988][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 06:52:03,491][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 06:52:03,998][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 06:52:04,499][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 06:52:04,998][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 06:52:05,499][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 06:52:06,001][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 06:52:06,502][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 06:52:07,002][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 06:52:07,501][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 06:52:08,000][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 06:52:08,498][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 06:52:08,995][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 06:52:09,497][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 06:52:09,998][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 06:52:10,498][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 06:52:10,999][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 06:52:11,499][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 06:52:12,011][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 06:52:12,510][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 06:52:13,010][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 06:52:13,509][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 06:52:14,015][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 06:52:14,533][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 06:52:15,035][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 06:52:15,537][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 06:52:16,040][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 06:52:16,540][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 06:52:17,048][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 06:52:17,551][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 06:52:18,051][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 06:52:18,552][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 06:52:19,050][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 06:52:19,550][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 06:52:20,046][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 06:52:20,545][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 06:52:21,049][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 06:52:21,543][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 06:52:22,042][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 06:52:22,534][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 06:52:23,030][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 06:52:23,527][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 06:52:24,030][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 06:52:24,527][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 06:52:25,038][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 06:52:25,536][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10866 tokens. [2025-11-13 06:52:26,390][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.24%, Current % of VRAM taken: 59.69%, Block Peak % of device VRAM: 62.31%, ΔTime: 00:00:32 [2025-11-13 06:52:27,023][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 06:52:27,026][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 06:52:27,028][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 06:52:27,835][__main__][INFO] - Iteration 564 took 1m 25s (57.95% Gen, 41.10% Train). Generation: 49s, Training: 35s. Estimated remaining time: 62h 28m 43s. Estimated total time: 71h 11m 29s. Time estimates for 10 more iterations: 14m 14s, 100 more iterations: 2h 22m 22s, 500 more iterations: 11h 51m 54s. [2025-11-13 06:52:27,838][__main__][INFO] - Starting iteration 564. [2025-11-13 06:52:28,335][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 56 and human policies 1. [2025-11-13 06:52:28,336][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 06:52:50,795][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:50,821][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:50,823][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:50,826][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:50,829][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:50,831][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:50,834][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:50,836][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:50,839][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:50,841][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:50,844][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:50,846][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:50,849][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:50,860][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:50,862][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:50,865][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:50,867][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:50,869][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:50,871][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:50,874][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:50,876][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:50,878][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:50,881][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:50,883][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:50,886][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:50,888][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:50,890][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:50,892][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:50,895][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:50,897][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:50,899][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:50,901][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:50,904][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:50,907][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:50,910][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:50,912][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:50,914][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:50,932][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:50,934][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:50,936][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:50,938][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:50,940][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:50,942][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:50,944][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:50,946][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:50,948][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:50,949][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:50,951][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:50,952][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:50,954][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:50,956][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:50,958][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:50,960][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:50,962][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:50,964][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:50,967][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:50,969][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:50,971][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:50,973][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:50,975][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:50,977][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:50,979][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:50,981][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:50,983][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:50,985][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:50,987][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:50,988][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:50,989][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:50,991][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:50,994][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:50,995][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:50,997][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:50,998][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:51,000][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:51,002][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:51,003][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:51,005][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:51,006][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:51,008][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:51,009][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:51,011][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:51,012][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:51,014][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:51,015][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:51,017][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:51,018][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:51,020][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:51,021][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:51,023][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:51,024][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:51,026][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:51,027][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:51,041][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:51,043][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:51,044][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:51,123][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:51,141][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:51,143][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:51,146][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:51,148][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:51,150][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:51,152][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:51,155][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:51,157][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:51,165][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:51,167][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:51,170][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:51,172][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:51,174][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:51,176][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:51,179][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:51,186][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:51,189][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:51,191][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:51,194][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:51,196][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:51,198][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:51,200][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:51,203][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:51,205][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:51,207][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:51,210][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:51,221][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:51,223][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:51,225][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:51,227][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:51,229][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:51,232][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:51,234][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:51,236][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:51,238][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:51,240][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:51,250][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:51,252][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:51,254][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:51,255][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:51,257][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:51,258][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:51,261][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:51,263][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:51,264][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:51,266][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:51,268][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:51,275][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:51,277][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:51,279][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:51,281][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:51,282][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:51,284][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:51,285][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:51,287][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:51,289][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:51,290][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:51,292][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:51,294][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:51,295][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:51,297][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:51,298][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:51,300][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:51,302][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:51,309][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:51,311][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:51,313][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:51,314][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:51,316][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:51,318][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:51,319][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:51,321][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:51,322][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:51,324][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:51,326][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:51,328][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:51,330][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:51,332][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:51,333][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:51,335][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:51,336][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:51,338][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:51,340][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:51,341][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:51,349][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:51,351][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:51,353][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:51,354][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:51,356][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:51,357][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:51,359][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:51,361][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:51,362][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:51,423][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:51,438][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:51,444][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:51,449][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:51,459][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:51,465][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:51,470][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:51,475][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:51,485][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:51,490][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:51,495][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:51,500][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:51,504][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:51,509][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:51,513][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:51,524][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:51,528][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:51,531][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:51,535][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:51,539][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:51,542][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:51,546][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:51,550][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:51,553][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:51,557][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:51,561][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:51,564][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:51,567][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:51,571][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:51,581][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:51,584][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:51,587][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:51,590][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:51,593][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:51,596][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:51,599][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:51,603][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:51,606][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:51,609][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:51,611][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:51,614][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:51,617][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:51,620][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:51,622][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:51,625][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:51,628][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:51,630][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:51,633][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:51,636][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:51,643][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:51,646][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:51,649][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:51,651][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:51,654][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:51,657][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:51,659][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:51,662][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:51,665][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:51,668][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:51,670][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:51,673][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:51,675][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:51,678][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:51,681][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:51,683][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:51,686][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:51,688][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:51,691][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:51,694][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:51,696][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:51,699][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:51,701][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:51,704][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:51,707][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:51,709][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:51,712][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:51,714][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:51,716][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:51,725][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:51,727][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:51,730][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:51,732][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:51,734][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:51,737][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:51,739][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:51,741][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:51,744][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:51,746][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:51,748][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:51,750][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:51,753][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:51,755][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:52,454][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:52,745][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:52,747][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:52,750][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:52,752][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:52,774][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:52,776][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:52,779][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:52,793][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:52,796][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:52,817][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:52,820][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:52,823][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:52,825][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:52,840][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:52,842][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:52,845][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:52,865][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:52,868][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:52,870][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:52,872][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:52,889][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:52,892][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:52,895][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:52,914][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:52,917][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:52,919][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:52,922][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:52,924][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:52,926][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:52,929][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:52,938][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:52,941][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:52,943][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:52,946][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:52,949][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:52,951][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:52,953][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:52,957][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:52,965][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:52,967][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:52,970][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:52,972][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:52,974][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:52,987][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:52,989][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:52,992][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:52,994][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:52,996][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:53,010][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:53,013][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:53,015][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:53,017][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:53,020][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:53,022][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:53,025][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:53,033][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:53,036][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:53,038][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:53,040][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:53,042][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:53,044][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:53,046][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:53,049][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:53,051][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:53,054][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:53,056][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:53,058][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:53,065][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:53,067][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:53,069][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:53,070][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:53,073][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:53,075][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:53,077][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:53,079][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:53,080][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:53,082][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:53,084][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:53,086][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:53,087][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:53,093][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:53,095][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:53,097][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:53,099][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:53,101][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:53,102][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:53,104][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:53,106][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:53,108][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:53,110][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:53,112][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:53,126][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:53,129][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:53,166][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:53,168][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:53,169][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:53,171][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:53,203][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:53,206][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:53,208][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:53,222][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:53,225][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:53,243][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:53,246][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:53,248][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:53,251][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:53,258][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:53,261][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:53,263][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:53,278][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:53,280][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:53,283][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:53,285][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:53,296][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:53,298][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:53,301][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:53,315][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:53,317][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:53,320][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:53,322][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:53,324][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:53,332][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:53,334][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:53,337][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:53,339][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:53,342][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:53,344][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:53,346][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:53,354][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:53,356][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:53,358][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:53,360][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:53,363][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:53,365][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:53,367][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:53,370][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:53,376][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:53,378][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:53,381][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:53,383][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:53,385][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:53,391][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:53,394][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:53,396][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:53,398][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:53,408][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:53,410][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:53,413][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:53,415][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:53,417][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:53,419][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:53,429][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:53,432][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:53,434][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:53,436][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:53,438][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:53,440][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:53,443][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:53,445][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:53,448][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:53,454][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:53,460][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:53,462][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:53,463][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:53,464][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:53,467][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:53,469][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:53,471][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:53,473][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:53,475][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:53,477][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:53,482][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:53,484][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:53,486][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:53,488][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:53,490][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:53,492][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:53,494][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:53,496][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:53,501][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:53,503][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:53,505][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:53,507][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:53,508][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:53,514][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:53,516][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:53,517][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:53,527][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:53,531][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:53,536][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:53,540][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:53,549][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:53,553][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:53,557][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:53,564][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:53,570][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:53,582][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:53,585][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:53,602][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:53,606][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:53,610][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:53,614][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:53,618][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:53,625][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:53,630][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:53,633][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:53,642][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:53,646][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:53,650][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:53,654][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:53,663][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:53,667][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:53,670][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:53,686][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:53,690][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:53,693][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:53,697][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:53,701][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:53,705][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:53,711][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:53,715][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:53,718][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:53,722][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:53,725][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:53,729][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:53,734][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:53,738][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:53,741][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:53,745][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:53,748][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:53,752][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:53,755][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:53,760][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:53,763][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:53,766][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:53,769][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:53,773][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:53,776][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:53,779][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:53,784][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:53,787][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:53,790][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:53,793][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:53,795][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:53,805][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:53,808][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:53,811][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:53,814][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:53,817][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:53,832][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:53,835][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:53,838][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:53,841][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:53,844][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:53,857][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:53,860][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:53,864][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:53,867][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:53,870][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:53,874][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:53,877][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:53,880][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:53,884][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:53,887][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:53,896][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:53,899][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:53,902][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:53,905][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:53,908][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:53,911][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:53,914][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:53,917][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:53,922][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:53,925][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:53,928][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:53,931][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:53,934][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:53,937][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:53,938][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:53,941][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:52:54,742][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:54,944][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:54,946][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:54,948][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:54,951][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:54,999][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:55,002][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:55,005][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:55,025][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:55,027][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:55,030][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:55,048][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:55,051][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:55,073][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:55,076][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:55,096][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:55,100][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:55,102][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:55,105][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:55,107][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:55,109][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:55,121][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:55,124][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:55,126][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:55,130][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:55,132][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:55,135][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:55,149][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:55,152][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:55,175][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:55,178][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:55,180][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:55,183][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:55,185][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:55,206][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:55,209][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:55,212][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:55,215][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:55,218][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:55,220][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:55,223][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:55,225][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:55,234][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:55,237][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:55,239][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:55,242][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:55,244][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:55,258][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:55,261][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:55,263][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:55,266][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:55,268][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:55,270][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:55,273][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:55,275][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:55,277][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:55,280][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:55,288][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:55,290][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:55,292][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:55,294][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:55,296][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:55,298][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:55,305][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:55,308][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:55,310][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:55,313][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:55,330][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:55,332][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:55,335][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:55,337][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:55,339][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:55,342][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:55,350][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:55,353][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:55,355][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:55,357][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:55,359][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:55,361][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:55,377][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:55,379][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:55,381][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:55,383][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:55,385][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:55,387][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:55,389][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:55,391][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:55,393][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:55,395][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:55,397][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:55,399][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:55,401][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:55,407][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:55,409][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:55,411][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:55,450][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:55,453][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:55,455][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:55,457][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:55,499][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:55,501][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:55,504][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:55,555][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:55,558][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:55,560][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:55,572][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:55,574][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:55,589][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:55,592][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:55,608][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:55,611][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:55,614][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:55,616][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:55,619][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:55,650][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:55,653][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:55,655][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:55,658][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:55,660][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:55,663][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:55,691][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:55,694][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:55,697][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:55,739][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:55,742][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:55,745][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:55,747][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:55,750][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:55,757][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:55,760][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:55,763][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:55,765][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:55,767][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:55,776][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:55,777][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:55,778][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:55,779][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:55,780][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:55,782][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:55,783][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:55,783][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:55,793][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:55,795][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:55,797][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:55,800][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:55,802][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:55,853][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:55,856][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:55,859][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:55,862][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:55,864][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:55,867][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:55,870][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:55,872][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:55,898][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:55,901][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:55,904][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:55,906][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:55,909][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:55,911][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:55,914][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:55,954][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:55,956][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:55,959][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:55,961][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:55,964][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:55,972][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:55,974][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:55,977][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:55,980][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:55,982][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:55,984][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:55,987][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:55,994][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:56,909][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:56,955][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:56,958][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:56,960][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:56,962][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:56,964][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:56,966][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:56,968][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:57,002][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:57,004][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:57,007][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:57,009][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:57,011][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:57,014][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:57,016][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:57,057][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:57,060][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:57,062][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:57,065][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:57,067][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:57,070][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:57,073][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:57,075][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:57,106][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:57,109][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:57,112][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:57,156][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:57,158][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:57,181][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:57,184][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:57,205][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:57,208][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:57,211][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:57,247][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:57,250][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:57,253][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:57,255][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:57,258][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:57,261][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:57,291][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:57,293][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:57,296][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:57,298][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:57,330][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:57,332][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:57,365][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:57,368][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:57,370][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:57,393][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:57,396][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:57,399][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:57,401][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:57,404][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:57,407][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:57,410][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:57,412][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:57,415][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:57,417][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:57,420][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:57,422][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:57,430][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:57,433][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:57,436][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:57,438][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:57,464][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:57,467][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:57,470][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:57,473][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:57,476][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:57,479][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:57,482][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:57,517][mllm.models.large_language_model_local][WARNING] - Response Given the updated values, where both you and Bob value books and balls highly and hats less, a cooperative approach might be beneficial to ensure we both maximize our points. Let's propose splitting the items in a way that ensures both of us receive a high value for books and balls. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:57,520][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:57,523][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:57,525][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:57,528][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:57,530][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:57,533][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:57,536][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:57,538][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:57,562][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:57,565][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:57,567][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:57,606][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:57,609][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:57,612][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:57,615][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:57,617][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:57,625][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:52:57,628][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:57,631][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:57,633][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:57,635][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:57,638][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:58,636][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:58,640][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:58,643][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:58,645][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:58,648][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:58,650][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:58,653][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:58,656][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:58,658][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:58,661][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:58,697][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:58,700][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:58,703][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:58,705][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:58,708][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:58,749][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:58,752][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:58,754][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:58,757][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:58,760][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:58,762][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:58,805][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:58,808][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:58,843][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:58,847][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:58,869][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:58,902][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:58,905][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:58,907][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:58,910][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:58,913][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:58,916][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:58,929][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:58,931][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:58,934][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:58,937][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:58,974][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:58,977][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:59,017][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:59,019][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:59,022][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:59,040][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:59,043][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:59,046][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:59,048][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:59,051][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:59,065][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:59,068][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:59,071][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:59,074][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:59,077][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:59,080][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:59,082][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:59,085][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:59,088][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:59,090][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:59,099][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:59,101][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:59,104][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:59,107][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:59,110][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:59,113][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:59,143][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:59,146][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:59,149][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:59,151][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:59,154][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:59,156][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:59,159][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:59,162][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:59,192][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:59,195][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:59,198][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:59,200][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:59,233][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:59,236][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:59,239][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:59,253][mllm.models.large_language_model_local][WARNING] - Response Given the new values, where both you and Alice have high values for hats and books, but Alice also values balls similarly to you, a cooperative approach might maximize points. Let's allocate the items based on their values to both parties. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:59,256][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:59,259][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:59,262][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:59,265][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:59,268][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:59,275][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:59,278][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:52:59,281][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:00,309][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:00,355][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:00,357][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:00,359][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:00,361][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:00,363][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:00,365][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:00,367][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:00,370][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:00,372][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:00,413][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:00,417][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:00,420][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:00,422][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:00,425][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:00,428][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:00,431][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:00,463][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:00,466][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:00,469][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:00,471][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:00,512][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:00,515][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:00,570][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:00,573][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:00,576][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:00,578][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:00,581][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:00,598][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:00,601][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:00,604][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:00,606][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:00,609][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:00,646][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:00,649][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:00,689][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it is beneficial to prioritize items that you value more highly. Specifically, since hats and balls are more valuable to you compared to books, it makes sense to allocate more of these items to yourself. Here's the proposal: Proposal: 10 hats, 10 balls, 10 books This allocation ensures you get the higher-value items, while also splitting the lower-value books relatively evenly. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:00,692][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:00,695][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:00,697][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:00,711][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:00,714][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:00,717][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:00,719][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:00,733][mllm.models.large_language_model_local][WARNING] - Response Given the new per-item values, it's clear that both you and Bob value the hats the most, followed by books and balls. You should propose a distribution that maximizes your points by allocating more items to the categories you value more. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:00,736][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:00,739][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:00,742][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:00,745][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:00,757][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:00,760][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:00,763][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:00,765][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:00,768][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:00,771][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:00,773][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:00,776][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:00,779][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:00,782][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:00,784][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:00,801][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:00,804][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:00,807][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:00,810][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:00,812][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:00,815][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:00,818][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:00,820][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:00,823][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:00,826][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:00,842][mllm.models.large_language_model_local][WARNING] - Response Given the values, you have a strong preference for hats and books, while Bob has a strong preference for balls. To maximize your points, you should propose to take all the hats and books, and leave the balls for Bob. Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:00,845][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:00,847][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:00,850][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:00,882][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:00,885][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:00,887][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:00,901][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:00,903][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:00,922][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:00,924][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:00,927][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:00,929][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:00,938][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:00,941][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:00,944][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:01,084][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it makes sense to propose a distribution that maximizes the value based on your higher valuation for hats and lower valuation for books and balls compared to Bob's. Here's a strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:01,779][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the fact that both Alice and I value the same items heavily, it makes sense to propose an allocation that maximizes our individual points. Since we both value the balls highly and the hats and books less, it might be strategic to try to secure more of the balls while still splitting the hats and books. Proposal: 10 hats, 10 books, 9 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:01,781][mllm.models.large_language_model_local][WARNING] - Response Given the new per-item values, where both you and Bob value hats and books at 10 and balls at 1, the optimal strategy would be to propose an equal split since the values are symmetric and both of you would benefit from coordinating to keep all items. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:01,907][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:01,953][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:01,956][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:01,959][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:01,961][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:01,964][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:01,966][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:01,969][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:01,972][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:02,000][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:02,004][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:02,007][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:02,010][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:02,013][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:02,015][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:02,041][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:02,043][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:02,046][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:02,049][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:02,052][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:02,090][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:02,094][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:02,165][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:02,168][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:02,170][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:02,173][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:02,194][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:02,197][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:02,200][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:02,202][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:02,205][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:02,208][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:02,721][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:02,724][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:02,747][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:02,749][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:02,752][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:02,755][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:02,774][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:02,777][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:02,781][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:02,783][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:02,795][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:02,798][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:02,800][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:02,803][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:02,806][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:02,820][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:02,823][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:02,826][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:02,829][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:02,832][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:02,835][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:02,838][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:02,841][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:02,843][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:02,846][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:02,849][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:02,857][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:02,861][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:02,863][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:02,866][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:02,868][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:02,871][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:02,873][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:02,875][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:02,878][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:02,885][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:02,888][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:02,890][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:02,892][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:02,895][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:02,897][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:02,899][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:02,905][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:02,907][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:02,909][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:02,929][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:02,931][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:02,950][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:02,953][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:02,956][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:02,958][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:02,960][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:02,962][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:03,054][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's important to focus on the items with higher personal value, which in this case are books and balls. A strategic proposal would be to offer a lower quantity of hats and a higher quantity of books and balls. Here's a possible proposal: Proposal: 5 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:03,141][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:03,262][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:03,265][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:03,268][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:03,270][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:03,287][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:03,289][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:03,292][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:03,295][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:03,309][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:03,312][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:03,314][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:03,332][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:03,335][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:03,337][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:03,340][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:03,355][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:03,358][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:03,360][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:03,377][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:03,380][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:03,383][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:03,385][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:03,388][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:03,390][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:03,401][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that you and Bob have complementary preferences. You value books the most, while Bob values hats the most. Distributing the items in a way that maximizes the benefit based on these values would be optimal. Here's a strategic proposal: Proposal: 10 hats, 10 balls, 10 books This proposal takes into account that you get the books (maximum value for you) and Bob gets the hats (maximum value for him), with the remaining items split between you. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:03,403][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:03,406][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:03,409][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:03,411][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:03,425][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:03,427][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:03,430][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:03,433][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:03,435][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:03,438][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:03,441][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:03,449][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:03,451][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:03,454][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:03,456][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:03,459][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:03,461][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:03,471][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:03,474][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:03,476][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:03,479][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:03,481][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:03,483][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:03,486][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:03,488][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:03,495][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:03,498][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:03,500][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:03,502][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:03,526][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:03,528][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:03,531][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:03,533][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:03,544][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:03,550][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:03,553][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:03,565][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:03,568][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:03,570][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:03,573][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:03,588][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:03,591][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:03,593][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:03,596][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:03,598][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:03,601][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:03,604][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:03,606][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:03,618][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:03,620][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:03,623][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:03,626][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:03,629][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:03,631][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:03,634][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:03,636][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:03,639][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:03,653][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:03,656][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:03,687][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric nature of the per-item values and the previous rounds' outcomes, it's clear that proposing to keep all the items will result in both you and Bob earning 60 points each, since the sum of your proposals exceeds the total number of items, leading to a proportional split. However, to maximize points more effectively, we should consider proposing a split that aligns with our higher values. Since I value hats and books more, and Bob values balls more, let's propose a split that takes this into account while being mindful of Bob's potential strategy. Proposal: 10 hats, 10 books, 7 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:03,689][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:03,694][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:03,699][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:03,704][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:03,713][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:03,718][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:03,723][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:03,727][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:03,735][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:03,739][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:03,744][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:03,753][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:03,757][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:03,761][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:03,766][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:03,773][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:03,777][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:03,781][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:03,788][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:03,792][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:03,796][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:03,799][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:03,803][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:03,813][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:03,817][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:03,819][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:03,822][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:03,826][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:03,830][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:03,837][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:03,841][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:03,844][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:03,848][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:03,860][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:03,864][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:03,867][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:03,871][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:03,875][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:03,878][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:03,882][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:03,885][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:03,891][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:03,895][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:03,898][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:03,901][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:03,905][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:03,908][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:03,911][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:03,917][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:03,920][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:03,923][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:03,926][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:03,930][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:03,944][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:03,948][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:03,951][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:03,952][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:03,970][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:03,974][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:03,996][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:04,000][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:04,004][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:04,006][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:04,044][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:04,046][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:04,048][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:04,049][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:04,051][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:04,135][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:04,137][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:04,140][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:04,142][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:04,145][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:04,147][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:04,149][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:04,222][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:04,226][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:04,228][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:04,231][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:04,234][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:04,236][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:04,239][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:04,241][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:04,244][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:04,390][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:04,723][mllm.models.large_language_model_local][WARNING] - Response Given that both you and Bob value hats and books highly, and balls less, a cooperative strategy might be beneficial. Since the values are the same for both of you, splitting the items equally would likely lead to the highest total points. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:04,852][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:04,882][mllm.models.large_language_model_local][WARNING] - Response Given the values, it appears that both you and Bob value books most highly. However, to ensure that we capture as much value as possible, we should consider the symmetry and the proportional distribution. Here's a strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:05,131][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's strategically beneficial to propose taking the majority of items where you have a high value and leave the rest for Bob where he has a high value. Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:05,133][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:05,137][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:05,180][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:05,329][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, we should aim to claim more of the items we value more. Since I value books the most at 10 and balls are second at 10, while Bob values hats the most and books second, a strategic proposal would be to take as many books and balls as possible. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:05,616][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:05,619][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:05,621][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:05,624][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:05,626][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:05,629][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:05,632][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:05,634][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:05,637][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:05,640][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:05,681][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric nature of the per-item values and the fact that both Alice and Bob have similar valuations for different types of items, it's strategic to propose an equal split of the items to ensure fairness and maximize the points based on the individual valuations. Here's the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:05,684][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:05,686][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:05,688][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:05,690][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:05,692][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:05,749][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:05,778][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:05,782][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:05,785][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:05,788][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:05,809][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:05,812][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:05,815][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:05,819][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:05,821][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:05,824][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:05,843][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:05,846][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:05,848][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:05,850][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:05,853][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:05,855][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:05,858][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:05,861][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:05,864][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:05,872][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the high value of books and balls for both parties, the optimal strategy would be to propose an equal split to avoid any proportional allocation issues. However, since both of us have similar per-item values for books and balls, proposing a fair split is beneficial. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:05,875][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:05,877][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:05,880][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:05,882][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:05,884][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:05,887][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:05,889][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:05,891][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:05,958][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:05,961][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:05,964][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:05,966][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:05,969][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:05,970][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:05,973][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:05,976][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:06,131][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:06,134][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:06,298][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:06,469][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:06,554][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:06,557][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:06,560][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:06,563][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:06,565][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:06,568][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:06,571][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:06,574][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:06,577][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:06,639][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:06,722][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:06,725][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:06,804][mllm.models.large_language_model_local][WARNING] - Response Given the value distributions, where you have a high value for hats and books, and Alice has a high value for hats and books, but the items are to be split equally, a strategic proposal would be to maximize your high-value items while ensuring Alice does not propose a higher total. Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:06,807][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:06,810][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:06,812][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:06,815][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:06,819][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:06,821][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:06,824][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:06,827][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:06,884][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:06,887][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:06,890][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:06,892][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:06,895][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:06,898][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:06,900][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:06,903][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:06,906][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:06,908][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:06,912][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:06,915][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:06,918][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:06,921][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:06,963][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:06,969][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:06,976][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:06,979][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:06,981][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:06,984][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:07,038][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:07,119][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:07,121][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:07,122][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:07,124][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:07,199][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:07,201][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:07,203][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:07,206][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:07,207][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:07,282][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:07,847][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric and competitive nature of the per-item values between you and Bob, a strategic proposal would be to maximize the distribution of items in a way that both you and Bob receive a fair share, considering the high values both of you have for books and balls. A balanced proposal might be: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:08,018][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and the symmetric nature of the situation, I will propose to allocate the items in a way that maximizes my utility based on my values for the items. Since I value books the most (10), followed by hats (1), and then balls (1), and Alice values balls the most (10), hats second (10), and then books (1), the optimal proposal would be to distribute the items according to our individual valuations. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:08,022][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:08,025][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:08,028][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:08,030][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:08,033][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:08,035][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:08,038][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:08,863][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:08,866][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:08,868][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:08,870][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:08,871][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:08,873][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:08,874][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:08,875][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:08,876][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:08,878][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:08,879][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:08,945][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:08,947][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:08,950][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:08,953][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:08,956][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:08,958][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:08,961][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:08,964][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:08,967][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:08,969][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:08,971][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:08,974][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:08,976][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:08,978][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:08,981][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:08,983][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:08,985][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:08,988][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:08,990][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:08,993][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:08,997][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:08,999][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:09,001][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:09,122][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:09,124][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:09,215][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:09,218][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:09,223][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:09,226][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:09,302][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:09,306][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:09,391][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:09,394][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:09,492][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:09,497][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:09,662][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:09,666][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:09,669][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:09,750][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:09,753][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:09,755][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:09,759][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:09,761][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:09,834][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:09,838][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:09,840][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:09,907][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:09,910][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:09,912][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:09,985][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:09,986][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:09,986][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:09,987][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:09,988][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:09,989][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:09,990][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:09,991][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:09,991][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:09,993][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:10,071][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:10,072][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:10,244][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:10,247][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:10,250][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:10,253][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:10,329][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:10,333][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:10,336][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:10,337][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:10,340][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:10,342][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:10,344][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:10,345][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:10,347][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:10,348][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:10,610][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Bob have complementary preferences. You value books the most and hats the least, while Bob values hats the most and books the least. Given this, a cooperative strategy where we allocate items based on their values could be effective. Here's a proposal that takes this into account: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:10,613][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:10,917][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in per-item values and the fair proposal by Bob in the previous rounds, a fair and strategic proposal would be to split the items evenly. This maximizes the likelihood of both parties receiving a fair share, which is crucial for maintaining cooperation and ensuring mutual benefit. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:10,964][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's optimal to propose a split that maximizes the points for both yourself and Bob, as we have seen in previous rounds that proposing an equal split leads to equal earnings. Since both you and Bob value books at 10 and balls at 10, we should ensure that we both get enough books and balls to maximize our points. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:11,078][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:11,201][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:11,203][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:11,204][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:11,205][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:11,206][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:11,207][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:11,268][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:11,269][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:11,270][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:11,271][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:11,273][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:11,274][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:11,275][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:11,276][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:11,277][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:11,278][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:11,279][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:11,294][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:11,296][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:11,297][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:11,298][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:11,299][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:11,300][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:11,301][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:11,302][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:11,303][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:11,304][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:11,305][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:11,305][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:11,306][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:11,306][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:11,308][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:11,309][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:11,310][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:11,360][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric nature of the per-item values and the fact that both you and Bob have equivalent valuations for the items, the fairest and most optimal strategy would be to propose an equal split of the items. This way, both parties maximize their individual points while ensuring fairness. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:11,549][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:11,570][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:11,653][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:11,657][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:11,659][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:11,699][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:11,703][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:11,707][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:11,711][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:11,746][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:11,752][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:11,766][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:11,768][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:11,771][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:11,774][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:11,778][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:11,780][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:11,782][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:11,791][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:11,794][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:11,796][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:11,798][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:11,801][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:11,804][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:11,806][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:11,807][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:11,809][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:11,812][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:11,815][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:11,816][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:11,820][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:11,821][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:11,823][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:11,824][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:11,825][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:11,826][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:11,828][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:12,058][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the fact that both you and Bob value the same items, proposing an equal split might be a strategic choice. However, since Bob values books and balls more than you do, it might be beneficial to propose slightly more books and balls for yourself to take advantage of their higher value. Proposal: 10 hats, 9 books, 9 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:12,075][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:12,079][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:12,082][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:12,093][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:12,098][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:12,101][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:12,112][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:12,114][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:12,117][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:12,131][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:12,169][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:12,171][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:12,174][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:12,177][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:12,179][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:12,182][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:12,187][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:12,189][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:12,190][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:12,193][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:12,195][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:12,196][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:12,198][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:12,199][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:12,212][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:12,214][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:12,216][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:12,218][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:12,219][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:12,221][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:12,225][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:12,228][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:12,232][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:12,235][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:12,238][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:12,240][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:12,262][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:12,264][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:12,266][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:12,267][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:12,268][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:12,271][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:12,273][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:12,275][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:12,277][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:12,285][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:12,287][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:12,394][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:12,410][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:12,413][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:12,416][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:12,430][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:12,435][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:12,449][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:12,454][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:12,456][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:12,468][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:12,550][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:12,635][mllm.models.large_language_model_local][WARNING] - Response Given the values where you have high value for books and Bob has high value for hats, a more strategic approach might be to offer just enough to satisfy Bob's valuation for hats and balls, and take most of the books. Here's a proposal: Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:12,642][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:12,648][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:12,655][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:12,661][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:12,716][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:12,722][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:12,728][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:12,733][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:12,739][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:12,743][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:12,748][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:12,753][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:12,757][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:12,794][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:12,797][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:12,801][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:12,804][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:12,882][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:12,886][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:12,964][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:12,968][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:12,971][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:12,975][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:12,978][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:12,980][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:12,983][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:12,986][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:13,056][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:13,060][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:13,063][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:13,378][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the fact that both Alice and Bob have similar values for the items, it's optimal to propose splitting all items evenly. This ensures that we maximize our expected value based on the given per-item values. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:13,715][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:13,722][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:13,815][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:14,102][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:14,209][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the fact that both you and Bob have identical valuation for hats and balls, but different valuations for books, a cooperative strategy might be effective. Since the per-item values are similar, splitting the items evenly could be a reasonable approach to maximize points. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:14,263][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:14,276][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and the symmetry in the situation, the best strategy is to mirror Bob's preferences. Since Bob values hats the most and balls the least, proposing all 10 hats, no books, and no balls would align with maximizing your points based on your per-item values. Proposal: 10 hats, 0 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:14,291][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:14,294][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:14,298][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:14,300][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:14,341][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:14,345][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:14,369][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:14,371][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:14,384][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:14,427][mllm.models.large_language_model_local][WARNING] - Response Given that Alice values hats and balls more and you value books the most, you should propose a distribution that maximizes your points from books while still offering enough hats and balls to avoid any non-proportional distribution. Here's a strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:14,442][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:14,445][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:14,447][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:14,462][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:14,465][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:14,468][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:14,469][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:14,519][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:14,652][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Bob value hats and balls less than books. To maximize your points, you might want to propose a bit less of the items you value less (hats and balls) and more of the item you value highly (books). Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:14,794][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:14,942][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:14,953][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:14,988][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:14,989][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:15,001][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:15,038][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:15,040][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:15,049][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:15,050][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:15,052][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:15,053][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:15,146][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:15,158][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:15,192][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:15,194][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:15,204][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:15,240][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:15,242][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:15,253][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:15,256][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:15,258][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:15,261][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:15,354][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:15,366][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:15,402][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:15,407][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:15,413][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:15,454][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:15,460][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:15,468][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:15,473][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:15,477][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:15,483][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:15,549][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric and competitive nature of the per-item values, where my values favor hats and books while Bob's values favor hats and books in reverse, the optimal strategy is to propose keeping the items in a way that both sides receive a fair share. Since the total quantity of items is 30 and both agents are likely to propose the same amount to avoid splitting, I will propose a competitive split that balances the distribution while maximizing my value. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:16,530][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and the symmetry in the situation, it's strategically optimal to propose an equal split that respects the higher value each agent places on different items. Since both agents value books at 1 and balls at 1, and we value hats more (10) while Bob values hats less (1), a fair and reasonable proposal would be to split the items as evenly as possible. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:16,702][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:17,089][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:17,101][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:17,273][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:17,447][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:18,046][mllm.models.large_language_model_local][WARNING] - Response Given the values, I should propose a distribution that maximizes my points. Since I value hats at 1, books at 10, and balls at 10, and Bob values hats at 10, books at 10, and balls at 1, we should both propose to keep all items to ensure we get the full value of what we consider most valuable. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:18,217][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:18,613][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:18,789][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:18,898][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in per-item values and the previous outcomes, it makes sense to propose an equal split to maximize the points. Here is the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:18,968][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:19,685][mllm.models.large_language_model_local][WARNING] - Response Given the values, proposing all items might not be the optimal strategy since the distribution of per-item values between us and Bob is such that books and balls are more valuable for Bob. However, since both of us are proposing to take all items, we might as well take a conservative approach that balances our values. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:20,312][mllm.models.large_language_model_local][WARNING] - Response Given the high value of books and balls for both of us, and the potential for proportional allocation if our proposals sum exceed the total items, I will propose to take the majority of the high-value items to maximize my points. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:20,705][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:20,878][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:21,057][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:21,450][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:21,461][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:53:21,621][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:21,632][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:53:21,791][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:21,802][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:53:22,867][__main__][INFO] - Number of regex retries in iteration 564: 1725 [2025-11-13 06:53:22,867][__main__][INFO] - agents played in iteration 564 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 06:53:23,934][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:53:23,953][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:53:23,971][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:53:23,990][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:53:23,991][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 06:53:23,991][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 06:53:24,812][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 06:53:25,263][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 06:53:25,767][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 06:53:26,263][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 06:53:26,773][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 06:53:27,270][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 06:53:27,770][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 06:53:28,266][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 06:53:28,763][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 06:53:29,267][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 06:53:29,763][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 06:53:30,261][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 06:53:30,761][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 06:53:31,261][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 06:53:31,756][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 06:53:32,256][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 06:53:32,752][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 06:53:33,266][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 06:53:33,757][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 06:53:34,253][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 06:53:34,747][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 06:53:35,240][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 06:53:35,732][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 06:53:36,223][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 06:53:36,712][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 06:53:37,205][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 06:53:37,696][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 06:53:38,186][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 06:53:38,677][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 06:53:39,167][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 06:53:39,664][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 06:53:40,154][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 06:53:40,646][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 06:53:41,150][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 06:53:41,644][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 06:53:42,136][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 06:53:42,633][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 06:53:43,126][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 06:53:43,614][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 06:53:44,106][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 06:53:44,596][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 06:53:45,089][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 06:53:45,585][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 06:53:46,077][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 06:53:46,568][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 06:53:47,057][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 06:53:47,546][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 06:53:48,036][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 06:53:48,527][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 06:53:49,017][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 06:53:49,505][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 06:53:49,997][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 06:53:50,487][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 06:53:50,981][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 06:53:51,470][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 06:53:51,963][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 06:53:52,451][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 06:53:52,943][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 06:53:53,432][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 06:53:53,923][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 06:53:54,413][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 06:53:54,902][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 06:53:55,391][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 06:53:55,882][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 06:53:56,374][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10892 tokens. [2025-11-13 06:53:57,150][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.24%, Current % of VRAM taken: 59.69%, Block Peak % of device VRAM: 62.32%, ΔTime: 00:00:32 [2025-11-13 06:53:57,866][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 06:53:57,868][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 06:53:57,870][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 06:53:59,877][__main__][INFO] - Iteration 565 took 1m 31s (59.57% Gen, 38.24% Train). Generation: 54s, Training: 35s. Estimated remaining time: 67h 32m 50s. Estimated total time: 76h 17m 8s. Time estimates for 10 more iterations: 15m 15s, 100 more iterations: 2h 32m 34s, 500 more iterations: 12h 42m 51s. [2025-11-13 06:53:59,882][__main__][INFO] - Starting iteration 565. [2025-11-13 06:54:00,351][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 56 and human policies 1. [2025-11-13 06:54:00,351][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 06:54:18,122][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,146][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,149][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,152][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,155][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,157][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,160][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,161][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,164][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,166][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,168][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,170][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,172][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,175][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,177][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,178][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,180][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,182][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,189][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,191][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,193][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,195][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,196][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,196][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,197][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,198][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,198][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,198][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,199][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,199][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,201][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,202][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,203][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,204][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,208][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,209][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,211][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,212][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,213][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,215][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,216][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,217][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,219][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,220][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,221][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,222][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,224][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,225][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,227][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,228][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,229][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,230][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,231][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,232][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,236][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,238][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,239][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,240][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,242][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,243][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,245][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,246][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,247][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,249][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,250][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,251][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,252][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,253][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,254][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,255][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,256][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,257][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,258][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,260][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,261][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,262][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,263][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,264][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,266][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,267][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,268][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,270][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,271][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,272][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,273][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,274][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,275][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,276][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,277][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,279][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,280][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,281][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,283][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,284][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,285][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,286][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,290][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,291][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,292][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:18,451][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,467][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,470][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,472][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,474][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,475][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,486][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,488][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,491][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,494][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,496][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,499][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,501][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,504][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,506][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,507][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,509][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,510][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,528][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,529][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,531][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,533][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,534][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,535][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,536][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,538][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,539][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,541][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,542][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,544][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,546][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,546][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,547][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,549][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,550][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,550][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,551][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,552][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,553][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,554][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,556][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,557][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,558][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,560][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,561][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,563][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,564][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,566][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,567][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,568][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,570][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,572][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,574][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,575][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,576][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,577][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,578][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,579][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,580][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,582][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,583][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,584][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,592][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,594][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,595][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,596][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,597][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,598][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,599][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,600][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,601][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,601][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,602][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,603][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,604][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,605][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,607][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,639][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,641][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,642][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,643][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,644][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,646][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,647][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,648][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,649][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,650][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,652][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,653][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,654][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,655][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,656][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,659][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,660][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,661][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,662][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,663][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,664][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:18,822][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:18,860][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:18,865][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:18,870][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:18,872][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:18,876][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:18,879][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:18,882][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:18,885][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:18,892][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:18,894][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:18,896][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:18,899][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:18,902][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:18,904][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:18,906][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:18,909][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:18,911][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:18,962][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:19,883][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:19,884][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:19,885][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:19,947][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:19,951][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:19,954][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:19,957][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:19,959][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:19,972][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:19,973][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:19,976][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:19,979][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:19,982][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:19,985][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:19,987][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:19,995][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:19,997][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:20,000][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:20,002][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:20,003][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:20,006][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:20,008][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:20,009][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,012][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:20,020][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:20,022][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:20,023][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:20,025][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:20,027][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:20,028][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:20,030][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:20,032][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:20,033][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:20,035][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:20,044][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:20,046][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:20,047][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:20,050][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:20,052][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:20,053][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:20,054][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:20,055][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:20,056][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:20,058][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:20,060][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:20,062][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:20,063][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:20,065][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:20,066][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:20,067][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:20,068][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:20,071][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:20,072][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:20,073][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:20,075][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:20,076][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:20,077][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:20,079][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:20,080][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:20,081][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:20,082][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:20,083][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:20,085][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:20,086][mllm.models.large_language_model_local][WARNING] - Response Proposal: 5 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:20,087][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:20,088][mllm.models.large_language_model_local][WARNING] - Response Proposal: 5 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:20,089][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:20,091][mllm.models.large_language_model_local][WARNING] - Response Proposal: 5 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:20,092][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:20,096][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:20,097][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:20,098][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:20,100][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:20,101][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:20,102][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:20,104][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:20,105][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:20,106][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:20,114][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:20,257][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:20,282][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:20,285][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:20,287][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:20,289][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:20,290][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:20,290][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:20,304][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:20,306][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:20,308][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:20,311][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:20,313][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:20,315][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:20,316][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:20,318][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:20,320][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,324][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:20,327][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:20,329][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:20,331][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,333][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,342][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:20,344][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:20,365][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,367][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,369][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,371][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,372][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,373][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,380][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,381][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,383][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,384][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,386][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,387][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,389][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,399][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,401][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,403][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,404][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,405][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,407][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,408][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:20,412][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,413][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,415][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,418][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,420][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,422][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,423][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,424][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,426][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,427][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,428][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,429][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,431][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,432][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,438][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,440][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,440][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,441][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,443][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,444][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,445][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,447][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,448][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,449][mllm.models.large_language_model_local][WARNING] - Response Proposal: 5 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,459][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,461][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,462][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,463][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,464][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,466][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,467][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,469][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,469][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,470][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,472][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,473][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,474][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,475][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,476][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,477][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,479][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,480][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,482][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,483][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,486][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,488][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,489][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,490][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,491][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,492][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,493][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,495][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,496][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,497][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,498][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,503][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,610][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,626][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,628][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,630][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,632][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,644][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,646][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,663][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,666][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,668][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,670][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,671][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,674][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,676][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,677][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,680][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,685][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:20,690][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,691][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,693][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:20,702][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:20,706][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,708][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:20,710][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:20,721][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:20,726][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:20,730][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:20,734][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:20,738][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:20,742][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:20,747][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:20,750][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:20,753][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:20,755][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:20,758][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:20,761][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:20,764][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:20,767][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:20,770][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:20,772][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:20,775][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:20,777][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:20,780][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:20,782][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:20,784][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:20,786][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:20,789][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:20,796][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:20,798][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:20,801][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:20,804][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:20,805][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:20,807][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:20,810][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:20,811][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:20,815][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:20,817][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:20,820][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:20,823][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:20,826][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:20,828][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:20,830][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:20,832][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:20,834][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:20,837][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:20,839][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:20,869][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:20,871][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:20,874][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:20,875][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:20,877][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:20,879][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:20,881][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:20,883][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:20,885][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:20,887][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:20,889][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:20,892][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:20,894][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:20,962][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:20,965][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:20,969][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:20,973][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:20,975][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:20,978][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:20,981][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:20,985][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:20,988][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:20,991][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:20,994][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:20,997][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:20,999][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:21,003][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:21,005][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:21,051][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:21,054][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:21,057][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:21,222][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:21,249][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:21,255][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:21,260][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:21,263][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:21,295][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:21,298][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:21,301][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:21,347][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:21,353][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:21,394][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:21,400][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:21,403][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:21,406][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:21,408][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:21,441][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:21,445][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:21,449][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:21,484][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:21,487][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:21,729][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:22,829][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:22,874][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:22,877][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:22,880][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:22,882][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:22,902][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:22,903][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:22,941][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:22,962][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:22,963][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:22,965][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:22,967][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:22,968][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:22,970][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:22,972][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:22,991][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:22,992][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:22,993][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:22,994][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:22,994][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:22,995][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:22,995][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:22,996][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:22,996][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:23,015][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:23,016][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,018][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:23,019][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:23,038][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:23,039][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:23,040][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:23,041][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:23,042][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:23,044][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:23,046][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:23,047][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:23,062][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:23,063][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:23,065][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:23,067][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:23,068][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:23,068][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:23,069][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:23,070][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:23,070][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:23,085][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:23,087][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:23,088][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:23,090][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:23,091][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:23,093][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:23,094][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:23,095][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:23,097][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:23,098][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:23,100][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:23,101][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:23,103][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:23,105][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:23,111][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:23,112][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:23,114][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:23,115][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:23,117][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:23,118][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:23,120][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:23,121][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:23,123][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:23,132][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:23,134][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:23,154][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:23,155][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:23,157][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:23,158][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:23,159][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:23,160][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:23,162][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:23,163][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:23,164][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:23,166][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:23,168][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:23,177][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:23,178][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:23,196][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:23,197][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:23,199][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:23,200][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:23,201][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:23,202][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:23,202][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:23,217][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:23,218][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:23,219][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:23,221][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:23,235][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:23,253][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:23,254][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:23,270][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,272][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,289][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,291][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,293][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,295][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:23,297][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:23,306][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,308][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,324][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,342][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,344][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,345][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,347][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,349][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,351][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,353][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,354][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,355][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,358][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,359][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,360][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,361][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,362][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,363][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,364][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,385][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,387][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:23,389][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,390][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,392][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 1 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,394][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,396][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,397][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,406][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,408][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,411][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,412][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,415][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,416][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,417][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,426][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,427][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,428][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,430][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,433][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,435][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,437][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,438][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,445][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,446][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,447][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,448][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,449][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,449][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,451][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,454][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,456][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,457][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,458][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,458][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,459][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,461][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,462][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,466][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,468][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,469][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,470][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,470][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,471][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,472][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,472][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,495][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,496][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,498][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,517][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,519][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,520][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,521][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,522][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,524][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,526][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,528][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,530][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,535][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,536][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,560][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,562][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,563][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,565][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,566][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,568][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,577][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,578][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,579][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,580][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,582][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,596][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,617][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,619][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:23,635][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:23,654][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:23,658][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:23,662][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:23,666][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,667][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:23,672][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:23,675][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:23,692][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:23,713][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:23,717][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:23,721][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:23,724][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:23,727][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:23,730][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:23,733][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:23,735][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:23,738][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:23,741][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:23,745][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:23,748][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:23,751][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:23,753][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:23,755][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:23,757][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:23,759][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:23,762][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:23,763][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:23,765][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:23,767][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:23,771][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:23,774][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:23,784][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:23,787][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:23,791][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:23,793][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:23,795][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:23,797][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:23,799][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:23,801][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:23,804][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:23,807][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:23,809][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:23,811][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:23,813][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:23,815][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:23,818][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:23,819][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:23,821][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:23,823][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:23,867][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:23,870][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:23,873][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:23,875][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:23,878][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:23,880][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:23,882][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:23,884][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:23,886][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:23,889][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:23,891][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:23,894][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:23,896][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:23,898][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:23,901][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:23,903][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:23,905][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:23,908][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:23,910][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:23,912][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:23,915][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:23,925][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:23,927][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:23,956][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:23,959][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:23,962][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:23,965][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:23,967][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:23,970][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:23,987][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:23,991][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:23,993][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:23,995][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:23,998][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:24,017][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:24,020][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:24,022][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:24,024][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:24,026][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:24,029][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:24,032][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:24,046][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:24,048][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:24,051][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:24,078][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:24,105][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:24,240][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:24,243][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:24,519][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,426][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,507][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,551][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,554][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,558][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,576][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,579][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,601][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,603][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,645][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,648][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,668][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,669][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,670][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,672][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,675][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,693][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,695][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,696][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,697][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,698][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,699][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,700][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,703][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,713][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,714][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,716][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,718][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:25,720][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,721][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,738][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,739][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,740][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,742][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,744][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,745][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,747][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,749][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,750][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,766][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,767][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,769][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,769][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,770][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,771][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,772][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,773][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,790][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,791][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,792][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,793][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,795][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,796][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,798][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,799][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,800][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,814][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,816][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,818][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,819][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,820][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,822][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,836][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,837][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,838][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,839][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,840][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,842][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,856][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,858][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,859][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,860][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,862][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,863][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,864][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,880][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,883][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,885][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,886][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,887][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,887][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,888][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,900][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,901][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,903][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,905][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,907][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,908][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,909][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,921][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,922][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,924][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,927][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,928][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,940][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:25,941][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,943][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,960][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:25,976][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:25,977][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:25,979][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:25,995][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:25,996][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:25,997][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:25,998][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:26,014][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:26,016][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:26,034][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:26,036][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:26,052][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:26,054][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:26,056][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:26,057][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:26,058][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:26,070][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:26,072][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:26,074][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:26,076][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:26,077][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:26,079][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:26,081][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:26,083][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:26,084][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:26,092][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:26,093][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:26,095][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,097][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:26,098][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:26,099][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:26,100][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:26,112][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:26,113][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:26,115][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:26,116][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:26,117][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:26,119][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:26,120][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:26,121][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:26,132][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:26,133][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:26,134][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:26,135][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:26,136][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:26,137][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:26,138][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:26,140][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:26,150][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:26,152][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:26,153][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:26,155][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:26,156][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:26,157][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:26,158][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:26,159][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:26,161][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:26,170][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:26,172][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:26,173][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:26,175][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:26,176][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:26,192][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:26,194][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:26,195][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:26,197][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:26,198][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:26,200][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:26,212][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:26,213][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:26,214][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:26,216][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:26,217][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:26,219][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:26,220][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:26,233][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:26,234][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:26,235][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:26,235][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:26,236][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:26,236][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:26,237][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:26,237][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:26,256][mllm.models.large_language_model_local][WARNING] - Response Given the values and Bob's strategy in previous rounds, it seems he values hats significantly more than youdo. To maximize my points, I should propose a balanced distribution that still takes into account my higher value for balls. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:26,258][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:26,259][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:26,261][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:26,262][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:26,264][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:26,265][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:26,275][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:26,276][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:26,277][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:26,278][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:26,279][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:26,296][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,299][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:26,301][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:26,315][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,335][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,338][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,341][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,354][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,357][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,360][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:26,361][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:26,373][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,376][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,394][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,396][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,413][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,416][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,420][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,425][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,428][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,431][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,436][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,439][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,441][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,444][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,447][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,450][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,453][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,455][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,458][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,460][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,464][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,466][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,468][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,471][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,473][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,475][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,489][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,491][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,493][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,495][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,498][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,500][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,502][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,543][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,546][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,549][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,552][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,555][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,558][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,560][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,562][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,568][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,570][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,573][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,575][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,577][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,579][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,581][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,583][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,597][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,599][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,601][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,604][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,606][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,631][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,634][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,637][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,639][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,642][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,644][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,661][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,663][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,666][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,669][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,671][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,674][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,676][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,678][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,694][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,697][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,699][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,701][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,704][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,706][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,709][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,712][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,722][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:26,723][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,725][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,727][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,730][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,732][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,734][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,757][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,760][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,763][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,765][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,792][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,795][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,925][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:26,930][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:27,200][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:27,941][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:28,125][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,218][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,285][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,286][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,288][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,323][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,326][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,345][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,347][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,387][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,388][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,419][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,422][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,424][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,426][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,444][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,447][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,449][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,452][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,453][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,454][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,456][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,468][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,469][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,471][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:28,472][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,474][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,498][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,500][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,502][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,504][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,506][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,508][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,510][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,512][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,514][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,524][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,526][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,527][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,528][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,528][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,548][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,549][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,551][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,553][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,555][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,557][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,559][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,560][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,562][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,564][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,575][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,577][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,579][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,580][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,582][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,583][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,584][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,586][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,600][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,602][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,603][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,604][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,606][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,608][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,625][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,627][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,628][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,629][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,645][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,647][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,648][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,649][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,650][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,651][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,652][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,654][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,669][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,670][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,672][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,673][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,674][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,676][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,678][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,692][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,693][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,695][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,696][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,697][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,698][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,734][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,736][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,738][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,739][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,741][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:28,742][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,821][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:28,824][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:28,903][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:28,907][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:28,982][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:28,986][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:28,989][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:28,992][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:29,063][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:29,142][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:29,145][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:29,223][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:29,225][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:29,227][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:29,304][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:29,308][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:29,309][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:29,310][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:29,312][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:29,314][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:29,384][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:29,387][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:29,389][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:29,395][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:29,397][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:29,399][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:29,401][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:29,402][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:29,467][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:29,470][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:29,473][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:29,475][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:29,478][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:29,549][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:29,552][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:29,555][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:29,557][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:29,559][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:29,628][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:29,631][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:29,634][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:29,638][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:29,640][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:29,642][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:29,644][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:29,646][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:29,649][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:29,651][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:29,706][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:29,709][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:29,710][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:29,712][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:29,713][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:29,714][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:29,716][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:29,719][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:29,721][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:29,783][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:29,787][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:29,789][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:29,791][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:29,792][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:29,862][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:29,865][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:29,868][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:29,871][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:29,940][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:29,943][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:29,946][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:29,950][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:29,952][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:29,954][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:29,957][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:29,959][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:30,020][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:30,022][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:30,025][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:30,027][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:30,029][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:30,031][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:30,033][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:30,036][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:32,923][mllm.models.large_language_model_local][WARNING] - Response Given the updated per-item values where both you and Bob value hats and books highly, and balls less, it's important to ensure we maximize our points by claiming the items we value more. Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:33,016][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the equal split of items, a fair and rational approach would be to propose an equal share of each item to maximize the total points. Here's the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:33,226][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:33,230][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:33,232][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:33,282][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:33,286][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:33,289][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:33,291][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:33,295][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:33,342][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:33,344][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:33,345][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:33,347][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:33,401][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:33,405][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:33,408][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:33,411][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:33,413][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:33,486][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:33,490][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:33,493][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:33,551][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:33,555][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:33,558][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:33,561][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:33,564][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:33,565][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:33,603][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:33,606][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:33,610][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:33,613][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:33,616][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:33,618][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:33,621][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:33,681][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:33,684][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:33,687][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:33,691][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:33,694][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:33,759][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:33,762][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:33,766][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:33,770][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:33,774][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:33,777][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:33,780][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:33,783][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:33,787][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:33,823][mllm.models.large_language_model_local][WARNING] - Response Given Alice's per-item values, it seems she values balls the most, hats the least, and books in the middle. To maximize my points, I should focus on getting the balls since they are worth the most to me. Here's my proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:33,826][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:33,829][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:33,832][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:33,835][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:33,908][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:33,912][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:33,915][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:33,918][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:33,922][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:33,925][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:33,971][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:33,975][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:33,977][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:33,981][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:33,984][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:33,987][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:33,990][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:33,993][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:33,994][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:33,995][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:34,050][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:34,054][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:34,057][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:34,059][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:34,063][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:34,134][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:34,138][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:34,141][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:34,143][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:34,146][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:34,211][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:34,215][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:34,218][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:34,220][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:34,223][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:34,230][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:34,233][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:34,236][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:34,238][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:34,272][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:34,275][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:34,355][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:34,358][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:34,362][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:34,366][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:34,407][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:34,452][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:34,455][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:34,460][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:34,463][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:34,466][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:34,495][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:34,499][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:34,502][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:34,506][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:34,508][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:35,944][mllm.models.large_language_model_local][WARNING] - Response Given the new values, where both you and Alice have a value of 1 for hats and 10 for books, and a value of 1 for balls, the optimal strategy would be to ensure that we both receive the items we value highly. Since we have the same valuations, we should propose splitting the books, which we both value at 10. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:35,948][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:35,952][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:36,043][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:36,114][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:36,116][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:36,198][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:36,201][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:36,205][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:36,208][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:36,241][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:36,245][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:36,302][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:36,306][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:36,309][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:36,312][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:36,314][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:36,318][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:36,320][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:36,321][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:36,323][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:36,359][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:36,363][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:36,366][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:36,368][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:36,450][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:36,453][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:36,457][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:36,460][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:36,461][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:36,464][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:36,466][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:36,527][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:36,531][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:36,534][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:36,607][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:36,611][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:36,614][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:36,617][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:36,619][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:36,622][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:36,625][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:36,628][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:36,660][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:36,664][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:36,667][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:36,670][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:36,672][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:36,675][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:36,678][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:36,681][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:36,751][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:36,754][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:36,756][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:36,758][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:36,843][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:36,846][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:36,850][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:36,853][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:36,856][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:36,859][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:36,905][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:36,909][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:36,912][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:36,915][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:36,918][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:36,977][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:36,981][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:36,984][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:36,987][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:36,990][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:37,044][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:37,047][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:37,050][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:37,053][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:37,055][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:37,059][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:37,062][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:37,065][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:37,067][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:37,071][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:37,073][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:37,119][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:37,122][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:37,126][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:37,129][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:37,206][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:37,210][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:37,213][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:37,215][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:37,216][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:37,219][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:37,222][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:37,225][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:37,296][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:37,303][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:37,307][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:37,310][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:37,381][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:37,385][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:37,388][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:38,102][mllm.models.large_language_model_local][WARNING] - Response Given the high value of hats and books for both of us, and the low value of balls, we should propose to take all the hats and books we can to ensure we maximize our points based on our high per-item values for these items. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:38,415][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:39,480][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:40,392][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:40,482][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:40,575][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:40,579][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:40,582][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:40,585][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:40,589][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:40,592][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:40,666][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:40,670][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:40,674][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:40,677][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:40,680][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:40,684][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:40,686][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:40,687][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:40,755][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:40,758][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:40,761][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:40,764][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:40,766][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:40,851][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:40,855][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:40,859][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:40,862][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:40,863][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:40,865][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:40,942][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:40,946][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:40,950][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:40,953][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:40,956][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:41,035][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 ballots did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:41,038][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:41,042][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:41,045][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:41,048][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:41,130][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:41,134][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:41,137][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:41,141][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:41,144][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:41,147][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:41,150][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:41,152][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:41,153][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:41,155][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:41,158][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:41,222][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:41,226][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:41,228][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:41,231][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:41,313][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:41,316][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:41,319][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:41,322][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:41,325][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:41,386][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:41,390][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:41,393][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:41,397][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:41,400][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:41,403][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:41,406][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:41,446][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:41,450][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:41,477][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:41,480][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:41,484][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:41,487][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:41,490][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:41,532][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:41,536][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:41,538][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:41,542][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:41,604][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:41,606][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:41,610][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:41,613][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:41,616][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:41,619][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:41,623][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:41,626][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:41,669][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:41,672][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:41,676][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:41,678][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:41,681][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:41,755][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:41,758][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:41,762][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:41,838][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:41,841][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:41,844][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:41,847][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:42,005][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:42,434][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it appears that both hats and balls are much more valuable to Bob compared to you. Hats and books have similar low values for you, while balls are highly valuable for you. Books, however, are less valuable for Bob. Given this, a strategic proposal might be to prioritize getting as many balls as possible, while splitting the hats and books more evenly. Proposal: 5 hats, 5 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:42,513][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:42,516][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:42,518][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:42,521][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:42,523][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:42,526][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:42,529][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:42,532][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:42,605][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:42,608][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:42,610][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:42,613][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:42,690][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:42,693][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:42,696][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:42,698][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:42,701][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:42,774][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:42,860][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:42,863][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:44,184][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:44,188][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:44,277][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:44,280][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:44,283][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:44,336][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:44,339][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:44,342][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:44,345][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:44,348][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:44,351][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:44,354][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:44,356][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:44,430][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:44,434][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:44,437][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:44,439][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:44,442][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:44,516][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:44,519][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:44,521][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:44,524][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:44,527][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:44,530][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:44,610][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:44,613][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:44,616][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:44,618][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:44,621][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:44,624][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:44,695][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:44,698][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:44,701][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:44,704][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:44,706][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:44,780][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:44,781][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:44,784][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:44,787][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:44,878][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:44,882][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:44,884][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:44,891][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:44,897][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:44,904][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:44,963][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:44,966][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:44,969][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:44,971][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:44,974][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:44,977][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:44,983][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:44,989][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:44,996][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:45,003][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:45,009][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:45,015][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:45,022][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:45,028][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:45,035][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:45,041][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:45,046][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:45,052][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:45,057][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:54:45,063][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:45,066][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:45,071][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:45,074][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:45,077][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:45,080][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:45,083][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:45,085][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:45,123][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:45,125][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:45,128][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:45,130][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:45,206][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:45,209][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:45,212][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:45,287][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:45,290][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:45,293][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:45,296][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:45,298][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:45,301][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:45,304][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:45,381][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:45,385][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:45,456][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:45,532][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it is strategically beneficial to maximize the items with higher personal value. Since you value hats and balls more and Bob values books more, proposing to take all hats and balls, and splitting the books, is a good strategy. Proposal: 10 hats, 10 balls, 5 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:45,535][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:45,538][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:45,540][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:45,613][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:45,616][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:54:45,689][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:45,692][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:46,000][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:48,635][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:52,681][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:52,998][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:53,314][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:53,475][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:53,634][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:53,637][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:53,719][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:53,800][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:53,803][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:53,882][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:54,043][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:54,438][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:54,594][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:54,600][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:54,763][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:54,917][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:55,078][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:55,081][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:55,165][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:55,326][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:55,401][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:55,405][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:55,560][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:55,563][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:55,643][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:55,646][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:55,883][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:55,971][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:55,974][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:56,053][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:56,372][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:56,376][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:56,453][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:56,534][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:56,617][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:56,620][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:56,622][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:57,016][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:57,098][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:57,262][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:57,343][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:57,502][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:57,506][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:57,664][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:57,667][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:57,748][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:57,905][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:57,985][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:57,988][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:58,069][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:58,151][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:58,233][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:58,312][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:58,470][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:58,473][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:58,554][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:58,635][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:58,638][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:58,714][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:58,796][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:58,799][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:58,881][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:58,962][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:59,043][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:59,046][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:59,520][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:59,523][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:59,606][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:59,690][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:59,772][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:54:59,931][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:55:00,169][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:55:00,247][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:55:00,328][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:55:00,409][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:55:00,489][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:55:00,725][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:55:00,806][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:55:00,809][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:55:00,971][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:55:01,051][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:55:01,134][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:55:01,807][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:55:01,966][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:55:02,048][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:55:02,051][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:55:02,286][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:55:02,515][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:55:02,593][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:55:02,820][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:55:02,896][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:55:02,898][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:55:02,977][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:55:03,053][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:55:03,128][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:55:03,203][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:55:03,280][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:55:03,506][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:55:06,604][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:55:10,143][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:55:10,147][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:55:10,150][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:55:10,154][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:55:10,172][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:55:10,176][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:55:10,179][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:55:10,183][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:55:10,185][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:55:10,188][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:55:10,191][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:55:10,264][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:55:10,421][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:55:10,502][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:55:10,661][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:55:10,665][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:55:10,669][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:55:10,672][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:55:10,676][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:55:10,680][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:55:10,746][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:55:10,750][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:55:10,754][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:55:10,757][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:55:10,831][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:55:10,834][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:55:10,838][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:55:10,840][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:55:10,842][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:55:10,843][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:55:10,926][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:55:10,929][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:55:10,932][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:55:10,936][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:55:10,938][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:55:10,941][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:55:13,641][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:55:13,645][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:55:13,654][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:55:13,665][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:55:13,810][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:55:13,821][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:55:13,824][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:55:13,842][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:55:13,977][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:55:14,006][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:55:14,013][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:55:14,030][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:55:14,466][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:55:14,490][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:55:14,639][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:55:14,661][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:55:14,814][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:55:14,835][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:55:15,948][__main__][INFO] - Number of regex retries in iteration 565: 1862 [2025-11-13 06:55:15,949][__main__][INFO] - agents played in iteration 565 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 06:55:16,813][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:55:16,832][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:55:16,853][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:55:16,873][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:55:16,874][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 06:55:16,874][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 06:55:17,674][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 06:55:18,125][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 06:55:18,626][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 06:55:19,123][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 06:55:19,617][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 06:55:20,111][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 06:55:20,606][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 06:55:21,099][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 06:55:21,597][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 06:55:22,105][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 06:55:22,603][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 06:55:23,099][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 06:55:23,599][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 06:55:24,098][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 06:55:24,597][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 06:55:25,097][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 06:55:25,617][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 06:55:26,115][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 06:55:26,614][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 06:55:27,115][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 06:55:27,609][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 06:55:28,104][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 06:55:28,603][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 06:55:29,099][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 06:55:29,592][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 06:55:30,084][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 06:55:30,577][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 06:55:31,067][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 06:55:31,559][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 06:55:32,055][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 06:55:32,546][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 06:55:33,046][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 06:55:33,542][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 06:55:34,038][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 06:55:34,540][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 06:55:35,037][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 06:55:35,536][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 06:55:36,033][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 06:55:36,527][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 06:55:37,023][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 06:55:37,521][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 06:55:38,016][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 06:55:38,516][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 06:55:39,014][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 06:55:39,513][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 06:55:40,008][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 06:55:40,508][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 06:55:41,005][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 06:55:41,500][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 06:55:41,998][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 06:55:42,494][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 06:55:42,993][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 06:55:43,493][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 06:55:43,987][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 06:55:44,476][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 06:55:44,967][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 06:55:45,456][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 06:55:45,945][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 06:55:46,434][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 06:55:46,925][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 06:55:47,412][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 06:55:47,904][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 06:55:48,394][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 06:55:48,880][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 06:55:49,369][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10382 tokens. [2025-11-13 06:55:50,150][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.14%, Current % of VRAM taken: 59.60%, Block Peak % of device VRAM: 62.22%, ΔTime: 00:00:32 [2025-11-13 06:55:50,832][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 06:55:50,834][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 06:55:50,835][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 06:55:51,704][__main__][INFO] - Iteration 566 took 1m 51s (67.89% Gen, 31.33% Train). Generation: 1m 15s, Training: 34s. Estimated remaining time: 84h 1m 31s. Estimated total time: 92h 47m 41s. Time estimates for 10 more iterations: 18m 33s, 100 more iterations: 3h 5m 35s, 500 more iterations: 15h 27m 56s. [2025-11-13 06:55:51,706][__main__][INFO] - Starting iteration 566. [2025-11-13 06:55:52,190][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 56 and human policies 1. [2025-11-13 06:55:52,190][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 06:56:12,500][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,527][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,530][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,532][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,534][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,537][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,540][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,542][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,545][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,547][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,550][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,552][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,554][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,557][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,559][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,561][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,564][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,566][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,568][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,570][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,572][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,574][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,576][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,578][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,580][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,583][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,585][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,587][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,603][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,605][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,607][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,610][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,612][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,614][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,616][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,619][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,621][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,623][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,626][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,629][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,631][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,633][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,636][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,637][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,639][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,641][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,644][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,646][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,648][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,650][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,653][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,655][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,657][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,659][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,661][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,664][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,666][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,682][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,684][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,686][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,688][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,689][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,691][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,693][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,694][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,696][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,698][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,700][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,702][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,704][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,705][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,707][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,709][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,711][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,712][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,714][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,715][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,716][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,719][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,721][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,723][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,724][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,726][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,727][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,729][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,730][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,732][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,733][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,735][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,737][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,739][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,741][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,742][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,744][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,746][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:12,820][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:12,837][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:12,839][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:12,842][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:12,844][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:12,846][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:12,854][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:12,857][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:12,859][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:12,862][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:12,864][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:12,866][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:12,868][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:12,871][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:12,873][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:12,875][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:12,876][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:12,879][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:12,886][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:12,888][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:12,891][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:12,893][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:12,895][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:12,897][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:12,899][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:12,901][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:12,903][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:12,906][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:12,916][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:12,918][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:12,920][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:12,922][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:12,924][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:12,926][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:12,928][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:12,930][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:12,932][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:12,934][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:12,936][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:12,938][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:12,940][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:12,949][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:12,951][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:12,953][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:12,955][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:12,956][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:12,958][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:12,960][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:12,962][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:12,964][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:12,966][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:12,968][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:12,976][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:12,978][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:12,980][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:12,982][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:12,984][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:12,986][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:12,988][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:12,994][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:12,995][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:12,997][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:12,999][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:13,000][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:13,002][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:13,003][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:13,005][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:13,006][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:13,010][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:13,012][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:13,014][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:13,015][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:13,016][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:13,018][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:13,020][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:13,021][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:13,022][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:13,024][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:13,026][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 1 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:13,030][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:13,032][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:13,034][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:13,036][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:13,038][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:13,039][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:13,041][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:13,043][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:13,044][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:13,046][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:13,050][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:13,052][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:13,053][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:13,055][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:13,057][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:13,058][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:13,119][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:13,163][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:13,170][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:13,176][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:13,182][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:13,188][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:13,193][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:13,199][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:13,204][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:13,218][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:13,222][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:13,226][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:13,230][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:13,234][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:13,238][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:13,242][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:13,246][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:13,250][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:13,254][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:13,258][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:13,262][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:13,266][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:13,270][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:13,274][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:13,278][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:13,281][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:13,285][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:13,288][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:13,302][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:13,305][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:13,309][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:13,312][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:13,315][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:13,319][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:13,322][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:13,325][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:13,328][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:13,331][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:13,335][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:13,338][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:13,341][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:13,344][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:13,347][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:13,350][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:13,353][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:13,356][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:13,359][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:13,362][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:13,365][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:13,368][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:13,371][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:13,374][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:13,377][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:13,380][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:13,382][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:13,385][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:13,388][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:13,391][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:13,394][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:13,396][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:13,399][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:13,408][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:13,411][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:13,413][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:13,416][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:13,418][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:13,421][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:13,423][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:13,426][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:13,428][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:13,431][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:13,434][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:13,436][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:13,439][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:13,441][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:13,444][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:13,446][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:13,449][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:13,451][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:13,454][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:13,456][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:13,459][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:13,462][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:13,464][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:13,467][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:13,469][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:13,472][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:13,475][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:13,477][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:13,479][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:13,482][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:13,484][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:13,487][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:13,489][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:13,492][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:14,478][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,481][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,484][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,486][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,488][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,504][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,507][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,509][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,532][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,534][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,537][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,540][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,553][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,556][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,578][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,580][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,583][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,586][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,602][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,604][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,607][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,609][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,612][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,615][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,617][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,626][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,629][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,631][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,651][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,654][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,657][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,660][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,663][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,665][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,667][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,668][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,673][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,675][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,677][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,679][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,680][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,682][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,698][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,700][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,703][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,705][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,706][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,708][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,723][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,726][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,728][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,730][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,732][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,735][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,738][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,740][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,749][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,752][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,755][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,757][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,760][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,762][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,764][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,773][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,775][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,777][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,779][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,782][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,785][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,787][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,789][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,796][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,798][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,800][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,802][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,805][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,807][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,809][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,811][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,813][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,814][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,820][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,821][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,823][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,825][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,827][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,829][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,830][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,832][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,834][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,835][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,840][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,841][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,843][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,844][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,846][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:14,885][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:14,887][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:14,889][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:14,890][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:14,892][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:14,901][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:14,904][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:14,906][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:14,920][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:14,922][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:14,924][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:14,936][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:14,939][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:14,941][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:14,955][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:14,957][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:14,960][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:14,963][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:14,970][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:14,972][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:14,974][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:14,977][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:14,979][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:14,987][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:14,989][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:14,992][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:14,994][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:14,996][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:15,007][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:15,009][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:15,011][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:15,014][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:15,025][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:15,028][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:15,030][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:15,032][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:15,034][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:15,036][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:15,038][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:15,040][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:15,043][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:15,045][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:15,051][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:15,054][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:15,056][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:15,058][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:15,060][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:15,062][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:15,068][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:15,070][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:15,072][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:15,074][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:15,076][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:15,078][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:15,084][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:15,086][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:15,088][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:15,090][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:15,092][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:15,094][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:15,096][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:15,104][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:15,106][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:15,109][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:15,110][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:15,112][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:15,114][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:15,128][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:15,130][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:15,132][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:15,134][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:15,136][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:15,138][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:15,140][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:15,142][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:15,148][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:15,151][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:15,153][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:15,154][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:15,156][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:15,158][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:15,159][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:15,160][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:15,162][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:15,169][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:15,171][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:15,173][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:15,175][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:15,177][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:15,179][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:15,180][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:15,182][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:15,184][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:15,186][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:15,191][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:15,230][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:15,235][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:15,240][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:15,244][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:15,249][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:15,257][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:15,262][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:15,267][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:15,275][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:15,280][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:15,284][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:15,292][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:15,296][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:15,300][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:15,308][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:15,312][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:15,319][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:15,323][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:15,327][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:15,331][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:15,335][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:15,338][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:15,345][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:15,348][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:15,351][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:15,355][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:15,358][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:15,364][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:15,367][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:15,371][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:15,374][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:15,377][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:15,384][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:15,387][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:15,390][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:15,393][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:15,408][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:15,412][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:15,415][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:15,418][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:15,422][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:15,425][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:15,429][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:15,434][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:15,438][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:15,441][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:15,444][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:15,446][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:15,448][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:15,451][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:15,460][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:15,463][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:15,466][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:15,468][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:15,469][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:15,471][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:15,474][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:15,476][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:15,478][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:15,486][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:15,489][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:15,491][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:15,493][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:15,496][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:15,498][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:15,499][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:15,502][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:15,509][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:15,511][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:15,514][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:15,517][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:15,533][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:15,535][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:15,537][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:15,539][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:15,541][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:15,543][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:15,545][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:15,560][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:15,562][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:15,564][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:15,567][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:15,569][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:15,571][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:15,573][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:15,575][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:15,577][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:15,579][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:15,581][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:15,586][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:15,588][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:15,591][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:15,593][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:15,595][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:15,597][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:16,680][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:16,681][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:16,706][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:16,710][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:16,713][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:16,716][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:16,719][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:16,728][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:16,731][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:16,734][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:16,753][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:16,755][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:16,757][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:16,759][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:16,776][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:16,778][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:16,780][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:16,782][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:16,800][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:16,803][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:16,805][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:16,807][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:16,810][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:16,823][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:16,826][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:16,828][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:16,831][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:16,833][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:16,835][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:16,837][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:16,847][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:16,850][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:16,852][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:16,855][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:16,857][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:16,869][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:16,872][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:16,895][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:16,898][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:16,901][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:16,903][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:16,906][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:16,908][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:16,911][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:16,914][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:16,916][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:16,919][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:16,927][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:16,930][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:16,932][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:16,935][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:16,937][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:16,939][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:16,940][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:16,943][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:16,946][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:16,947][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:16,950][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:16,952][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:16,954][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:16,955][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:16,957][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:16,958][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:16,960][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:16,962][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:16,967][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:16,969][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:16,971][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:16,989][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:16,990][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:16,992][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:16,993][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:16,994][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:16,995][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:16,995][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:16,996][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:17,010][mllm.models.large_language_model_local][WARNING] - Response Given the updated per-item values, my strategy needs to be more balanced and consider the relative values. Since hats are valued very low by both parties, they can be shared almost evenly. Books and balls, on the other hand, have much higher values and should be heavily claimed to maximize points. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:17,012][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:17,015][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:17,017][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:17,019][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:17,021][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:17,022][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:17,024][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:17,027][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:17,031][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:17,033][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:17,035][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:17,037][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:17,039][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:17,040][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:17,042][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:17,044][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:17,045][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:17,088][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:17,090][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:17,102][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:17,105][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:17,107][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:17,109][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:17,121][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:17,123][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:17,125][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:17,127][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:17,137][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:17,139][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:17,141][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:17,144][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:17,155][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:17,157][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:17,159][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:17,162][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:17,171][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:17,173][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:17,175][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:17,176][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:17,178][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:17,191][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:17,193][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:17,195][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:17,197][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:17,199][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:17,210][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:17,212][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:17,214][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:17,216][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:17,218][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:17,219][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:17,221][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:17,228][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:17,230][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:17,249][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:17,251][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:17,254][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:17,256][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:17,267][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:17,269][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:17,271][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:17,272][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:17,274][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:17,276][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:17,277][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:17,279][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:17,281][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:17,286][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:17,287][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:17,289][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:17,290][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:17,292][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:17,294][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:17,295][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:17,297][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:17,298][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:17,300][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:17,302][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:17,307][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:17,308][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:17,310][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:17,313][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:17,315][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:17,316][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:17,316][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:17,327][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:17,329][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:17,330][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:17,331][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:17,332][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:17,334][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:17,336][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:17,337][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:17,347][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:17,350][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:17,351][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:17,353][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:17,355][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:17,356][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:17,356][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:17,367][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:17,368][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:17,370][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:17,372][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:17,374][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:17,375][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:17,377][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:17,379][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:17,386][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:17,389][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:17,390][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:17,425][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:17,430][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:17,441][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:17,445][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:17,449][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:17,453][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:17,461][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:17,465][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:17,469][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:17,474][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:17,480][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:17,484][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:17,487][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:17,491][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:17,502][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:17,505][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:17,509][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:17,513][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:17,522][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:17,525][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:17,529][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:17,532][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:17,535][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:17,542][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:17,545][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:17,548][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:17,551][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:17,554][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:17,564][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:17,566][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:17,569][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:17,571][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:17,573][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:17,576][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:17,587][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:17,590][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:17,593][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:17,610][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:17,613][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:17,616][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:17,619][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:17,633][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:17,635][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:17,638][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:17,641][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:17,644][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:17,647][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:17,659][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:17,661][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:17,664][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:17,665][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:17,669][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:17,672][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:17,674][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:17,677][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:17,680][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:17,682][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:17,685][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:17,689][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:17,691][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:17,694][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:17,696][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:17,702][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:17,707][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:17,711][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:17,714][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:17,718][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:17,720][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:17,725][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:17,727][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:17,729][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:17,732][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:17,734][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:17,737][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:17,739][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:17,761][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:17,764][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:17,767][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:17,770][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:17,773][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:17,776][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:17,794][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:17,797][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:17,803][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:17,808][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:17,811][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:17,815][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:17,819][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:17,831][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:17,834][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:17,837][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:17,840][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:18,628][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:18,631][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:18,959][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:18,984][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:18,987][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:18,990][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:18,992][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:19,011][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:19,014][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:19,017][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:19,020][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:19,040][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:19,043][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:19,064][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:19,067][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:19,070][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:19,073][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:19,089][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:19,092][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:19,116][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:19,118][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:19,119][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:19,121][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:19,123][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:19,126][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:19,128][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:19,131][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:19,134][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:19,136][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:19,139][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:19,146][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:19,148][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:19,151][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:19,153][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:19,167][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:19,169][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:19,171][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:19,173][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:19,192][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:19,193][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:19,194][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:19,195][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:19,197][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:19,199][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:19,215][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:19,217][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:19,219][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:19,220][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:19,220][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:19,221][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:19,238][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:19,240][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:19,241][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:19,242][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:19,243][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:19,245][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:19,246][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:19,247][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:19,248][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:19,248][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:19,262][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:19,264][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:19,265][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:19,266][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:19,266][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:19,267][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:19,267][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:19,268][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:19,269][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:19,271][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:19,283][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:19,285][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:19,306][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:19,307][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:19,307][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:19,308][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:19,308][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:19,309][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:19,309][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:19,310][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:19,311][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:19,326][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:19,327][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:19,328][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:19,329][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:19,330][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:19,331][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:19,376][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:19,392][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:19,393][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:19,408][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:19,409][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:19,410][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:19,412][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:19,426][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:19,427][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:19,443][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:19,444][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:19,445][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:19,447][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:19,460][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:19,461][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:19,480][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:19,480][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:19,481][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:19,483][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:19,485][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:19,487][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:19,488][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:19,496][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:19,498][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:19,500][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:19,501][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:19,503][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:19,505][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:19,506][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:19,507][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:19,516][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:19,518][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:19,519][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:19,521][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:19,536][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:19,537][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:19,539][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:19,540][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:19,540][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:19,541][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:19,556][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:19,557][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:19,559][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:19,561][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:19,562][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:19,563][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:19,572][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:19,573][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:19,574][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:19,575][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:19,576][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:19,578][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:19,579][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:19,581][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:19,582][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:19,584][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:19,593][mllm.models.large_language_model_local][WARNING] - Response Given the new values, where hats and books are highly valued by you but not as much by Bob, and balls are valued the same by both, a more strategic proposal would be to take full advantage of your higher values for hats and books. Here’s the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:19,597][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:19,598][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:19,600][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:19,601][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:19,602][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:19,605][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:19,606][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:19,607][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:19,608][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:19,608][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:19,614][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:19,615][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:19,616][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:19,618][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:19,636][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:19,637][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:19,638][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:19,640][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:19,641][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:19,643][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:19,644][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:19,645][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:19,655][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:19,655][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:19,656][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:19,658][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:19,659][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:19,661][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:19,708][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:19,724][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:19,741][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:19,747][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:19,751][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:19,757][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:19,760][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:19,777][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:19,780][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:19,784][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:19,787][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:19,795][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that books are highly valuable for both of us, while hats and balls are less so. A strategic proposal might involve securing a significant portion of the books to maximize points. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:19,796][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:19,798][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:19,818][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:19,821][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:19,823][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:19,826][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:19,829][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:19,831][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:19,833][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:19,839][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:19,841][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:19,843][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:19,845][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:19,847][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:19,850][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:19,863][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:19,865][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:19,868][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:19,870][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:19,883][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:19,887][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:19,889][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:19,892][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:19,894][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:19,906][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:19,909][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:19,911][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:19,914][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:19,916][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:19,936][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:19,939][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:19,942][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:19,945][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:19,948][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:19,950][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:19,952][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:19,954][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:19,957][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:19,959][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:19,963][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:19,965][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:19,968][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:19,970][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:19,972][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:19,974][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:19,976][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:19,978][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:19,981][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:19,994][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:19,996][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:19,998][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:20,001][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:20,002][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:20,023][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:20,026][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:20,029][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:20,032][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:20,034][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:20,037][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:20,039][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:20,041][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:20,049][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:20,051][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:20,053][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:20,055][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:20,057][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:20,059][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:20,107][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that books are highly valuable to you while hats are highly valuable to Bob. To maximize your points, you should propose to take all the books and a minimal number of hats and balls. Here's a proposal that balances risk and reward: Proposal: 1 hat, 10 books, 1 ball did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:20,109][mllm.models.large_language_model_local][WARNING] - Response Given the values, it appears that both you and Alice have similar or reversed preferences for the items. Since both of you are likely to have the same expected per-item value and the distribution of items is random, a cooperative strategy might yield the best results. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:20,180][mllm.models.large_language_model_local][WARNING] - Response Given the values, both you and Alice place high value on books and balls, while hats have lower value for both. To maximize points, we can strategically propose to take more books and balls to maximize the value from those items. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:20,279][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:20,645][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:20,721][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:20,725][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:20,983][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric and competitive nature of the per-item values, it seems proposing the same allocation as Bob would be a reasonable approach to ensure we both receive our full allocation without exceeding the total quantity of items. Thus, we can continue to propose the following: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:20,985][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:21,046][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:21,129][mllm.models.large_language_model_local][WARNING] - Response Given the values, it seems that books are highly valued by both you and Bob, while hats and balls have lower values. Proposing to take all items might lead to a proportional split if Bob also proposes to take all items, resulting in a lower payoff for high-value items. A cooperative or slightly competitive approach might be better to maximize the high-value books. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:21,132][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:21,158][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:21,160][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:21,163][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:21,183][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the value assignments and the previous outcomes, it seems that proposing equal shares might not be the optimal strategy. Let's consider a different approach to maximize points. Since books have high value for both of us but in different rounds, and balls have a low value but are the same for both, a balanced but strategic proposal might be beneficial. Here's a revised proposal: Proposal: 10 hats, 10 books, 2 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:21,186][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:21,188][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:21,206][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:21,208][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:21,210][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:21,235][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:21,237][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:21,240][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:21,243][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:21,246][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:21,258][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:21,260][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:21,262][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:21,265][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:21,267][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:21,284][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:21,286][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:21,288][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:21,290][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:21,310][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:21,312][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:21,314][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:21,332][mllm.models.large_language_model_local][WARNING] - Response Given the situation where both you and Alice have high values for different items, a strategy of splitting the items more evenly might yield higher points overall. Let's propose to take 5 hats, 5 books, and 10 balls. This way, we can maximize the value of the items we receive while ensuring that neither of us holds all the items with high value for the other. Proposal: 5 hats, 5 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:21,334][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:21,336][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:21,338][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:21,357][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:21,358][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:21,360][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:21,361][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:21,364][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:21,365][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:21,379][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:21,380][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:21,381][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:21,382][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:21,382][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:21,383][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:21,386][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:21,388][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:21,390][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:21,391][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:21,393][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:21,395][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:21,397][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:21,398][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:21,409][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:21,436][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:21,438][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:21,440][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:21,443][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:21,446][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:21,448][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:21,450][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:21,455][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:21,457][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:21,459][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:21,460][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:21,462][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:21,464][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:21,465][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:21,475][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:21,526][mllm.models.large_language_model_local][WARNING] - Response Given the new per-item values, where you value hats and books at 1 and balls at 10, and Alice values balls at 1, it's clear that the balls are most valuable to you. Here's the proposal: Proposal: 10 balls, 10 hats, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:21,543][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:21,563][mllm.models.large_language_model_local][WARNING] - Response Given the new per-item values, where I value hats and books much higher than balls, and Bob has a similar but reversed preference, a strategic approach would be to propose an allocation that maximizes my points given my higher value for hats and books. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:21,568][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:21,569][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:21,571][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:21,582][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the high value that both you and Alice place on balls, a strategic approach would be to ensure that you both agree on the distribution or at least that it is fair. Since the balls are worth the most, it's crucial to secure as many as possible. Here's a balanced proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:21,584][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:21,587][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:21,607][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:21,609][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:21,612][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:21,627][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:21,629][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:21,632][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:21,634][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:21,635][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:21,648][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:21,650][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:21,652][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:21,655][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:21,668][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:21,670][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:21,672][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:21,691][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:21,694][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:21,696][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:21,708][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:21,710][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:21,712][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:21,729][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's important to consider the strategic allocation to maximize points. If I take all the hats and books, I will get the full value from those items, and Alice will take the balls to get the full value from those items. Here's the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:21,733][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:21,735][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:21,737][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:21,739][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:21,740][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:21,741][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:21,750][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:21,754][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:21,756][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:21,758][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:21,759][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:21,761][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:21,763][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:21,764][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:21,766][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:21,768][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:21,769][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:21,770][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:21,771][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:21,772][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:21,775][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:21,777][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:21,778][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:21,780][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:21,797][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:21,799][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:21,800][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:21,802][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:21,816][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:21,818][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:21,819][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:21,820][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:21,821][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:21,823][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:21,824][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:21,824][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:21,826][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:21,853][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the fact that both you and Bob have identical or similar valuations for the books and balls, it's important to ensure that we both get our preferred items without overproposing, which could result in proportional allocation if the total exceeds the available quantity. Proposal: 10 hats, 10 books, 10 balls This proposal matches the available items exactly, which should allow us to keep our full proposed amounts, optimizing our points based on our high valuations for books and balls. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:21,871][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the equal splits in previous rounds, it seems that both Alice and Bob are proposing to take all items, which results in a proportional split when the total proposals exceed the quantity of items. Considering your per-item values and Bob's, it makes sense to propose an equal split to avoid the proportional allocation, which would reduce your earnings. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:21,940][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:21,945][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:21,960][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:21,964][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:21,980][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:21,984][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:21,988][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:22,003][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:22,007][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:22,011][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:22,014][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:22,017][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:22,053][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:22,056][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:22,058][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:22,061][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:22,073][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:22,075][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:22,098][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:22,102][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:22,105][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:22,120][mllm.models.large_language_model_local][WARNING] - Response Given the values, you see that both you and Alice have very similar valuations for the items, but you place higher value on books and balls compared to hats. A strategic approach would be to align your proposal with these valuations while ensuring you do not overpropose for any item, as the items will be allocated proportionally if the total exceeds their quantity. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:22,124][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:22,127][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:22,141][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:22,144][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:22,148][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:22,151][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:22,164][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:22,167][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:22,170][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:22,172][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:22,175][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:22,177][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:22,180][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:22,182][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:22,185][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:22,220][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric and competitive nature of the per-item values, a strategic proposal would be to undermine Alice's high valuation for hats and balls while maximizing your own value for books. Here's the proposal: Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:22,221][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:22,225][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:22,228][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:22,231][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:22,234][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:22,237][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:22,238][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:22,245][mllm.models.large_language_model_local][WARNING] - Response Given the new per-item values, where you have a higher value for hats and Alice has a higher value for books and balls, a strategic proposal would be to focus on items where you have a higher value. Here's the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:22,247][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:22,249][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:22,251][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:22,277][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:22,280][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:22,282][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:22,284][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:22,287][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:22,289][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:22,291][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:22,294][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:22,505][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:22,533][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:22,536][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:22,678][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric and competitive nature of the per-item values, where Alice values books very highly and you value hats and balls highly, but not books, it makes sense to try to balance your proposals in a way that maximizes your value while also considering potential counter-proposals from Alice. A strategic approach would be to offer a split that ensures you get a significant portion of the items you value highly, while not completely disregarding the items Alice values highly. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:22,722][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:22,724][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:22,773][mllm.models.large_language_model_local][WARNING] - Response Given the high value of hats for both you and Bob, and the high value of balls for both, it's beneficial to try to maximize the distribution of these items. Since our values for books are relatively low, we should try to avoid proposing more books than necessary. Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:22,883][mllm.models.large_language_model_local][WARNING] - Response Given that both you and Bob have high values for hats and books and low values for balls, proposing to take all items seems like a reasonable strategy. However, if we assume that proposing to take all items might lead to a proportional distribution if the other does the same, we should consider a slightly less aggressive approach to ensure we cover more valuable items. Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:23,016][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:23,137][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:23,141][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:23,210][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:23,213][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:23,542][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the values, proposing a balanced distribution seems reasonable. However, since the books have a higher value for you and the hats and balls have a higher value for Bob, you might want to ensure you get more of the high-value items. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:23,568][mllm.models.large_language_model_local][WARNING] - Response Given that both you and Alice have similar high values for hats and books, and balls are valued similarly, a cooperative proposal could maximize points. However, to balance the distribution and ensure no over-proposal, we can split the items fairly. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:23,610][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:23,613][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:23,673][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:23,676][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:23,761][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:23,765][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:23,767][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:23,770][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:23,843][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's beneficial to maximize the items with higher personal value, which in this case are books and balls. Here's a strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:23,849][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:23,852][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:23,854][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:23,856][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:23,872][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it appears that hats are valued equally, books slightly less, and balls the least by both Alice and Bob. However, balls are valued the highest by Alice and the lowest by Bob. We should aim to maximize our points by taking into account both the item quantities and the per-item values. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:23,875][mllm.models.large_language_model_local][WARNING] - Response Given the consistent pattern where Alice and I have complementary valuations for the items, it makes sense to propose an allocation that maximizes the total value for both of us. Since we each have the highest valuation for different items, proposing an equal split of each type of item would be fair and maximize our combined points. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:23,878][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:23,879][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:23,881][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:23,883][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:23,885][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:23,899][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:23,930][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the previous round results, it seems that both Alice and Bob are likely to propose the same distribution to maximize their points based on their individual valuations. In this round, proposing a balanced distribution that aligns with the perceptible pattern from previous rounds seems optimal. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:23,933][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:23,936][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:23,954][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:23,957][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:23,960][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:23,962][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:23,977][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:23,980][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:24,000][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:24,003][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:24,005][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:24,008][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:24,010][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:24,012][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:24,014][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:24,016][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:24,018][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:24,020][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:24,633][mllm.models.large_language_model_local][WARNING] - Response Given the values, the optimal strategy would be to propose an allocation that takes into account the value each item has for both Alice and Bob. Since both you and Bob value balls highly, it's crucial to secure a significant portion of the balls to maximize points. Here’s the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:24,636][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values where both you and Bob value hats and books highly, but balls less, it makes sense to propose an allocation that maximizes your value for the items you and Bob both value highly. Here is the proposal: Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:24,643][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:24,650][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:24,653][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:24,656][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:24,658][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:24,714][mllm.models.large_language_model_local][WARNING] - Response Given the new values, it's clear that Bob values books and balls significantly more than hats, while you value hats and books relatively higher and balls lower. However, because both of you have significant values for books, it might be wise to ensure you both get a fair share of books to maximize overall points. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:24,717][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:24,720][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:24,723][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:24,726][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:24,743][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:24,745][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:24,748][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:24,750][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:24,753][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:24,755][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:24,850][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:24,857][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:24,868][mllm.models.large_language_model_local][WARNING] - Response Given Bob's per-item values for books are significantly higher than mine, it suggests he might prioritize books more than I do. However, my value for hats and balls is higher than his, so I should still propose to take most of the hats and balls. A strategic approach would be to maximize my points by taking the items I value the most, and assume Bob will take the books due to his higher value for them. Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:24,965][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the perceived value and the fact that both of our valuations are similar for each item type, a cooperative approach seems beneficial. We should aim to split the items evenly to maximize the value we each receive. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:24,969][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:24,973][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:24,976][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:24,979][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:24,994][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:25,000][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:25,005][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:25,008][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:25,014][mllm.models.large_language_model_local][WARNING] - Response Given the new values, where you have a high value for books and balls, and Bob has a high value for hats and balls, we should aim to maximize our shares in books and balls while ensuring we get a fair share of the hats. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:25,019][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:25,022][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:25,024][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:25,026][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:25,062][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:25,066][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:25,069][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:25,074][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:25,077][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:25,080][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:25,197][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:25,200][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:25,279][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:25,282][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:25,285][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:25,288][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:25,337][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric and complementary nature of the per-item values, a cooperative approach might yield better results. Since both Alice and Bob value books highly, and the items to split include many books, we should try to maximize the allocation of books while ensuring a fair split of the other items. Proposal: 10 hats, 10 books, 10 balls However, to ensure a more balanced and potentially fair split that might encourage cooperation, we can suggest a more equitable distribution: Proposal: 5 hats, 10 books, 5 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:25,344][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:25,357][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:25,359][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:25,362][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:25,364][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:25,367][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:25,369][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:25,372][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:25,374][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:25,377][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:25,380][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:25,385][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:25,389][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:25,391][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:25,393][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:25,460][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Alice value balls and books highly, but you value balls significantly more than Alice does. To maximize your points, you should propose to take all the balls and a portion of the books, since you can trade some books for hats if necessary. Here is a reasonable proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:25,465][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it is clear that both you and Bob value the hats and books more and the balls less. Since both of you have complementary preferences, a cooperative approach might yield the best results. Proposing to take all the items could lead to a proportional distribution if the total is exceeded. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:25,469][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:25,475][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:25,476][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:25,478][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:25,479][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:25,516][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:25,760][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:25,767][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:25,771][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:25,786][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:25,792][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:25,795][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:25,798][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:25,868][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:25,873][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:25,879][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:25,884][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:25,887][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:25,892][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:25,897][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:26,120][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:26,122][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:26,124][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:26,127][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:26,214][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:26,220][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:26,226][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:26,233][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:26,266][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:26,354][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:26,359][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:26,362][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:26,366][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:26,370][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:26,442][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:26,449][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:26,455][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:26,461][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:26,467][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:26,472][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:26,476][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:26,481][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:26,485][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:26,487][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:26,617][mllm.models.large_language_model_local][WARNING] - Response Given the values, the best strategy is to propose a distribution that takes into account the high value of books and balls for the other player while ensuring you get enough hats. Here's the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:26,621][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:26,628][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:26,634][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:26,640][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:26,710][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:26,713][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:26,716][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:26,719][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:26,722][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:26,725][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:26,809][mllm.models.large_language_model_local][WARNING] - Response Given the dynamic nature of the per-item values and the desire to maximize points, let's consider a strategic approach. In this round, you have a strong preference for books and balls but a weak preference for hats. Bob, on the other hand, has a strong preference for balls and a weak preference for hats. Let's propose an allocation that leverages these preferences while ensuring a fair deal. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:26,816][mllm.models.large_language_model_local][WARNING] - Response Given the values and the symmetry in the situation, a strategic proposal would consider splitting the items equally to maximize points in both directions. Here's the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:26,993][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:26,996][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:27,085][mllm.models.large_language_model_local][WARNING] - Response Given the new values where you have a higher valuation for books and balls compared to Alice, it makes sense to propose a distribution that reflects this. Here's a proposal that maximizes your points by keeping more of the items you value more highly: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:27,088][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:27,092][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:27,259][mllm.models.large_language_model_local][WARNING] - Response Given the values, it seems that both your and Bob's valuations for hats and books are quite similar, while there's a significant difference in the valuation of balls. Based on this, a strategy that takes advantage of this asymmetry could be effective. You value balls far more than Bob does, while he values books more than you do. Here’s a proposal that maximizes your points: Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:27,380][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:27,409][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:27,412][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:27,413][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:27,416][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:27,418][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:27,419][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:27,421][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:27,463][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:27,465][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:27,468][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:27,472][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:27,474][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:27,745][mllm.models.large_language_model_local][WARNING] - Response Given the current per-item values, it's clear that books and balls are much more valuable to you than hats, while hats are more valuable to Bob. To maximize your points, you should propose to keep as many books and balls as possible since they are highly valuable to you. Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:27,804][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:27,807][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:28,036][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:28,039][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:28,043][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:28,084][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that books are highly valuable to both of us, while hats and balls have minimal value. My strategy should focus on maximizing the number of books I receive, as this will significantly increase my points. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:28,090][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:28,317][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's in your best interest to propose an allocation that maximizes your points. Since both you and Bob value books the most, but you value hats and balls higher than Bob, it's strategic to take more balls and less hats and books. Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:28,320][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:28,403][mllm.models.large_language_model_local][WARNING] - Response Given that both you and Bob value the same items highly, a competitive strategy might be beneficial. Since you both value hats and books highly, you might consider proposing to take a larger share of these items to maximize your points. Here is a strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:28,467][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:28,469][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:28,502][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:28,505][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:28,508][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:28,511][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:28,514][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:28,517][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:28,519][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:28,522][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:28,525][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:28,531][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:28,532][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:28,533][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:28,535][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:28,536][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:28,539][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:28,635][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:28,638][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:28,641][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:28,691][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:28,694][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:28,742][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:28,781][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values where hats=10, books=1, and balls=1 for you, and hats=1, books=1, and balls=10 for Alice, it would be strategic to focus on the items with higher personal value. Here's the proposed allocation: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:28,784][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's crucial to maximize the points in this round. Since both of us value hats and balls highly, and both of us will likely propose to keep all items, we should try to maximize our share while considering the proportional allocation rule. Here's the optimal proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:28,785][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:28,787][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:28,789][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:28,791][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:28,794][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:28,797][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:28,799][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:28,801][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:28,802][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:28,803][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:28,805][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:28,911][mllm.models.large_language_model_local][WARNING] - Response Given the high value of books and balls for both you and Alice, and to maximize the points, it's crucial to secure as many of these items as possible. A strategic split would be to secure more of the highly valued items while ensuring both you and Alice get a fair share of the total quantity. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:28,913][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:28,916][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:28,918][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:28,920][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:28,971][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:29,031][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:29,034][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:29,037][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:29,040][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:29,044][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:29,047][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:29,076][mllm.models.large_language_model_local][WARNING] - Response Given the values, it seems that both you and Bob have different valuations for the items. You place the highest value on books (10) and the lowest on hats (1), while Bob values hats the most and books the least. To maximize your points, you should try to keep as many books as possible, since they are worth the most to you, while Bob will likely try to keep the hats and books. A good strategy would be to propose keeping all the books and a fair share of the hats and balls. Here’s a proposal: Proposal: 5 hats, 10 books, 5 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:29,079][mllm.models.large_language_model_local][WARNING] - Response Given the high value of hats and books for both Alice and me, and the low value of balls, we should try to maximize the distribution of hats and books. Here's a strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:29,082][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:29,084][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:29,086][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:29,088][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:29,169][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:29,172][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:29,175][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:29,178][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:29,251][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:29,254][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:29,256][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:29,260][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:29,262][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:29,264][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:29,279][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:29,282][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:29,299][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:29,320][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:29,321][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:29,324][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:29,338][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the values of the items, it seems that both you and Alice value the items in a way that would benefit from a cooperative approach. Here’s a proposal that takes into account the values of both you and Alice: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:29,339][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:29,341][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:29,344][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:29,345][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:29,347][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:29,361][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Alice place the highest value on the balls, followed by books, and then hats. To increase your earnings, it might be more strategic to consider Alice's higher valuation of hats and books. Here's a proposal that takes into account these valuations and aims to balance the distribution: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:29,363][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:29,365][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:29,367][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:29,368][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:29,372][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:29,375][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:29,379][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:29,382][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:29,385][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:29,408][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:29,409][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:29,410][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:29,412][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:29,642][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:29,735][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:29,890][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:29,893][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:29,944][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:29,945][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:29,947][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:29,949][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:29,966][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:29,968][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:29,971][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:29,973][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:29,975][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:29,976][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:30,064][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:30,067][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:30,070][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:30,148][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:30,153][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:30,187][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:30,189][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:30,194][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:30,197][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:30,200][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:30,202][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:30,274][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:30,281][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:30,287][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:30,293][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:30,363][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:30,455][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:30,459][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:30,462][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:30,464][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:30,521][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:30,524][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:30,528][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:30,546][mllm.models.large_language_model_local][WARNING] - Response Given the values, you have a strong preference for hats and balls, while Bob values books and balls highly. To maximize points, you should propose to take all the items that you value more highly. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:30,548][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:30,551][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:30,571][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric and complementary nature of the per-item values, proposing to take all items seems to be a strategic choice as both you and Alice will likely do the same, leading to a proportional allocation. Here's the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:30,574][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:30,599][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:30,602][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:30,605][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:30,608][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:30,610][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:30,613][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:30,615][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:30,618][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:30,621][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:30,623][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:30,625][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:30,627][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:30,629][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:30,632][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:30,662][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:30,664][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:30,666][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:30,667][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:30,669][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:30,671][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:30,722][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:30,810][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:30,836][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:30,840][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:30,859][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:30,862][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:30,865][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:30,867][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:30,869][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:30,873][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:30,874][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:30,876][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:30,888][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:30,891][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:30,893][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:30,894][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:30,897][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:30,902][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:30,907][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:30,912][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:30,916][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:30,919][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:30,922][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:30,925][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:30,927][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:30,981][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:30,987][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:30,989][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:31,070][mllm.models.large_language_model_local][WARNING] - Response Given that both you and Bob have similar values for books and balls, and considering that the optimal strategy might involve splitting items to maximize individual expected values, let's propose a distribution that ensures you get a fair share of the high-value items (books and balls) while still respecting the total item quantity constraint. Proposal: 10 hats, 10 books, 10 balls This proposal keeps all items, ensuring you get a balanced share of each item. However, since the values for books are much higher for you, it might be beneficial to slightly favor books and balls in your proposal to maximize your points. Let's refine the proposal to account for this: Proposal: 10 hats, 9 books, 11 balls This proposal retains 10 hats as it has the lowest value for you, keeps a higher share of books (since they are highly valued by you), and slightly increases the share of balls, ensuring you still get a fair distribution while maximizing your points. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:31,075][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the high value that both you and Bob place on the same items (books and hats), it's likely that proposing to take all of the books and hats will be beneficial. Here's a proposal that maximizes the value you can receive based on your per-item values: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:31,078][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:31,241][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the high values for books and balls, it makes sense to propose an allocation that maximizes the points based on the values you and Alice have for these items. Here’s a strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:31,346][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:31,349][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:31,352][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:31,354][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:31,356][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:31,358][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:31,414][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:31,418][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:31,424][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:31,430][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:31,466][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:31,468][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:31,505][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:31,509][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:31,548][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:31,592][mllm.models.large_language_model_local][WARNING] - Response Given the strategic nature of the game, it's important to consider how Alice might respond, especially considering the high value of books and balls from both perspectives. Since both you and Alice value books and balls highly, it might be beneficial to propose a split that ensures you get a significant portion of these valuable items, even if it means you have to give up some hats. Proposal: 10 hats, 8 books, 12 balls This proposal gives you a total of 20 books and balls, whereas Alice proposed a total of 20 books and balls as well. Given the high value of books and balls, this proposal might lead to a more favorable outcome for both parties in terms of value received. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:31,596][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:31,599][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:31,601][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:31,602][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:31,603][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:31,605][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:31,607][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:31,610][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:31,621][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric nature of the per-item values and the equal allocation when proposals match, it makes strategic sense to propose in a way that maximizes the value based on your preferences. Since both you and Alice value hats highly, it's important to secure as many hats as possible while balancing the other items. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:31,623][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:31,625][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:31,627][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:31,629][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:31,632][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:31,634][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:31,710][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:31,981][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Alice place high value on different items. You value books and balls highly, while Alice values hats and books. Proposing to split the items evenly might not be optimal, as it doesn't account for the differing values each of you places on the items. To maximize your points, you should propose to take more of the items that you value highly. Since both of you seem to value books highly, it might be beneficial to propose a split that takes into account the relative values. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:32,296][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:32,299][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:32,382][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:32,385][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:32,387][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:32,389][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:32,392][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:32,433][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both hats and balls are highly valued by one of us, while books are highly valued by the other. To maximize points, we need to ensure we get our highly valued items. Here's the strategy: 1. Since hats are valued the same by both of us (10), we can try to split them evenly. 2. Since balls are highly valued by both but you are more valued at 10 compared to my 1, I should get the majority of the balls. 3. Books are highly valued by you but not by me, so I should avoid taking books to prevent you from getting them. Given these values, a good proposal would be to take the majority of the balls and share the hats and books. A balanced proposal might be: Proposal: 5 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:32,437][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:32,440][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:32,443][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:32,446][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:32,449][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:32,452][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:32,454][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:32,456][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:32,459][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:32,461][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:32,463][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:32,465][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:32,466][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:32,766][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:32,769][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:32,827][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:32,829][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:32,830][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:32,833][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:32,834][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:32,980][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:32,983][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:33,165][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:33,168][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:33,172][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:33,176][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:33,179][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:33,180][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:33,181][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:33,182][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:33,183][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:33,267][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:33,270][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:33,272][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:33,273][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:33,275][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:33,277][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:33,371][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:33,375][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:33,378][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:33,382][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:33,463][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:33,467][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:33,470][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:33,472][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:33,475][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:33,478][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:33,512][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:33,516][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:33,562][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the previous rounds where proposing all items led to equal and fair outcomes, I will continue this strategy to maximize my points. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:33,565][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:33,568][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:33,570][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:33,573][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:33,575][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:33,610][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:33,612][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:33,617][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:33,622][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:33,626][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:33,631][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:33,635][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:33,643][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:33,645][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:33,647][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:33,649][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:33,650][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:33,651][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:33,653][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:33,654][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:33,656][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:33,658][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:33,660][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:33,675][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:33,704][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:33,706][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:33,728][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:33,754][mllm.models.large_language_model_local][WARNING] - Response Given the new values, it appears that you and Bob have opposite preferences for the items. You value books highly (10) and balls slightly (1), while Bob values books slightly (1) and balls highly (10). To maximize your points, you should propose to take all the items you value highly. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:33,757][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:33,760][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:33,762][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:33,764][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:33,852][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:33,855][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:33,858][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:33,861][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:33,863][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:33,865][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:33,868][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:33,869][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:33,871][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:33,873][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:33,893][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:33,895][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:34,215][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:34,218][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:34,289][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values for this round, I should focus on maximizing the items with higher personal value. Here, the books and balls are more valuable to me, while the hats are less valuable. My strategy should be to take as many books and balls as possible, while still considering the allocation of hats. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:34,291][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:34,296][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:34,302][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:34,307][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:34,311][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:34,347][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in values and the proposal patterns from previous rounds, it seems both players are likely to propose splitting all items equally. To maintain the equilibrium and maximize points, I will propose the same as Alice to ensure we both receive our proposed amounts. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:34,349][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:34,373][mllm.models.large_language_model_local][WARNING] - Response Given the values, proposing all items maximizes the potential points. However, considering the previous rounds where proposing all items didn't yield the maximum possible points in some scenarios, we need to be strategic to ensure we don't overpropose and risk proportional distribution. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:34,376][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:34,380][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:34,384][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:34,388][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:34,391][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:34,394][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:34,398][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:34,401][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:34,403][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:34,405][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:34,407][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:34,441][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:34,482][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:34,483][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:34,508][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:34,535][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:34,538][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:34,539][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:34,540][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:34,541][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:34,593][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in values and the previous rounds where proposing all items results in equal and maximum earnings, I will continue to propose that strategy. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:34,639][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:34,641][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:34,641][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:34,642][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:34,643][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:34,643][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:34,645][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:34,646][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:34,647][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:34,648][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:34,666][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:34,667][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:34,720][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:34,722][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:34,723][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:34,723][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:34,724][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:34,747][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:34,749][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:34,750][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:34,752][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:34,754][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:34,756][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:34,757][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:34,758][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:34,759][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:34,760][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:34,762][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:34,764][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:34,765][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:34,777][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:34,778][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:34,780][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:34,781][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:34,812][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:34,814][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:34,817][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:34,818][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:34,820][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:34,822][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:34,824][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:34,842][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:34,844][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:34,889][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:34,951][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:34,955][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:34,956][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:34,996][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:35,000][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:35,003][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:35,006][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:35,008][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:35,011][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:35,013][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:35,014][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:35,015][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:35,055][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:35,094][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:35,097][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:35,131][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:35,168][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:35,171][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:35,173][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:35,176][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:35,211][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:35,259][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both you and Bob have a significant interest in different items. You value hats and balls highly (10 each), while Bob values hats and books highly (10 each). Balls are valued the least by both of you. To maximize your points, you should propose a split that reflects your higher values for hats and balls. Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:35,261][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:35,265][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:35,268][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:35,272][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:35,275][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:35,278][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:35,280][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:35,282][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:35,285][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:35,288][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:35,291][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:35,295][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:35,336][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:35,338][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:35,339][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:35,341][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:35,342][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:35,373][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:35,374][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:35,376][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:35,377][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:35,379][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:35,381][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:35,383][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:35,384][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:35,385][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:35,387][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:35,388][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:35,389][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:35,403][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:35,405][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:35,407][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:35,408][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:35,437][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:35,439][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:35,440][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:35,442][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:35,443][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:35,445][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:35,448][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:35,465][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:35,466][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:35,468][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:35,469][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:35,494][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:35,498][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:35,499][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:35,500][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:35,502][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:35,503][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:35,522][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:35,524][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:35,525][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:35,526][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:35,550][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:35,552][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:35,553][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:35,554][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:35,555][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:35,557][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:35,558][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:35,560][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:35,562][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:35,563][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:35,564][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:35,762][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:35,763][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:35,813][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Alice place high value on the same items. To maximize points, it's beneficial to coordinate and ensure that both of you get the items you value highly. Here's a proposal that splits the items fairly while ensuring that both you and Alice benefit maximally from their high-value items. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:36,012][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:36,037][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:36,231][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:36,235][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:36,239][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:36,242][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:36,245][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:36,283][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:36,288][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:36,291][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:36,294][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:36,297][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:36,300][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:36,303][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:36,306][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:36,308][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:36,310][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:36,313][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:36,316][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:36,360][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:36,364][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:36,367][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:36,370][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:36,432][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:36,435][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:36,438][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:36,440][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:36,443][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:36,446][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:36,449][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:36,501][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:36,504][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:36,506][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:36,508][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:36,555][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:36,557][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:36,559][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:36,561][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:36,563][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:36,655][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:36,659][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:36,663][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:36,665][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:36,751][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:36,754][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:36,759][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:36,764][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:36,769][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:36,771][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:36,772][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:36,773][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:36,774][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:36,776][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:36,778][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:36,779][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:36,781][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:36,782][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:36,783][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:36,786][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:36,787][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:36,788][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:36,820][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:36,822][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:36,850][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:36,904][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:36,907][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:36,909][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:36,911][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:36,956][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the straightforward nature of the proposal in the previous rounds, I will propose a balanced distribution to maximize the points: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:37,023][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:37,025][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:37,028][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:37,030][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:37,033][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:37,035][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:37,037][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:37,040][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:37,042][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:37,044][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:37,151][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it would be most beneficial to strategically propose a split that maximizes the points based on the values. In this round, the high value for balls for both of us suggests proposing to keep all the balls and splitting the other items proportionally. Here is the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:37,341][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:37,343][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:37,346][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:37,352][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:37,382][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:37,386][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:37,390][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:37,408][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:37,414][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:37,418][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:37,422][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:37,425][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:37,427][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:37,429][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:37,431][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:37,432][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:37,434][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:37,435][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:37,481][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the proposed strategy in previous rounds, it makes sense to propose a fair distribution that maximizes the utility for both parties. Here, you value hats the least, books moderately, and balls the most. Bob values books and balls more, but hats less. Proposing an even split might balance the distribution, ensuring that each agent gets a mix of items that they value. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:37,484][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:37,486][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:37,539][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:37,590][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:37,593][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:37,595][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:37,597][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:37,598][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:37,600][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:37,617][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:37,641][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:37,688][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:37,690][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:37,692][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:37,693][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:37,694][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:37,695][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:37,711][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:37,712][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:37,714][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:37,716][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:37,718][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:37,780][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:37,783][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:37,786][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:37,788][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:37,791][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:37,794][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:37,810][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:37,812][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:37,815][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:37,817][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:37,820][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:37,822][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:37,825][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:37,827][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:37,828][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:37,839][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:37,841][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:38,561][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:38,565][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:38,581][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:38,585][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:38,587][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:38,590][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:38,593][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:38,602][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:38,605][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:38,607][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:38,610][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:38,613][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:38,617][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:38,621][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:38,626][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:38,629][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:38,631][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:38,634][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:38,656][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the equal value structure of this round, proposing an equal split would likely maximize the points for both parties. However, considering the values, splitting books and balls in a way that you get the higher-valued items could be strategic. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:38,659][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:38,660][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:38,664][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:38,690][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:38,770][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:38,775][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:38,781][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:38,783][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:38,788][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:38,790][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:38,792][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:38,885][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:38,891][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:38,897][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:38,901][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:38,905][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:38,913][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:38,916][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:38,919][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:38,923][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:38,926][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:38,982][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the fact that both you and Bob have the same value for hats, but different values for books and balls, proposing an equal share might still be a strategic choice. However, recognizing the high individual value you place on books and Bob's high individual value on balls, a slight adjustment could be beneficial. Proposal: 10 hats, 10 books, 10 balls This proposal keeps the items evenly split, maximizing the chance of proportional allocation and ensuring you get a fair share of all items. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:38,985][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:38,987][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:38,990][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:38,993][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:38,995][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:38,996][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:39,010][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:39,011][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:39,013][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:39,015][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:39,017][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:39,019][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:39,021][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:39,022][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:39,024][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:39,049][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:39,051][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:39,099][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:39,106][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:39,121][mllm.models.large_language_model_local][WARNING] - Response Given that both you and Bob value hats highly (10) and balls slightly (1), and you both value books less (1), a cooperative strategy would be beneficial. Here's a proposal that splits the items evenly while accounting for the differing valuations: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:39,124][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:39,127][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:39,129][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:39,145][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:39,147][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:39,149][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:39,151][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:39,153][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:39,155][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:39,156][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:39,159][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:39,175][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:39,177][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:39,178][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:39,180][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:39,182][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:39,183][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:39,185][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:39,377][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:39,379][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:39,469][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:39,495][mllm.models.large_language_model_local][WARNING] - Response Given Alice's per-item values, she values the hats the most, followed by books, and then balls. Since you value books and balls highly and Alice values hats and books highly, proposing to split the items equally might not be optimal. However, since both you and Alice value books and hats highly, a strategic proposal might involve splitting books and balls in a way that maximizes your value while still considering Alice's high valuation for hats. Proposal: 10 hats, 10 books, 10 balls This proposal keeps the distribution simple and balanced, which might lead to a proportional split that still benefits you due to the high values you assign to books and balls. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:39,561][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:39,567][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:39,573][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:39,578][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:39,583][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:39,598][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:39,602][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:39,607][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:39,612][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:39,615][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:39,618][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:39,622][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:39,625][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:39,654][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:39,659][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:39,837][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:39,840][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:39,847][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:39,853][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:39,869][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:39,872][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:39,876][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:39,877][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:39,879][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:39,883][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:39,887][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:39,890][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:39,899][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:39,901][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:39,902][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:39,905][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:39,906][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:39,908][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:39,911][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:39,913][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:39,936][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:39,939][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:39,941][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:39,943][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:39,945][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:39,947][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:39,948][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:39,949][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:40,017][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:40,045][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:40,048][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:40,051][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:40,054][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:40,057][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:40,059][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:40,072][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:40,074][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:40,155][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's important to maximize the points based on my high value for books and balls while also considering Bob's high value for books and balls. Here's a strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:40,159][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:40,160][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:40,450][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:40,453][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:40,605][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:40,607][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:40,609][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:40,614][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:40,658][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:40,663][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:40,669][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:40,673][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:40,678][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:40,682][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:40,687][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:40,691][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:40,721][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:40,724][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:40,727][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:40,729][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:40,732][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:40,734][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:40,736][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:40,739][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:40,805][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:40,827][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:40,831][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:40,834][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:40,837][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:40,841][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:40,844][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:40,857][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:40,864][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:40,923][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:40,979][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:40,980][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:40,982][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:41,003][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:41,006][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:41,023][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:41,025][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:41,045][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:41,049][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:41,052][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:41,055][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:41,058][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:41,060][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:41,097][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:41,113][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:41,116][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:41,120][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:41,122][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:41,123][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:41,124][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:41,144][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:41,149][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:41,151][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:41,198][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:41,205][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:41,209][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:41,213][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:41,215][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:41,219][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:41,223][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:41,228][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:41,253][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:41,274][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:41,276][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:41,280][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:41,285][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:41,289][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:41,293][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:41,356][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:41,409][mllm.models.large_language_model_local][WARNING] - Response Given the values, it makes sense to propose a distribution that reflects the high value you place on balls and the high value Bob places on books. To maximize points, we should try to capture as many of the high-value items as possible. Here's a proposed split: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:41,417][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:41,420][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:41,423][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:41,428][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:41,430][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:41,452][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:41,460][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:41,470][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:41,473][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:41,476][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:41,478][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:41,481][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:41,483][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:41,529][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:41,536][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:41,543][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:41,547][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:41,550][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:41,553][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:41,555][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:41,561][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:41,564][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:41,566][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:41,569][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:41,669][mllm.models.large_language_model_local][WARNING] - Response Given the values, it makes sense to propose an allocation that maximizes the points based on the per-item values provided. Since you have a high value for hats and Alice has a high value for balls, it's important to allocate the items in a way that takes these values into account. Here's a strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:41,732][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:41,735][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:41,759][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:41,863][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both Alice and Bob place high value on hats and low value on books and balls. Since both of us propose the same amounts, let's try to optimize our strategy by considering the potential for negotiation or split judgments. Proposal: 10 hats, 5 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:41,871][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:41,878][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:41,884][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:41,890][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:41,896][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:41,908][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:41,913][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:41,918][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:41,923][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:41,931][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:41,975][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:41,977][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:41,982][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:41,988][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:41,990][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:41,994][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:41,999][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:42,008][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:42,010][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:42,012][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:42,039][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:42,042][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:42,043][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:42,061][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:42,082][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:42,085][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:42,105][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:42,108][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:42,124][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:42,127][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:42,130][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:42,357][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:42,373][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:42,380][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:42,383][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:42,386][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:42,392][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:42,395][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:42,398][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:42,400][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:42,402][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:42,453][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:42,532][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:42,535][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:42,611][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:42,615][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:42,689][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:42,693][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:42,700][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:43,230][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:43,311][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:43,314][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:43,318][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:43,390][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:43,851][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Bob have a unique set of preferences. You value hats and books highly (10 each), but only balls minimally (1), while Bob values balls highly (10) and hats and books at a moderate value (10 each). To maximize your points, a strategic proposal would be to align your proposal with Bob's values as much as possible, ensuring you get a fair share of the items you value highly. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:43,891][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:43,894][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:43,974][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:43,988][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:44,093][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the previous rounds' outcomes, a strategic proposal would be to maximize the allocation of items that have high values for both yourself and Alice, while ensuring the total is less than or equal to the item quantity. Here, both you and Alice value hats at 1, so we can propose allocating all hats to one of us. Given the equal values, we can propose splitting the other items proportionally. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:44,107][mllm.models.large_language_model_local][WARNING] - Response Given that both you and Alice have very different preferences, with Alice valuing hats much higher than you do, it's important to propose allocating the hats to Alice as much as possible to maximize her utility, since she values them significantly more. Here, I will propose giving Alice all the hats, sharing the books, and keeping as many balls as possible. Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:44,110][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:44,113][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:44,116][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:44,118][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:44,119][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:44,122][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:44,195][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and the symmetry in the problem, a fair proposal that maximizes the expected points for you would be to match Bob's values as closely as possible while ensuring you get a good share of the high-value items (balls). Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:44,198][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the goal to maximize points, a fair and competitive proposal would be to propose an equal split that matches Bob's potential strategy. Here’s the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:44,684][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, the best strategy is to propose a split that maximizes the points considering both agents' values. Here, proposing all items equally might not be the most optimal strategy, as it leads to equal division of points based on the mid-range values. Instead, we should consider proposing a split that takes into account the high value of balls for Bob and the high value of hats and books for us. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:44,706][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:44,715][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:44,860][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:44,883][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:44,894][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:45,030][mllm.models.large_language_model_local][WARNING] - Response Given the high value that both you and Alice place on different items, proposing an equal share may not be the optimal strategy. Since you value hats more than Alice and balls more than Alice, you might want to maximize your gain by proposing a split that capitalizes on these differences. Here is a proposal that takes into account your higher value for hats and balls: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:45,061][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:45,073][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:45,219][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:45,438][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:45,493][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:45,504][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:56:45,674][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:45,684][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:56:45,803][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Bob place high values on hats and books, and both value balls less. A cooperative strategy might yield better results, as allocating items proportionally will benefit both parties. Since both of you value hats and books equally high, and balls less, we can propose an equitable distribution to maximize our combined gains. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:45,855][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:45,865][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:56:46,988][__main__][INFO] - Number of regex retries in iteration 566: 2463 [2025-11-13 06:56:46,989][__main__][INFO] - agents played in iteration 566 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 06:56:47,971][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:56:47,996][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:56:48,028][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:56:48,052][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.55%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:56:48,053][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 06:56:48,053][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 06:56:48,879][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 06:56:49,334][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 06:56:49,836][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 06:56:50,337][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 06:56:50,838][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 06:56:51,337][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 06:56:51,833][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 06:56:52,331][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 06:56:52,825][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 06:56:53,326][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 06:56:53,823][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 06:56:54,319][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 06:56:54,819][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 06:56:55,317][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 06:56:55,812][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 06:56:56,313][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 06:56:56,807][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 06:56:57,297][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 06:56:57,787][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 06:56:58,276][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 06:56:58,777][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 06:56:59,265][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 06:57:05,156][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 06:57:05,647][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 06:57:06,137][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 06:57:06,629][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 06:57:07,118][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 06:57:07,605][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 06:57:08,094][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 06:57:08,584][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 06:57:09,075][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 06:57:09,574][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 06:57:10,067][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 06:57:10,557][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 06:57:11,046][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 06:57:11,535][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 06:57:12,029][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 06:57:12,519][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 06:57:13,010][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 06:57:13,505][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 06:57:13,994][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 06:57:14,498][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 06:57:14,988][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 06:57:15,477][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 06:57:15,966][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 06:57:16,455][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 06:57:16,944][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 06:57:17,433][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 06:57:17,922][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 06:57:18,412][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 06:57:18,899][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 06:57:19,388][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 06:57:19,890][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 06:57:20,380][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10895 tokens. [2025-11-13 06:57:21,147][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.21%, Current % of VRAM taken: 59.66%, Block Peak % of device VRAM: 62.41%, ΔTime: 00:00:32 [2025-11-13 06:57:21,776][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 06:57:21,778][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 06:57:21,779][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 06:57:22,566][__main__][INFO] - Iteration 567 took 1m 30s (60.63% Gen, 38.49% Train). Generation: 54s, Training: 34s. Estimated remaining time: 66h 31m 10s. Estimated total time: 75h 18m 51s. Time estimates for 10 more iterations: 15m 3s, 100 more iterations: 2h 30m 37s, 500 more iterations: 12h 33m 8s. [2025-11-13 06:57:22,568][__main__][INFO] - Starting iteration 567. [2025-11-13 06:57:23,036][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 56 and human policies 1. [2025-11-13 06:57:23,037][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 06:57:40,233][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,260][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,263][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,266][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,269][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,271][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,274][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,276][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,279][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,282][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,284][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,286][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,288][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,290][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,290][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,291][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,294][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,296][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,301][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,302][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,304][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,306][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,307][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,309][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,310][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,312][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,315][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,318][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,320][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,322][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,323][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,325][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,326][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,328][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,329][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,330][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,332][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,333][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,335][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,341][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,342][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,343][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,345][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,348][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,350][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,351][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,352][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,354][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,355][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,356][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,357][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,358][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,361][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,362][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,365][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,367][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,368][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,370][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,371][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,373][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,375][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,376][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,378][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,379][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,380][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,382][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,384][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,385][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,386][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,387][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,389][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,390][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,400][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,402][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,403][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,405][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,407][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,408][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,409][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,411][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,414][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,416][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,418][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,420][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,422][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,424][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,425][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,426][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,427][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,428][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,430][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,431][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,433][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,434][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,435][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,436][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,437][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,438][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,610][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,628][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,631][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,633][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,636][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,638][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,641][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,649][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,651][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,653][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,655][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,657][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,659][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,661][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,664][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,665][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,667][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,669][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,671][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,683][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,685][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,687][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,689][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,691][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,693][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,694][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,696][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,697][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,699][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,700][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,701][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,703][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,704][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,706][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,707][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,708][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,710][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,711][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,713][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,714][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,716][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,717][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,719][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,720][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,732][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,733][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,735][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,736][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,738][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,740][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,741][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,742][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,744][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,745][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,747][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,748][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,750][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,752][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,754][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,756][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,757][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,759][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,760][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,761][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,762][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,762][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,763][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,764][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,766][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,767][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,769][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,771][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:40,777][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,778][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,780][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,781][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,782][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,784][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,786][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,787][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,788][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,790][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,790][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,791][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,792][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,793][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,795][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,796][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,797][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,798][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,798][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,799][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,799][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,800][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,800][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,804][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,805][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:40,983][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,029][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,035][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,042][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,047][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,052][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,057][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,061][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,067][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,069][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,071][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,074][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,078][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,082][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,085][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,089][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,093][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,096][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,099][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,111][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,113][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,116][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,118][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,120][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,122][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,125][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,128][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,130][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,132][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,135][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,138][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,140][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,143][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,146][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,148][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,151][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,153][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,155][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,158][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,159][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,162][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,166][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,168][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,171][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:41,172][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,174][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,176][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,191][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,194][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,196][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,199][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,201][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,204][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,206][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,208][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,210][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,213][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,215][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,217][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,218][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,221][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,223][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,225][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,227][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,229][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,232][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,234][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,235][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,238][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,240][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,243][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:41,244][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:41,246][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,248][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,250][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,253][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,255][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,258][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,260][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,262][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,264][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,266][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,268][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,270][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,272][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,274][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,276][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,279][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,281][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,284][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,286][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,288][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,290][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,292][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,298][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,300][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,302][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,641][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:41,699][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:41,937][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:41,940][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:42,341][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,345][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,348][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,369][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,372][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,374][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,377][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,380][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,383][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,386][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,389][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,399][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,401][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,404][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,406][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,409][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,411][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,425][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:42,427][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,429][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,430][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,431][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,432][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,443][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:42,446][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,447][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,448][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,449][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,450][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,452][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,453][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,455][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,456][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,458][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,460][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,462][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,464][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,465][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,467][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,469][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,475][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,476][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,479][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,480][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,481][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,483][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,485][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,486][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,488][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,490][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,491][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,497][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,499][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,501][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,502][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,503][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,504][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,505][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,507][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,509][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,511][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,513][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,515][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,516][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 9 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,521][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,522][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,523][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,526][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,527][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,529][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,530][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,531][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,533][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,535][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,548][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,549][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,550][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,551][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,553][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,554][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,554][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,555][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,556][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,556][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,558][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,559][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,560][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,561][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,563][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,564][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,565][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,567][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,568][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,569][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,571][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,573][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,574][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,575][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:42,771][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:42,774][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:42,777][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:42,789][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:42,791][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:42,794][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:42,796][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:42,799][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:42,801][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:42,809][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:42,812][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:42,814][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:42,817][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:42,819][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:42,821][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:42,824][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:42,826][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:42,834][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:42,840][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:42,843][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:42,845][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:42,848][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:42,850][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:42,857][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:42,859][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:42,861][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:42,863][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:42,865][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:42,867][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:42,869][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:42,872][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:42,874][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:42,886][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:42,888][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:42,891][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:42,894][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:42,896][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:42,898][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:42,900][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:42,902][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:42,905][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:42,907][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:42,910][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:42,915][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:42,917][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:42,918][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:42,920][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:42,922][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:42,924][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:42,927][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:42,929][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:42,931][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:42,933][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:42,935][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:42,937][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:42,938][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:42,944][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:42,945][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:42,947][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:42,949][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:42,950][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:42,952][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:42,953][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:42,955][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:42,957][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:42,959][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:42,960][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:42,962][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:42,990][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:42,992][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:42,995][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:42,997][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:42,999][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:43,001][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:43,004][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:43,005][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:43,007][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:43,015][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:43,018][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:43,020][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:43,022][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:43,024][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:43,026][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:43,028][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:43,029][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:43,031][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:43,033][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:43,034][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:43,035][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:43,037][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:43,039][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:43,041][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:43,042][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:43,043][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:43,045][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:43,047][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:43,049][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:43,051][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:43,053][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:43,183][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,190][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,197][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,209][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,215][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,220][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,223][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,228][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,231][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,233][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,237][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,240][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,244][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,249][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,253][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,256][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,259][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,264][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,267][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,269][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,272][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,274][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,277][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,279][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,286][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,289][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,291][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,293][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,295][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,297][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,299][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,306][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:43,307][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,309][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,311][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,313][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,315][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,317][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,319][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,321][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,323][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,325][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,327][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,329][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,331][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,334][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,336][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,338][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,340][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,343][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,345][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,347][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,349][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,351][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,354][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,373][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,375][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,378][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,380][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,382][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,385][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,387][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,388][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,391][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,393][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,395][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,397][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,399][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,426][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,428][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,430][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,433][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,435][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,438][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:43,454][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:43,455][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,458][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,460][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,463][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,466][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,469][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,471][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,486][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,488][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,491][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,493][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,495][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,497][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,499][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,502][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,504][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,507][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,509][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,511][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,514][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,518][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,521][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,523][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,525][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:43,866][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:43,921][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:44,090][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:44,175][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:44,600][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:44,603][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:44,621][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:44,624][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:44,626][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:44,629][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:44,631][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:44,643][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:44,646][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:44,648][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:44,650][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:44,667][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:44,670][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:44,672][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:44,675][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:44,677][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:44,679][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:44,689][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:44,691][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:44,694][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:44,696][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:44,698][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:44,701][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:44,703][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:44,705][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:44,707][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:44,708][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:44,709][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:44,714][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:44,714][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:44,715][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:44,716][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:44,718][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:44,719][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:44,720][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:44,721][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:44,722][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:44,723][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:44,724][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:44,726][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:44,727][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:44,729][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:44,730][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:44,739][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:44,741][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:44,743][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:44,744][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:44,746][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:44,748][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:44,750][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:44,752][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:44,761][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:44,763][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:44,764][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:44,765][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:44,766][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:44,767][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:44,769][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:44,771][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:44,772][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:44,809][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:44,812][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:44,814][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:44,816][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:44,817][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:44,818][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:44,820][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:44,822][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:44,824][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:44,826][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:44,828][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:44,830][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:44,842][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:44,844][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:44,845][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:44,846][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:44,847][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:44,850][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:44,852][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:44,854][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:44,855][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:44,856][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:44,858][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:44,860][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:44,862][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:44,863][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:44,865][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:44,867][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:44,874][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:44,876][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:44,878][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:44,880][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:44,881][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:44,882][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:44,884][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:44,885][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:45,063][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,067][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,083][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,085][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,088][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,090][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,102][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,105][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,107][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,110][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,114][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,124][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,127][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,130][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,133][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,135][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,144][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,147][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:45,152][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,154][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,156][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,159][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,162][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,163][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,171][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,173][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,175][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,177][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,178][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,179][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,180][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,182][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,184][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,186][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,188][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,189][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,191][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,193][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,195][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,197][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,198][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,200][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,201][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,203][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:45,204][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,205][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,206][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,207][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,209][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,211][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,213][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,214][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,218][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,220][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,221][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,222][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,223][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,224][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,225][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,226][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,227][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,245][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,247][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,249][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,250][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,251][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,251][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,252][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,265][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,267][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,268][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,269][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,270][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,271][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:45,273][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,275][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,277][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,278][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,285][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,286][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,288][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,289][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,290][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,292][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,293][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,294][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,296][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,298][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,299][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,300][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,302][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:45,303][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,304][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,305][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,306][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,308][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,309][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,310][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,311][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,444][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:45,451][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:45,463][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:45,470][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:45,473][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:45,478][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:45,486][mllm.models.large_language_model_local][WARNING] - Response Given the new per-item values, you have a significantly higher preference for hats and a lower preference for balls compared to books. Bob values books and balls much more than hats. Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:45,488][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:45,492][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:45,497][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:45,501][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:45,509][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:45,513][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:45,517][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:45,520][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:45,523][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:45,527][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:45,529][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:45,552][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:45,554][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:45,556][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:45,560][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:45,562][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:45,565][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:45,569][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:45,573][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:45,576][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:45,585][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:45,588][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:45,591][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:45,594][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:45,596][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:45,599][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:45,602][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:45,604][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:45,607][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:45,609][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:45,615][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:45,620][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:45,623][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:45,627][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:45,630][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:45,633][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,635][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:45,637][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:45,640][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:45,643][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:45,647][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:45,650][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:45,652][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:45,654][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:45,657][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:45,659][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:45,661][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:45,664][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:45,666][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:45,668][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:45,671][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:45,673][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:45,675][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:45,697][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:45,700][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:45,702][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:45,705][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:45,707][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:45,710][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:45,716][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:45,732][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:45,735][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:45,738][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:45,741][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:45,743][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:45,745][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,747][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:45,749][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:45,767][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:45,769][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:45,772][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:45,775][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:45,778][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:45,781][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:45,784][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:45,787][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:45,789][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:45,792][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:45,795][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:45,797][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:45,800][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:45,802][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:45,803][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:45,805][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:45,808][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:45,810][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:45,812][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:45,815][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:45,817][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:45,819][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:46,035][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:46,226][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:46,259][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:46,395][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:46,576][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:46,924][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:46,926][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:46,958][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:46,962][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:46,964][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:46,965][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:46,966][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:46,968][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:46,979][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:46,980][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:46,981][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:46,982][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:47,004][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:47,005][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:47,006][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:47,006][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:47,007][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:47,029][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:47,030][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:47,031][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:47,032][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:47,055][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:47,080][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:47,082][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:47,083][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:47,084][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:47,110][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:47,113][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:47,116][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:47,118][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:47,120][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:47,121][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:47,123][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:47,124][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:47,126][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:47,127][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:47,129][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:47,131][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:47,132][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:47,132][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:47,133][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:47,135][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:47,175][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:47,178][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:47,181][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:47,183][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:47,186][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:47,187][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:47,198][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:47,200][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:47,202][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:47,204][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:47,206][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:47,207][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:47,208][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:47,210][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:47,222][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:47,223][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:47,225][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:47,226][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:47,228][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:47,230][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:47,232][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:47,234][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:47,235][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:47,236][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:47,238][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:47,239][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:47,240][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:47,241][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:47,241][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:47,242][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:47,242][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:47,249][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:47,250][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:47,250][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:47,251][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:47,252][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:47,253][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:47,254][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:47,256][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:47,257][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:47,258][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:47,259][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:47,260][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:47,261][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:47,262][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:47,264][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:47,428][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:47,430][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:47,446][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:47,449][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:47,451][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:47,453][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:47,456][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:47,470][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:47,471][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:47,472][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:47,473][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:47,487][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:47,488][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:47,490][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:47,491][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:47,493][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:47,506][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:47,506][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:47,507][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:47,507][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:47,524][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:47,547][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:47,548][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:47,550][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:47,552][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:47,554][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:47,565][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:47,567][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:47,568][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:47,570][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:47,572][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:47,574][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:47,576][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:47,578][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:47,579][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:47,581][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:47,583][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:47,584][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:47,586][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:47,587][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:47,589][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:47,590][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:47,594][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:47,596][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:47,598][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:47,599][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:47,600][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:47,602][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:47,614][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:47,616][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:47,617][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:47,618][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:47,620][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:47,621][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:47,622][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:47,635][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:47,636][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:47,637][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:47,638][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:47,639][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:47,640][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:47,642][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:47,643][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:47,645][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:47,655][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:47,656][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:47,657][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:47,658][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:47,659][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:47,661][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:47,662][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:47,664][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:47,665][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:47,667][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:47,668][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:47,670][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:47,672][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:47,673][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:47,674][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:47,675][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:47,676][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:47,678][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:47,679][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:47,680][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:47,681][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:47,682][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:47,683][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:47,686][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:47,793][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:47,798][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:47,811][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:47,815][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:47,819][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:47,823][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:47,826][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:47,830][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:47,833][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:47,833][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:47,836][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:47,839][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:47,841][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:47,852][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:47,854][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:47,857][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:47,860][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:47,862][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:47,873][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:47,875][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:47,879][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:47,881][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:47,914][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:47,917][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:47,920][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:47,923][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:47,925][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:47,958][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:47,960][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:47,963][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:47,965][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:47,968][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:47,970][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:47,973][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:47,981][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:47,983][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:47,985][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:47,988][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:47,991][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:47,993][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:47,995][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:47,998][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:48,000][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:48,002][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:48,004][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:48,006][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:48,007][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:48,009][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:48,011][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:48,014][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:48,016][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:48,018][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:48,020][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:48,022][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:48,024][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:48,049][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:48,052][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:48,054][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:48,057][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:48,059][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:48,061][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:48,064][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:48,066][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:48,069][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:48,080][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:48,083][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:48,086][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:48,088][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:48,090][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:48,093][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:48,095][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:48,098][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:48,100][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:48,102][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:48,105][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:48,114][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:48,117][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:48,120][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:48,122][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:48,125][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:48,127][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:48,130][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:48,132][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:48,134][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:48,137][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:48,139][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:48,141][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:48,144][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:48,350][mllm.models.large_language_model_local][WARNING] - Response Given the new per-item values, where my values are higher for hats and books and Bob's values are higher for hats and balls, a strategic proposal would be to propose keeping more of the items where I have higher values. Here’s my proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:48,353][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:48,356][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:48,408][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Bob value books the most, and you both value balls the second most. However, your value for hats is very low compared to both your own and Bob's value for books and balls. Proposing an equal split for hats and books might be a good strategy to ensure that both items are shared fairly, while keeping all the balls for yourself to maximize your points from that highly valued item. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:48,586][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:48,629][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:49,273][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:49,302][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:49,305][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:49,359][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:49,363][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:49,366][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:49,370][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:49,373][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:49,376][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:49,378][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:49,379][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:49,381][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:49,390][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:49,391][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:49,393][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:49,423][mllm.models.large_language_model_local][WARNING] - Response Given the values, both you and Alice place a high value on balls (10) and a low value on hats (1) and books (1). Since Alice values hats the most and books the least, and you value books and balls the most, it's beneficial to propose a distribution that maximizes the points based on your values. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:49,425][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:49,427][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:49,576][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:49,578][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:49,579][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:49,582][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:49,585][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:49,588][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:49,591][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:49,610][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:49,612][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:49,615][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:49,618][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:49,620][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:49,623][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:49,625][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:49,628][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:49,630][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:49,632][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:49,634][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:49,636][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:49,698][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:49,700][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:49,703][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:49,706][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:49,710][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:49,713][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:49,762][mllm.models.large_language_model_local][WARNING] - Response Given the updated values, where your per-item values for hats and books are higher and Bob's per-item value for balls is higher, a more strategic approach might be necessary. Since your value for hats and books is significantly higher than Bob's, you might want to secure more of these items, while leaving the balls for Bob as his value for balls is the highest. Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:49,765][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:49,768][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:49,771][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:49,775][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:49,777][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:49,784][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:49,787][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:49,790][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:49,792][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:49,795][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:49,797][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:49,799][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:49,807][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:49,810][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:49,813][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:49,815][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:49,817][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:49,819][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:49,821][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:49,823][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:49,826][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:49,828][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:49,833][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:49,835][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:49,836][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:49,935][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:49,953][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:49,957][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:49,975][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:49,978][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:49,991][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:49,994][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:49,997][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:50,012][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:50,015][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:50,018][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:50,021][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:50,023][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:50,026][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:50,028][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:50,031][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:50,145][mllm.models.large_language_model_local][WARNING] - Response Given the new per-item values, where your values are hats=10, books=1, balls=1 and Bob's values are hats=1, books=1, balls=10, the optimal strategy would be to maximize the items that you value highly. Since hats are the highest valued item for you, you should propose to take all of them, and then split the remaining items proportionally based on your and Bob's values. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:50,148][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that hats and books are highly valued by both you and Alice, while balls are less valuable. Since the allocations are split proportionally if the total exceeds the item quantity, it's strategic to propose a distribution that maximizes your value while ensuring you don't overclaim and have your proposal reflected proportionally. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:50,152][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:50,155][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:50,158][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:50,160][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:50,163][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:50,166][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:50,168][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:50,171][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:50,173][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:50,176][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:50,178][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:50,208][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:50,209][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:50,212][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:50,214][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:50,217][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:50,219][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:50,223][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:50,225][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:50,227][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:50,271][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:50,274][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:50,276][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:50,279][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:50,282][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:50,285][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:50,289][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:50,290][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:50,292][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:50,294][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:50,295][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:50,298][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:50,300][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:50,302][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:50,304][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:50,317][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:50,319][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:50,320][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:50,322][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:50,324][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:50,411][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:50,435][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:50,451][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:50,454][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:50,470][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:50,488][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:50,494][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:50,499][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:50,508][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:50,513][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:50,515][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:50,517][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:50,520][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:50,530][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the values provided, it seems optimal to propose an equal split to maximize the points based on our values. Here’s the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:50,531][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:50,532][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:50,533][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:50,535][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:50,537][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:50,559][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:50,580][mllm.models.large_language_model_local][WARNING] - Response Given the new values, I should prioritize the items that I value more. Since both agents now value hats and books quite highly but at different items, I need to strategically propose to maximize my points. My per-item values: hats=10, books=10, balls=1 Bob's per-item values: hats=10, books=1, balls=10 Since both of us value hats and books similarly but place high value on different items, a good strategy would be to propose keeping all hats and books for myself, as these are my highest-value items. Let's propose keeping all hats and books for myself and all balls for Bob, as his valuation for balls is the highest. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:50,585][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:50,587][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:50,588][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:50,593][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:50,597][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:50,601][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:50,606][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:50,610][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:50,613][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:50,616][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:50,620][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:50,623][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:50,631][mllm.models.large_language_model_local][WARNING] - Response Given the new values, it seems like both you and Alice have a significant value for hats and balls, but less value for books. Here's a strategic proposal to maximize points: Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:50,632][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:50,633][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:50,634][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:50,635][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:50,637][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:50,638][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:50,641][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:50,643][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:50,646][mllm.models.large_language_model_local][WARNING] - Response Given the values, it appears that the optimal strategy would be to propose taking as much of the items with the highest individual value. In this round, both you and Bob value books the highest at 10, followed by hats at 1 for you and 10 for Bob, and balls at 1 for you and 1 for Bob. Since the total value for books is 100 (10*10) for each of you, it might be beneficial to take all the books. Here's the proposal: Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:50,648][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:50,650][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:50,653][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:50,656][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:50,658][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:50,661][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:50,665][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:50,668][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:50,671][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:50,674][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:50,676][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:50,678][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:50,680][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:50,682][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:50,684][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:50,687][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:50,689][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:50,691][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:50,693][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:50,696][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:50,700][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and the opposition in valuations between you and Alice, a strategic approach is necessary. You value hats and books highly, while Alice values hats and balls highly. If Alice proposes to take all the balls, this leaves a more balanced distribution between hats and books. Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:50,702][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:50,704][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:50,706][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:50,708][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:50,710][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:50,855][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:50,860][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:50,873][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both hats and books are more valuable to Bob, while balls are more valuable to you. In this round, the optimal strategy would be to propose all items to you, as your per-item value for balls is significantly higher than Bob's. Proposal: 0 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:50,917][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:50,924][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:50,930][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:50,936][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:50,951][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:50,954][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:50,957][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:50,959][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:50,962][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:50,964][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:50,970][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:51,013][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both you and Bob have significantly different valuations for the items. You value hats and books more than balls, whereas Bob values balls much more than hats and books. Given your valuations, proposing to get all the items seems optimal to maximize your points. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:51,017][mllm.models.large_language_model_local][WARNING] - Response Given Alice's per-item values, she will likely propose to take all the books and balls, while keeping a few hats for herself. To maximize my points, I should propose to take all the hats and books, and leave the balls for Alice. This strategy aligns with my per-item values. Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:51,021][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:51,028][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:51,043][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that we both place high value on hats and books, and low value on balls. To maximize our points, we should propose to keep the items that we and Alice value the most while considering the proportional allocation rule. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:51,045][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the results of previous rounds, it's clear that proposing to take all the items is a fair and effective strategy since the allocations are proportionate. Therefore, I will continue to propose taking all the items. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:51,069][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Bob consider balls to be of high value, while hats and books are of lower value. To maximize your points, you should propose to take the majority of the balls while splitting the hats and books more evenly. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:51,071][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:51,073][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:51,095][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:51,567][mllm.models.large_language_model_local][WARNING] - Response Given the updated values, hats are valuable for both of us, while books and balls are less valuable. However, since both of us have some value on hats and balls, it's important to consider the proportional distribution to avoid proposing more than what is fair. Proposal: 10 hats, 10 books, 10 balls This proposal evenly splits the items, which seems fair given our values. Both you and Bob value hats, and the others are somewhat valued by each of us. Thus, an even split might be the most balanced approach. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:51,678][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the high value of hats and balls for both parties, and the low value of books, it's beneficial to ensure that both hats and balls, which have high values for you, are maximally allocated. Here's a proposal that maximizes the value based on these considerations: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:51,702][mllm.models.large_language_model_local][WARNING] - Response Given the values, it seems optimal to maximize the items with higher personal value. Since I value books at 10 and balls at 1, and Alice values balls at 10 and books at 1, we should propose to keep as many books and balls as possible, while distributing hats fairly. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:51,752][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:51,823][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:51,830][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:51,833][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:51,835][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:51,855][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Alice place a high value on books and a low value on hats and balls. To maximize the points, you should aim to get as many books as possible while sharing the hats and balls equally or with a slight bias towards you, since you value hats and balls higher than Alice does. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:51,857][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values where I value hats and balls more and books less than Bob, a strategic proposal would be to maximize my points by taking most of the items I value more. Here’s my proposal: Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:51,862][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:51,866][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:51,871][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:51,876][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:51,881][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:51,885][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:51,891][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:51,955][mllm.models.large_language_model_local][WARNING] - Response Given the new per-item values, where you value hats at 10, books at 1, and balls at 1, and Bob values hats at 1, books at 1, and balls at 10, it's clear that Bob highly values the balls while you highly value the hats. Since the books are valued the same by both of us and the total quantity of books is 10, we might consider splitting the books evenly to ensure we both get some value. For the hats and balls, it's best to take as much as possible to maximize our points. Here's a balanced proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:51,958][mllm.models.large_language_model_local][WARNING] - Response Given the values, you have a high value for hats and balls, and Bob values books and balls highly. To maximize your points, you should prioritize keeping items you value highly. Here’s a strategic proposal: Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:51,965][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:51,967][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:51,970][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:51,972][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:52,702][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric and equal distribution of values, the optimal strategy would be to mirror Bob's proposal to ensure that each agent receives a fair share of the items. Here is the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:52,704][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:52,706][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:52,888][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:52,891][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:52,893][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:52,896][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:52,898][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:52,973][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:52,977][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:52,979][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:52,982][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:53,014][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:53,017][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:53,021][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:53,038][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:53,040][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:53,042][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:53,044][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:53,046][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:53,047][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:53,048][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:53,052][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:53,054][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:53,055][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:53,112][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:53,132][mllm.models.large_language_model_local][WARNING] - Response Given Bob's valuation for balls and our own high valuation for balls, we should consider keeping a significant portion of the balls to maximize our points. However, we should also consider Bob's high valuation for books, so we need to propose a fair share for books as well. Here's a balanced proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:53,136][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:53,151][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the high value both you and Alice place on different items, a cooperative yet competitive strategy might be effective. Since the items have differing values to each of you, it might be beneficial to propose a split that maximizes the value for both of you while ensuring neither gains an unfair advantage. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:53,168][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:53,171][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:53,173][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:53,190][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both you and Alice place high value on different items. You value hats and books highly, while Alice values books and balls highly. Proposing an equal split might not be optimal since the values are asymmetric. A strategic proposal would be to allocate items in a way that maximizes your total points. Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:53,210][mllm.models.large_language_model_local][WARNING] - Response Given the values, you have a high value for books (10) and Alice values balls highly (10), while both value hats at 1. In this scenario, a strategic proposal would involve taking the majority of books and balls to capture the higher value items, while taking a share of hats. A reasonable proposal would be to take the maximum of books and balls, and a fair share of hats. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:53,231][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:53,233][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:53,253][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:53,256][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:53,258][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:53,387][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and Bob's strategy in previous rounds, it seems that both Alice and Bob prioritize the items differently, and Bob seems to value "books" more than "balls" and "hats" less than "balls". To maximize my points, I should aim to secure as many "balls" as possible, and "hats" and "books" less heavily. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:53,391][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both hats and balls are highly valued by one of the players, making a strategic proposal crucial. Since hats and balls have high values for both players but books have lower values, we should aim to secure as many high-value items as possible. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:53,397][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in per-item values and the previous outcomes where proposing to take all items led to equal splits and equal points, I will propose the same strategy to maximize my points. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:53,436][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the per-item values, it's clear that the optimal strategy would be to propose the fair split that maximizes the value for both parties. Since we both have the same per-item values for the items, the fairest and most competitive proposal would be to split all items equally. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:53,441][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the structure of the game, it is optimal to propose an equal split to maximize the outcome in each round. Therefore, I will propose: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:53,445][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:53,446][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:53,448][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:53,449][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:53,451][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:53,452][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:53,499][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:53,501][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:53,503][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:53,565][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the values of the items, it appears that both Alice and I value hats and balls highly, but books less so. To maximize points, it makes sense to ensure we both receive our most valued items while splitting the books proportionally. Therefore, a strategic proposal would be to take all of the hats and balls, and split the books equally. Proposal: 10 hats, 10 balls, 10 books / 2 = 10 hats, 10 balls, 5 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:53,568][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values where you have high value for balls and Bob has high value for books, the optimal strategy would be to propose a distribution that maximizes your points based on your values. Here, you should keep as many balls as possible and try to split the hats and books fairly or to your benefit. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:53,575][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:53,578][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:53,581][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:53,583][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:53,586][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the fact that both agents are proposing to take all items in every round, it's reasonable to continue this pattern to maximize points. Here is the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:53,587][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:53,588][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:53,612][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:53,614][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:53,616][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:53,617][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:53,620][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:53,622][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:53,624][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:53,636][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:53,638][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:53,640][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:53,641][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:53,681][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:53,699][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:53,706][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:53,742][mllm.models.large_language_model_local][WARNING] - Response Given the values where you place high value on balls (10) and Bob places high value on books (10), a cooperative strategy might be beneficial. If we both propose to take mostly items we value highly, we can avoid the proportional distribution penalty that occurs if our total proposals exceed the available items. Proposal: 10 hats, 10 balls, 10 books This strategy ensures that we each get the items we value the most, thus maximizing our points. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:53,746][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:53,749][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:53,763][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric and opposite valuations between you and Alice, proposing an allocation where we both maximize our points based on our individual values could be effective. Here’s the proposal: Proposal: 10 hats, 10 books, 10 balls This proposal ensures that both you and Alice receive all the items, and each will receive the items they value the most. Since the total quantity of items is equal to the total items to be split, this direct allocation should work without any proportional redistribution. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:53,766][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:53,809][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:53,815][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:53,820][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:53,832][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and the symmetry in the previous rounds, it's clear that splitting the items equally is a good strategy. Here, you value hats and balls highly, while Bob values books highly. Since the per-item values are similar across rounds and the items are to be split equally, it makes sense to propose an equal split. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:53,838][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:53,841][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:53,843][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:53,845][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:53,846][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:53,847][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:53,850][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:53,862][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric and equal distribution of high value items between you and Alice, a cooperative strategy seems to be the most optimal. Proposing to take the full allocation of items will ensure that both of you maximize your scores based on the high values of the items. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:53,865][mllm.models.large_language_model_local][WARNING] - Response Given the values, it seems optimal to maximize the items with higher individual value for you, which are hats and books. Here's a more strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:53,867][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:53,870][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:53,872][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:53,874][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:53,886][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:53,889][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:53,891][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:53,906][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:53,911][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:53,916][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:53,918][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:53,920][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:53,922][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:53,924][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:53,926][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:53,940][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:53,944][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:53,946][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:53,948][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:53,952][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:53,955][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:53,957][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:53,958][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:53,959][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:53,960][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:53,968][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:53,970][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:53,971][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:53,973][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:53,975][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:53,999][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:54,041][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:54,046][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:54,050][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:54,055][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:54,060][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:54,064][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:54,078][mllm.models.large_language_model_local][WARNING] - Response Given the new values and understanding that Bob values balls significantly more than I do, I need to adjust my strategy to ensure I get a fair share of balls while still valuing hats and books. Here’s a balanced proposal: Proposal: 10 hats, 5 books, 5 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:54,080][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:54,084][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:54,088][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:54,092][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:54,096][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:54,099][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:54,103][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:54,108][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:54,112][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:54,115][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:54,116][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:54,120][mllm.models.large_language_model_local][WARNING] - Response Given that both you and Alice value balls highly but value hats and books lower, it makes sense to propose to keep all the items to maximize the points from the balls. Here is the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:54,453][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:54,458][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:54,465][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:54,540][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:54,543][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:54,547][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:54,554][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:54,618][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:54,640][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:54,643][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:54,646][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:54,648][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:54,649][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:54,649][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:54,650][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:54,684][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:54,689][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:54,691][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:54,694][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:54,696][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:54,698][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:54,732][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:54,735][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:54,737][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:54,759][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:54,762][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:54,765][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:54,767][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:54,770][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:54,771][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:54,773][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:54,775][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:54,787][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:54,791][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:54,803][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:54,806][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:54,808][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:54,809][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:54,810][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:54,811][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:54,813][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:54,817][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:54,822][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:54,826][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:54,828][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:54,890][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:55,036][mllm.models.large_language_model_local][WARNING] - Response Given the current per-item values, it's clear that both you and Bob value hats at 1, but you value books and balls significantly more than Bob does. Therefore, it would be strategic to maximize your share of the balls and books, which you value highly. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:55,092][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:55,303][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric nature of the per-item values and the previous round results, a strategic approach would be to mirror Bob's values to ensure an equal distribution and maximize points. Here’s the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:55,403][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:55,407][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:55,410][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:55,413][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:55,463][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's beneficial to maximize the items with higher personal value. Since both hats and books have high values for Bob, and balls have high value for you, you might want to ensure you get a fair share of the balls while also getting some hats and books. Here's a strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:55,465][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, you have a clear advantage in books and a slight disadvantage in hats and balls. Here’s a proposal that balances the split: Proposal: 5 hats, 10 books, 5 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:55,468][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:55,470][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:55,525][mllm.models.large_language_model_local][WARNING] - Response Given the values, you have a higher valuation for hats and books, while Alice values the balls more. A strategic approach would be to recognize that splitting the items equally might not always be optimal, given the asymmetry in valuations. Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:55,566][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:55,574][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:55,594][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:55,598][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:55,604][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:55,610][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:55,615][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:55,621][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:55,624][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:55,643][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:55,647][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:55,651][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:55,656][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:55,666][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:55,670][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:55,700][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:55,705][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:55,710][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:55,714][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:55,787][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:55,793][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:55,795][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:55,855][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:55,858][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:55,862][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:55,869][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:55,875][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:55,882][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:55,887][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:55,890][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:55,892][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:55,898][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:55,901][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:55,903][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:55,906][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:56,008][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the previous outcomes, it seems that proposing the full amount of each item (10 hats, 10 books, 10 balls) has been yielding 60 points per round. Since both agents are proposing the same amounts and earning the same points, it suggests that the distribution is allocated proportionally and the values are in such a way that a 50% split is optimal. However, to explore potential strategies, let's consider whether proposing a slightly different distribution might lead to a higher allocation for the high-value items (books and balls) for me. Given my per-item values for books and balls are higher, I might try to slightly overload these items to see if it affects the proportional allocation. Proposal: 10 hats, 11 books, 11 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:56,012][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:56,105][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:56,108][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:56,112][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:56,115][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:56,189][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Alice have different preferences for hats, books, and balls. You value hats and books more, while Alice values balls more. To maximize your points, it's strategic to propose a distribution that takes advantage of your higher values for hats and books. Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:56,192][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:56,196][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:56,234][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:56,237][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:56,321][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:56,329][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:56,332][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:56,334][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:56,368][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:56,371][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:56,413][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:56,417][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:56,420][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:56,422][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:56,425][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:56,427][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:56,428][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:56,430][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:56,431][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:56,434][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:56,438][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:56,443][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:56,445][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:56,447][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:56,522][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:56,607][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:56,693][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the values, a strategic approach would be to propose an allocation that reflects a fair split, considering both the per-item values and the likelihood of Bob proposing similarly. Here's a proposal that maximizes mutual benefit while adhering to the values: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:56,697][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:56,781][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:56,785][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:56,789][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:56,793][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:56,796][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:56,912][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:56,915][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:56,971][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:56,975][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:57,123][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the values and the fact that both you and Alice value the same items similarly, it's important to ensure that neither of you gets less than what you value proportionally. A fair and strategic approach is to propose an equal split of items, ensuring that the total value for both of you is maximized. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:57,203][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:57,206][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:57,209][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:57,212][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:57,215][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:57,272][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:57,279][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:57,282][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:57,285][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:57,357][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:58,004][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:58,050][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:58,054][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:58,060][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:58,066][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:58,069][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:58,072][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:58,074][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:58,075][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:58,077][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:58,119][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:58,147][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:58,150][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:58,152][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:58,156][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:58,160][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:58,163][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:58,166][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:58,204][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:58,208][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:58,211][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:58,232][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it is beneficial to propose a distribution that maximizes the value based on your higher valuation of books. Here's a strategic proposal that considers the higher values for books and balls: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:58,239][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:58,241][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:58,244][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:58,246][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:58,248][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:58,250][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:58,252][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:58,254][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:58,256][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:58,259][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:58,446][mllm.models.large_language_model_local][WARNING] - Response Given the values, it seems that both you and Bob have a higher value for books and balls, and a lower value for hats. Proposing to take all the items could lead to a proportional allocation if Bob also proposes to take all, but it might result in a lower allocation if he decides to take only a portion. A more strategic approach might be to try to secure as many books and balls as possible, while still proposing a fair share of hats. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:58,699][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:58,786][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:58,789][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:58,793][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:58,796][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:58,799][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:58,803][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:58,806][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:58,870][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:58,874][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:58,877][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:58,880][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:58,883][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:58,887][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:58,961][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:58,965][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:59,111][mllm.models.large_language_model_local][WARNING] - Response Given the values, you have a high preference for books (10) and a low preference for hats (1) and balls (1). Alice has a high preference for balls (10) and a low preference for hats (1) and books (1). Since the items are split proportionally if the total proposed amount exceeds the available items, it's strategic to propose keeping all the books to maximize your points, as you value them the most. Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:59,115][mllm.models.large_language_model_local][WARNING] - Response Given the values, it appears that both you and Alice have complementary preferences for the items. To ensure we maximize our points, we should each propose to take items that align with our higher values while also ensuring the total quantities don't exceed the available items. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:59,122][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:59,125][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:59,128][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:59,131][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:59,140][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:59,144][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:59,147][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:59,150][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:59,153][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:59,165][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the values provided, both you and Bob are likely to propose the same amount for each item to ensure fairness and maximize points considering our equal per-item value expectations. Therefore, a strategic proposal would be to evenly distribute the items between us. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:59,171][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric nature of the item values and Bob's likely strategic response, a balanced and cooperative proposal might maximize points by leveraging the high mutual value of books and balls while ensuring a fair distribution of hats. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:59,174][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:59,177][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:59,183][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:59,186][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:59,190][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:59,192][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:59,195][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:59,327][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:59,333][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:59,340][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:59,343][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:59,354][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:59,357][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:59,360][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:59,382][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, you should propose an allocation that maximizes your benefit. Since you value hats and books more than balls, and Alice values balls more than hats and books, proposing to take all the items might not be the best strategy as it could lead to a proportional distribution where you receive less than your full value if Alice also overvalues the items you propose to take. A better approach might be to propose a slightly more modest claim but still ensure you capture the items with the highest value for you. Here is a revised proposal: Proposal: 10 hats, 10 books, 5 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:59,384][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:59,386][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:59,388][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:59,390][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:59,453][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that books are the most valuable for both me and Alice. To maximize points, I should primarily focus on securing as many books as possible, while also considering the distribution of hats and balls. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:59,547][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:59,550][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:59,601][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:59,605][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:59,607][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:59,611][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:59,614][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:59,616][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:57:59,623][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:59,652][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:59,654][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:57:59,744][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the values of the items, a reasonable strategy is to propose a division that reflects the equal distribution of values. Since both of us value the same items significantly, we can propose an equal share of each item to avoid any disputes or proportional allocations. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:57:59,748][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:00,089][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:00,169][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in item values and the previous outcomes where both Alice and Bob propose to take all items, it appears that both parties are fairly competitive and aggressive in claiming all available items. However, we should still consider the potential for a cooperative or more strategic approach to maximize our individual points. In this round, since the values are quite uneven (hats and books are highly valued by you, and balls are highly valued by Bob), a more strategic proposal might be to split the items in a way that balances our anticipated individual gains. Proposal: 10 hats, 10 books, 5 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:00,262][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:00,319][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:00,327][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:00,334][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:00,340][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:00,362][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:00,367][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:00,371][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:00,374][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:00,376][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:00,393][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:00,395][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:00,443][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric and balanced nature of the per-item values between you and Bob, a strategy that focuses on splitting the items equally should maximize points. Since both of your highest values are for books and balls, while Bob values hats the most, proposing an equal split would likely yield the best result. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:00,445][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:00,448][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:00,471][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that hats and books are highly valuable to both of us, while balls are less valuable. To maximize the points, we should aim to get as many of the more valuable items (hats and books) as possible. Here is a balanced proposal that tries to secure the maximum value for both hats and books: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:00,474][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:00,476][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:00,495][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:00,498][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:00,500][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:00,503][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:00,505][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:00,508][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:00,510][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:00,620][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:00,714][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:00,716][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:00,718][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:00,720][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:00,721][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:00,722][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:00,724][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:00,787][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:00,790][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:00,792][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:00,793][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:00,794][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:00,795][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:00,796][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:00,864][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:00,867][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:00,904][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:00,907][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:00,910][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:00,930][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:00,933][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:00,936][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:00,939][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:00,942][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:00,945][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:00,948][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:00,951][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:00,954][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:00,956][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:00,996][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:01,000][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:01,003][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:01,005][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:01,009][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:01,091][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that books and balls are highly valued by both Alice and Bob, while hats are valued much less. To maximize points, we should try to secure as many books and balls as possible since they contribute significantly to the final score. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:01,215][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Alice have a mismatched valuation of the items. You highly value books (10) and balls (1), while Alice highly values hats (10). To maximize your points, you should focus on securing more books and balls. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:01,224][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:01,261][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the fact that both you and Bob have high values for books and balls while having a low value for hats, proposing a fair split that considers the high values of books and balls would be reasonable. Here's a proposal that tries to maximize the value of the items you receive: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:01,264][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:01,341][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Alice have a strong preference for hats and books, and a much lower value for balls. proposing equally might lead to an inefficient split. Here, since both of you value hats and books highly, a cooperative approach might yield better results if we can ensure that we both get a significant portion of these valued items. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:01,344][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:01,446][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that hats and books are less valuable to both of us compared to balls. To optimize our points, we should try to maximize our share of the balls. Here's a proposal that focuses on this: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:01,450][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:01,453][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:01,471][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:01,474][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:01,478][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:01,499][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both you and Bob prefer balls the least, followed by hats and then books. However, since the values are very close and the items are identical, a cooperative strategy where you propose to split the items equally is likely to yield the best outcome for both parties. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:01,502][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:01,509][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:01,516][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:01,519][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:01,522][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:01,525][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:01,528][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:01,535][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:01,538][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:01,540][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:01,567][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:01,570][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:01,573][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:01,576][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:01,578][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:01,582][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:01,591][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:01,619][mllm.models.large_language_model_local][WARNING] - Response Given the new values, where both you and Alice have a lower value for hats and books, and a higher value for balls, it's important to secure as many balls as possible while still trying to split hats and books fairly. Here's the proposal: Proposal: 5 hats, 5 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:01,931][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:02,136][mllm.models.large_language_model_local][WARNING] - Response Given that both you and Alice value hats and books similarly and balls very differently, the optimal strategy would be to propose a fair split that maximizes your points based on your higher value for balls. Here is a proposal that balances the split: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:02,140][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:02,143][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:02,145][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:02,146][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:02,148][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:02,150][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:02,151][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:02,152][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:02,154][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:02,175][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:02,177][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:02,260][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:02,267][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:02,287][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:02,294][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:02,370][mllm.models.large_language_model_local][WARNING] - Response Given the symmetrical nature of the per-item values and the robust performance from previous rounds, it seems that proposing to take all items (10 hats, 10 books, 10 balls) has been beneficial. However, it's important to consider the possibility of Alice adjusting her strategy as well. To increase the likelihood of achieving the highest possible points, we can propose slightly different allocations that still total to 10 for each item category, but might subtly affect the distribution and potentially leave some items for Alice if her proposal is not exactly 10 for each category. Proposal: 10 hats, 10 books, 9 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:02,374][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:02,381][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:02,387][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:02,391][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:02,394][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:02,395][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:02,396][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:02,419][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:02,422][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:02,425][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:02,427][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:02,429][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:02,515][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Bob would benefit the most by proposing to take all the balls since they are valued highly by you and the other way around for Bob. This round, I will propose to take all the items to maximize the points based on your high values for hats and books, and Bob's high value for balls. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:02,519][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:02,521][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:02,525][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:02,606][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:02,613][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:02,616][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:02,620][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:02,623][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:02,626][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:02,695][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:02,699][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:02,703][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:02,705][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:02,709][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:02,711][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:02,713][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:02,718][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:02,774][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:02,802][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Bob value balls the most at 10, but the books hold a higher value for you, while hats are valued more by Bob. To maximize your points, you should propose to take all the balls and share the other items more equally. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:02,805][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:02,808][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:02,828][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:02,832][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:02,835][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:02,839][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:02,841][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:02,845][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:02,847][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:02,849][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:02,851][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:02,869][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:02,872][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:02,875][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:02,877][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:02,880][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:02,894][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:02,915][mllm.models.large_language_model_local][WARNING] - Response Given the high value of hats and books for both of us, and the low value of balls, it makes sense to propose to take as many hats and books as possible, while dividing balls more fairly or equally. Here's a proposal that allocates the items to maximize my value: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:02,938][mllm.models.large_language_model_local][WARNING] - Response Given the values, I should focus on maximizing the points from books and balls since they have higher values compared to hats. Here's a balanced proposal to maximize my points: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:02,942][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:02,945][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:02,963][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric nature of the per-item values and the consistent outcomes where both you and Alice propose the same amounts and end up with the same points, it seems that a cooperative strategy might be optimal. In this round, since both you and Alice have the same per-item values for hats and books, and different but complementary values for balls, a fair split might still be beneficial. Proposal: 10 hats, 10 books, 10 balls / 2 = 5 hats, 5 books, 10 balls / 2 = 5 balls This proposal allocates all hats and books equally and all balls equally, ensuring that you maximize your points by leveraging the higher values of hats and books relative to Alice's values, while also securing the balls, which Alice values higher. Proposal: 10 hats, 10 books, 5 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:02,970][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:02,974][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:02,977][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:02,980][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:02,982][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:02,984][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:02,985][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:02,987][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:02,990][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:03,195][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's beneficial to propose a splitting that maximizes your points. Since you value hats and balls more than Alice, and books less, a strategic proposal could be to take a larger share of the highly valued items for you. Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:03,241][mllm.models.large_language_model_local][WARNING] - Response Given the value distributions, it's clear that both you and Alice have a significant value for books and balls, and only a minimal value for hats. To maximize your points, you should focus on claiming as many books and balls as possible while sharing hats proportionally since their value is minimal for you. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:03,326][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:03,330][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:03,333][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:03,340][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:03,346][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:03,353][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:03,359][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:03,365][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:03,367][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:03,372][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:03,377][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:03,380][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:03,382][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:03,384][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:03,387][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:03,388][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:03,406][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:03,407][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:03,409][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:03,411][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:03,413][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:03,414][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:03,415][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:03,417][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:03,504][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:03,585][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:03,588][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:03,591][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:03,666][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:03,669][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:03,672][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:03,676][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:03,679][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:03,683][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:03,686][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:03,689][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:03,692][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:03,752][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the values, it seems that splitting the items 50-50 is a fair and optimal strategy. Let's propose an equal split to maximize the use of items and ensure both Alice and Bob benefit equally from the negotiation. Proposal: 5 hats, 5 books, 5 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:03,756][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:03,765][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:03,768][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:03,771][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:03,775][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:03,831][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:03,992][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:04,071][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:04,151][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:04,158][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:04,164][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:04,171][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:04,178][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:04,183][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:04,189][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:04,192][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:04,194][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:04,197][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:04,388][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:04,391][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:04,395][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:04,398][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:04,469][mllm.models.large_language_model_local][WARNING] - Response Given the values, it seems both you and Alice place a high value on hats and books, and a low value on balls. To maximize points, we should consider proposing to take more of the items that we value more. Here's a strategic approach: - You value hats and books more, so propose to take most of these items. - Alice values balls more, so she will likely propose to take most of the balls. To maximize your points, you should propose to take all the hats and books, while leaving the balls for Alice. Here is the proposal: Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:04,554][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:04,558][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:04,561][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:04,563][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:04,564][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:04,648][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:05,090][mllm.models.large_language_model_local][WARNING] - Response Given Bob's valuation of books and balls, it's clear he values books and balls significantly more than hats. To maximize my points, I should propose taking the majority of the books and balls to leverage my higher valuations for those items. Here's a strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:05,135][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:05,139][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:05,210][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:05,215][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:05,222][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:05,229][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:05,235][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:05,240][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:05,246][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:05,252][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:05,257][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:05,262][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:05,268][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:05,273][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:05,278][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:05,282][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:05,288][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:05,292][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:05,297][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:05,301][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:05,306][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:05,310][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:05,315][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:05,319][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:05,323][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:05,328][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:05,332][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:05,336][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:05,340][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:05,345][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:05,346][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:05,349][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:05,353][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:05,355][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:05,357][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:05,358][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:05,360][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:05,362][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:05,364][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:05,365][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:05,396][mllm.models.large_language_model_local][WARNING] - Response Given the values of the items, where hats and books are much more valuable to both of us compared to balls, and considering that Alice might propose to take most of the hats and books, it makes sense to propose to take the majority of the items ourselves to maximize our points. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:05,399][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:05,401][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:05,402][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:05,403][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:05,405][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:05,585][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:05,637][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:05,640][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:05,643][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:05,645][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:05,668][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:05,671][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:05,694][mllm.models.large_language_model_local][WARNING] - Response Given our values and Bob's, it makes sense to split the items in a way that maximizes the value for both of us. Since we both value hats and books higher and balls lower, we can propose to each get 10 hats, 10 books, and share the balls. Let's propose to each get 5 balls to ensure both of us get a good share of the lower-valued items. Proposal: 10 hats, 10 books, 5 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:05,724][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:05,728][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:05,751][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:05,755][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:05,781][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it is beneficial to propose a distribution that maximizes the value based on our individual and Bob's valuation. Since both you and Bob value hats and books highly, and balls less, it's strategic to propose a split that considers these values. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:05,807][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:05,811][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:05,833][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:05,835][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:05,858][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:05,883][mllm.models.large_language_model_local][WARNING] - Response Given the values, both you and Alice place the highest value on hats and books, and have some value for balls. However, splitting the items equally didn't yield the best results in previous rounds. Since both you and Alice value hats and books highly and balls minimally, a competitive approach might be more effective. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:05,886][mllm.models.large_language_model_local][WARNING] - Response Given the values, I should propose to take the items that align with my high value for balls while also considering the distribution of values to balance the points effectively. Here is my proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:05,889][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:05,924][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Bob place high value on different items. You value hats and books highly but lowly value balls, while Bob values balls highly but lowly values hats and books. Proposing to split all items equally might not be optimal due to the differing valuations. Here's a proposal that maximizes your points by ensuring you get the items you value highly: Proposal: 10 hats, 10 books, 10 balls This proposal ensures you get all hats and books, which are highly valued by you, and a share of balls, which have moderate value. Given the symmetric nature of the problem and the previous rounds, this is a balanced and likely optimal proposal. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:05,928][mllm.models.large_language_model_local][WARNING] - Response Given the values, I should aim to maximize my points by claiming the items I value the most while also considering Alice's value for those items. Here, hats and books are valued the same by both of us, but I value them higher than balls. Given the symmetry, proposing the same distribution as Alice might lead to a fair split. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:05,935][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the fact that both you and Alice value hats and balls similarly but Alice values books more, a balanced proposal might be the best strategy. However, since Alice values books higher, you might need to adjust your proposal slightly to account for this. Proposal: 10 hats, 10 books, 9 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:05,939][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:05,949][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric nature of the per-item values and the fact that both players are optimizing their points, the optimal strategy here is to propose an equitable split that maximizes the total points for both players without overproposing. Let's consider a proposed allocation that respects the values each of us has for different items: - Hats: You value at 1, Bob values at 10. - Books: You value at 10, Bob values at 1. - Balls: You value at 10, Bob values at 10. A fair and effective proposal might be to split the items in a way that balances the values we place on each type of item. Since both of us value hats and balls higher than Bob, and books more, a fair proposal could be: Proposal: 10 hats, 10 books, 10 balls This proposal assumes that both you and Bob would want to maximize your points by respecting the values each places on different items. However, to ensure a fair and balanced outcome, we should consider splitting the books and balls more equitably since both of us value them highly. A more nuanced proposal might be: Proposal: 5 hats, 10 books, 10 balls This proposal divides the books and balls more fairly, taking into account the values each of us places on these items. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:05,953][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that balls are highly valuable to both of us, and books are also valued much more than hats. To maximize my points, I should try to secure as many balls as possible, followed by books, while considering that Bob values books more. Here's a more strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:06,013][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Bob place the highest value on books and balls, and the lowest value on hats. Since the values are symmetric and both of you will likely propose to take all items, a cooperative approach might yield better results. Here's a proposal that maximizes the value based on your higher valuation of hats and balls: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:06,039][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:06,042][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:06,053][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:06,056][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:06,059][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:06,066][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:06,071][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:06,077][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:06,083][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:06,088][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:06,093][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:06,096][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:06,098][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:06,100][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:06,101][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:06,102][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:06,103][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:06,104][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:06,165][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:06,166][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:06,168][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:06,171][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:06,174][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:06,176][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:06,180][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:06,270][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:06,355][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:06,358][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:06,363][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:06,366][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:06,442][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:06,445][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:06,618][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:06,621][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:06,703][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:06,706][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:06,786][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:06,869][mllm.models.large_language_model_local][WARNING] - Response Given that both you and Alice value hats and books highly and balls less, it's crucial to secure as many hats and books as possible while leaving enough balls for Alice to take. A balanced approach is to propose a distribution that maximizes your value without overextending. Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:06,872][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:06,874][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:06,956][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:06,959][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:07,046][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:07,139][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:07,142][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:07,149][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:07,306][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:07,309][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:07,406][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:07,409][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:07,416][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:07,419][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:07,788][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:07,791][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:07,793][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:07,796][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:07,799][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:07,802][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:07,805][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:07,808][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:07,810][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:07,813][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:07,815][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:08,506][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:08,509][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:08,512][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:08,514][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:08,517][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:08,521][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:08,573][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:08,577][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:08,583][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:08,590][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:08,596][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:08,602][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:08,609][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:08,615][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:08,621][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:08,627][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:08,634][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:08,638][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:08,643][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:08,648][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:08,653][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:08,658][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:08,663][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:08,668][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:08,723][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:08,726][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:08,771][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:08,778][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:08,784][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:08,869][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:08,950][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:08,953][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:08,956][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:08,971][mllm.models.large_language_model_local][WARNING] - Response Given the values, hats and books have low value for both of us, but balls are highly valued by you and slightly valued by Alice. To maximize points, you should propose to take as many balls as possible while splitting hats and books fairly. Here's a proposal that maximizes your points: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:08,975][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:09,048][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:09,051][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:09,068][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:09,071][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:09,073][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:09,077][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:09,079][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:09,095][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:09,098][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:09,101][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:09,104][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:09,106][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:09,109][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:09,112][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:09,114][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:09,117][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:09,120][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:09,123][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:09,129][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:09,132][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:09,135][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:09,137][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:09,140][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:09,170][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:09,173][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:09,195][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:09,202][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:09,205][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:09,604][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:09,605][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:09,607][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:09,608][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:09,611][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:09,612][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:09,613][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, the optimal strategy would be to align the distribution of items with the higher values each of us has for different items. Since I value hats and balls at 1 and books at 10, and Alice values books and balls at 10 and hats at 1, proposing the maximum of each item (10 hats, 10 books, 10 balls) ensures that we each get the maximum value for our preferred items. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:09,613][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:09,614][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:09,615][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:09,616][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:09,617][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:09,618][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:09,618][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it is clear that both you and Alice place the highest value on balls. However, to maximize your points, you should try to secure as many balls as possible while also getting some hats and books to avoid a proportional split that might not favor you as much. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:09,619][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:09,620][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:09,621][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:09,621][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:09,622][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:09,623][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:09,624][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:09,624][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's beneficial to allocate more resources to items where both you and Bob value them highly. In this case, hats and books are valued highly by both of you, while balls are valued more by Bob. A strategic proposal could be to maximize the high-value items. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:09,625][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:09,627][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:09,629][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:09,631][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:09,633][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:09,635][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:09,637][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:09,638][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:09,640][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:09,642][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:09,644][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:09,646][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:09,648][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:09,650][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:09,652][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:09,654][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:09,655][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:09,656][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:09,657][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:09,664][mllm.models.large_language_model_local][WARNING] - Response Given the values, it seems strategic to consider Bob's lower valuation of hats and books, and your higher valuation of books and balls. Here's a proposal that maximizes your value: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:09,666][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:09,667][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:09,668][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:09,669][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:09,670][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:09,670][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:09,671][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:09,672][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:09,673][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:09,674][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:09,675][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:09,676][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:10,999][mllm.models.large_language_model_local][WARNING] - Response Given the high value of books for both of us, and the fact that these items are finite, it's important to prioritize securing as many high-value books as possible. Let's focus on that while still ensuring we get some balls to cover the low-value items. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:11,006][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both you and Alice have a higher value for books and balls compared to hats. To maximize your points, you should try to secure more of the items you value higher. Here's a strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:11,250][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the proviso that both agents are equally likely to have the same expected per-item value, a cooperative strategy might be beneficial. However, to maximize your points, you should try to secure the items with the highest value to you. Since you value hats (1), books (10), and balls (10) highly, and Alice values them as hats (10), books (1), and balls (10) respectively, splitting items evenly would not be ideal. Instead, you should try to secure as many books and balls as possible, while taking a few hats to balance the distribution. Proposal: 5 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:11,257][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric and equal per-item values and the nature of the game, it makes sense to propose an equal split of the items to maximize the points for both you and Alice. Here’s the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:11,334][mllm.models.large_language_model_local][WARNING] - Response Given the values, you have a significant advantage in valuing hats and books (both at 10), while Alice values balls more highly. To maximize your points, you should propose to take all items that you value highly to ensure you get a higher portion of items you value more. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:11,396][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both you and Bob value balls the most, followed by hats, and then books. To maximize your points, you should try to claim more of the items you value more, while still considering Bob's preferences. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:11,902][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:11,906][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:11,909][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:11,915][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:11,922][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:11,925][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:11,927][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:11,930][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:11,933][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:11,935][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:11,938][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:11,941][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:11,944][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:11,947][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:11,950][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:11,953][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:11,981][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:12,119][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:12,121][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:12,206][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:12,209][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:12,286][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:12,290][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:12,368][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:12,450][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:12,453][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:12,541][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:12,544][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:12,547][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:12,629][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:12,632][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:12,648][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:12,669][mllm.models.large_language_model_local][WARNING] - Response Given the values, it is clear that both you and Bob have a higher valuation for balls compared to hats and books. To maximize your points, you should try to capture more of the high-value balls. Here is a proposal that emphasizes this: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:12,672][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:12,675][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:12,689][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:12,692][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:12,709][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:12,712][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:12,727][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:12,730][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:12,944][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:12,947][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:12,950][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:12,953][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:12,955][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:12,959][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:12,961][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:12,964][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:12,967][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:12,970][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:12,972][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:12,975][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:12,978][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:12,980][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:12,983][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:12,986][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:12,988][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:12,991][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:12,994][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:13,021][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:13,024][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:13,027][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:13,085][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:13,088][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:13,101][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:13,118][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:13,120][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:13,139][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:13,142][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:13,154][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:13,157][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:13,171][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:13,174][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:13,219][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:13,222][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:13,235][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:13,238][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:13,241][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:13,244][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:13,252][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:13,269][mllm.models.large_language_model_local][WARNING] - Response Given the values, books and balls are highly valued by both Alice and yourself. However, to maximize points, we should try to secure more of the highly valued items. Here's a strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:13,285][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:13,300][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:13,303][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:13,306][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:13,308][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:13,321][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:13,324][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:13,327][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:13,329][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:13,332][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:13,335][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:13,338][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:13,340][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:13,343][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:13,345][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:13,352][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:13,358][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:13,365][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:13,371][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:13,374][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:13,380][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:13,386][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:13,392][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:13,397][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:13,407][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:13,412][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:13,417][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:13,424][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:13,429][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:13,435][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:13,454][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:13,459][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:13,474][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:13,479][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:13,492][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:13,498][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:13,510][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:13,513][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:13,572][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:13,574][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:13,586][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:13,589][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:13,592][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:13,605][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:13,608][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:13,622][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:13,639][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:13,656][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:13,659][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:13,662][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:13,674][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:13,677][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:13,683][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:13,690][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:13,696][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:13,705][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:13,711][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:13,717][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:13,723][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:13,729][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:13,742][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:14,060][mllm.models.large_language_model_local][WARNING] - Response Given Bob's valuation of books and balls higher than hats, and your own valuation of balls much higher than hats and books, it's crucial to secure as many balls as possible. Here's a strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:14,063][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:14,069][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:14,079][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:14,086][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:14,092][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:14,155][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:14,161][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:14,282][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:14,324][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:14,331][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:14,337][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:14,408][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:14,563][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:14,566][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:14,569][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:14,572][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:14,575][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:14,578][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:14,580][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:14,583][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:14,598][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:14,601][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:14,604][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:14,606][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:14,641][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:14,644][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:14,647][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:14,658][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:14,676][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:14,693][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric nature of the per-item values and the previous outcomes where proposing to take all items resulted in equal splits, it seems that proposing to take all items is a stable strategy. However, to ensure a strategic approach, we can consider a slightly conservative proposal to avoid the risk of equal splits where both might risk proposing all items. Proposal: 9 hats, 9 books, 9 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:14,817][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:14,821][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:14,848][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:14,851][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:14,854][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:14,864][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:14,867][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:14,878][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:14,881][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:14,884][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:14,886][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:14,893][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:14,896][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:14,899][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:14,902][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:14,904][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:14,907][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:14,910][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:14,913][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:14,932][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:14,935][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:14,937][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:14,940][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:14,965][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:14,968][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:14,971][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:14,982][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:14,997][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:15,012][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:15,041][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:15,044][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:15,054][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:15,068][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:15,071][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:15,074][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:15,111][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:15,114][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:15,139][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:15,142][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:15,145][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:15,155][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:15,158][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:15,171][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:15,174][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:15,177][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:15,188][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:15,191][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:15,198][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:15,204][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:15,210][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:15,217][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:15,223][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:15,230][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:15,236][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:15,249][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:15,254][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:15,260][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:15,265][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:15,270][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:15,276][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:15,281][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:15,288][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:15,293][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:15,299][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:15,344][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:15,347][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:15,361][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:15,377][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:15,380][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:15,383][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:15,426][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:15,432][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:15,458][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:15,464][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:15,470][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:15,479][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:15,485][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:15,494][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:15,501][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:15,507][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:15,516][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:15,667][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:15,674][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:15,705][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:15,721][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:15,729][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:15,735][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:15,872][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:15,875][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:15,923][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:15,935][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:15,938][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:16,020][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:16,023][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:16,077][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:16,080][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:16,089][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:16,092][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:16,134][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:16,145][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:16,148][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:16,158][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:16,160][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:16,223][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:16,226][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:16,277][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:16,280][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:16,289][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:16,296][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:16,339][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:16,351][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:16,358][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:16,365][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:16,368][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:16,447][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:16,453][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:16,507][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:16,514][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:16,599][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:16,606][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:16,743][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:16,746][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:16,929][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:16,932][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:17,000][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:17,003][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:58:17,109][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:17,116][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:17,188][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:17,191][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:58:17,378][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:17,384][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:58:18,094][__main__][INFO] - Number of regex retries in iteration 567: 2604 [2025-11-13 06:58:18,095][__main__][INFO] - agents played in iteration 567 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 06:58:19,080][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:58:19,104][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:58:19,128][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:58:19,150][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:58:19,150][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 06:58:19,151][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 06:58:19,910][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 06:58:20,359][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 06:58:20,851][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 06:58:21,342][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 06:58:21,830][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 06:58:22,318][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 06:58:22,808][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 06:58:23,300][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 06:58:23,794][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 06:58:24,290][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 06:58:24,781][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 06:58:25,278][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 06:58:25,771][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 06:58:26,264][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 06:58:26,756][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 06:58:27,250][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 06:58:27,743][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 06:58:28,236][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 06:58:28,737][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 06:58:29,234][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 06:58:29,730][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 06:58:30,229][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 06:58:30,728][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 06:58:31,225][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 06:58:31,722][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 06:58:32,216][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 06:58:32,712][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 06:58:33,225][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 06:58:33,719][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 06:58:34,217][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 06:58:34,715][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 06:58:35,215][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 06:58:35,719][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 06:58:36,219][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 06:58:36,717][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 06:58:37,221][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 06:58:37,717][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 06:58:38,215][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 06:58:38,727][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 06:58:39,224][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 06:58:39,725][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 06:58:40,224][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 06:58:40,722][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 06:58:41,228][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 06:58:41,729][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 06:58:42,232][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 06:58:42,734][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 06:58:43,232][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 06:58:43,732][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 06:58:44,230][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 06:58:44,727][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 06:58:45,231][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 06:58:45,731][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 06:58:46,231][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 06:58:46,739][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 06:58:47,238][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 06:58:47,736][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 06:58:48,239][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 06:58:48,736][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 06:58:49,237][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 06:58:49,733][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 06:58:50,228][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 06:58:50,729][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 06:58:51,228][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 06:58:51,729][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10880 tokens. [2025-11-13 06:58:52,570][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.30%, Current % of VRAM taken: 59.75%, Block Peak % of device VRAM: 62.41%, ΔTime: 00:00:32 [2025-11-13 06:58:53,231][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 06:58:53,234][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 06:58:53,235][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 06:58:54,002][__main__][INFO] - Iteration 568 took 1m 30s (60.53% Gen, 38.63% Train). Generation: 55s, Training: 35s. Estimated remaining time: 66h 59m 5s. Estimated total time: 75h 48m 17s. Time estimates for 10 more iterations: 15m 9s, 100 more iterations: 2h 31m 36s, 500 more iterations: 12h 38m 2s. [2025-11-13 06:58:54,004][__main__][INFO] - Starting iteration 568. [2025-11-13 06:58:54,515][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 56 and human policies 1. [2025-11-13 06:58:54,516][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 06:59:13,711][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:13,737][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:13,739][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:13,741][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:13,744][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:13,747][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:13,750][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:13,752][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:13,755][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:13,757][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:13,759][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:13,761][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:13,765][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:13,768][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:13,771][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:13,779][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:13,782][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:13,784][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:13,786][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:13,789][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:13,791][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:13,794][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:13,796][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:13,804][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:13,807][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:13,809][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:13,811][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:13,813][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:13,816][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:13,818][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:13,820][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:13,822][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:13,824][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:13,826][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:13,828][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:13,831][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:13,833][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:13,835][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:13,836][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:13,838][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:13,840][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:13,841][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:13,842][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:13,843][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:13,845][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:13,849][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:13,851][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:13,861][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:13,863][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:13,865][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:13,867][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:13,868][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:13,869][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:13,870][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:13,871][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:13,872][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:13,874][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:13,876][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:13,877][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:13,878][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:13,879][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:13,880][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:13,882][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:13,882][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:13,884][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:13,885][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:13,885][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:13,885][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:13,886][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:13,887][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:13,887][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:13,888][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:13,889][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:13,890][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:13,893][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:13,894][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:13,896][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:13,898][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:13,899][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:13,900][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:13,901][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:13,902][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:13,904][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:13,905][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:13,906][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:13,907][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:13,908][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:13,909][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:13,910][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:13,912][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:13,913][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:13,914][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:13,915][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:13,916][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:13,917][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:14,030][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,050][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,052][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,055][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,058][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,060][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,064][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,070][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,073][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,075][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,078][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,081][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,082][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,083][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,085][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,087][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,092][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,094][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,096][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,098][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,100][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,103][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,104][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,108][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,109][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,109][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,110][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,111][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,113][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,115][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,117][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,119][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,121][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,123][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,124][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,126][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,128][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,129][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,130][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,133][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,135][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,138][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,139][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,140][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,141][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,144][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,145][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,153][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,154][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,156][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,157][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,169][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,171][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,173][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,174][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,176][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,177][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,179][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,180][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,181][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,182][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,184][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,185][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,186][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,191][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,192][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,193][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,194][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,196][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,197][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,199][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,201][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,202][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,203][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,204][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,205][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,207][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,208][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,209][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,210][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,212][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,214][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,215][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,216][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,218][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,219][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,221][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,222][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,223][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,223][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,224][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,224][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,225][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,226][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,229][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:14,320][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:14,335][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:14,340][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:14,343][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:14,346][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:14,352][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:14,355][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:14,357][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:14,360][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:14,362][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:14,371][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:14,374][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:14,378][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:14,381][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:14,383][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:14,385][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:14,387][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:14,391][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:14,394][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:14,396][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:14,398][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:14,400][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:14,402][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:14,404][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:14,405][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:14,407][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:14,409][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:14,412][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:14,414][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:14,416][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:14,418][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:14,420][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:14,422][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:14,423][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:14,425][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:14,427][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:14,430][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:14,432][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:14,435][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:14,437][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:14,439][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:14,441][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:14,443][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:14,445][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:14,447][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:14,449][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:14,452][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:14,454][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:14,456][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:14,458][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:14,470][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:14,472][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:14,474][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:14,477][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:14,479][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:14,481][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:14,494][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:14,496][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:14,499][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:14,501][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:14,503][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:14,506][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:14,508][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:14,510][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:14,513][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:14,515][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:14,519][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:14,522][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:14,524][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:14,526][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:14,528][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:14,531][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:14,533][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:14,535][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:14,537][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:14,539][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:14,541][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:14,543][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:14,545][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:14,549][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:14,551][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:14,553][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:14,555][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:14,558][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:14,560][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:14,562][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:14,564][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:14,566][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:14,568][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:14,570][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:14,573][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:14,574][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:14,577][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:14,580][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:15,598][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,602][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,622][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,625][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,648][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,651][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,653][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,656][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,658][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,672][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,674][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,677][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,679][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,697][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,700][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,703][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,705][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,708][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,710][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,721][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,724][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,726][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,729][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,731][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,734][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,736][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,739][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,741][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,744][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,752][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,754][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,757][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,759][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,761][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,763][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,765][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,767][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,769][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,772][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,779][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,782][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,784][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,786][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,788][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,790][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,792][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,794][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,797][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,799][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,805][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,808][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,810][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,811][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,814][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,816][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,818][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,820][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,826][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,828][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,831][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,833][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,834][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,836][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,839][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,846][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,848][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,849][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,851][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,852][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,854][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,855][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,856][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,858][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,867][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,869][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,871][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,872][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,874][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,875][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,877][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,878][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,880][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,881][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,883][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,885][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,890][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,892][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,894][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,895][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,897][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,898][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,900][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,901][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,903][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,904][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:15,986][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:15,988][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:16,004][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:16,007][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:16,021][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:16,024][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:16,026][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:16,029][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:16,031][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:16,038][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:16,041][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:16,043][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:16,046][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:16,057][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:16,060][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:16,062][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:16,065][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:16,067][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:16,076][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:16,079][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:16,081][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:16,084][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:16,086][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:16,088][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:16,090][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:16,092][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:16,099][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:16,101][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:16,104][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:16,106][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:16,108][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:16,110][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:16,112][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:16,114][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:16,121][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:16,123][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:16,125][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:16,127][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:16,129][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:16,131][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:16,133][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:16,135][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:16,141][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:16,143][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:16,145][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:16,147][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:16,149][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:16,151][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:16,153][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:16,155][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:16,157][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:16,158][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:16,160][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:16,165][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:16,167][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:16,169][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:16,171][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:16,173][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:16,175][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:16,177][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:16,179][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:16,181][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:16,183][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:16,188][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:16,190][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:16,192][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:16,194][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:16,195][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:16,197][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:16,199][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:16,200][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:16,202][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:16,204][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:16,209][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:16,211][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:16,213][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:16,215][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:16,216][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:16,218][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:16,220][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:16,222][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:16,223][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:16,228][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:16,230][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:16,232][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:16,234][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:16,235][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:16,237][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:16,239][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:16,241][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:16,242][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:16,247][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:16,249][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:16,251][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:16,253][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:16,297][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:16,313][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:16,329][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:16,336][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:16,346][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:16,352][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:16,357][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:16,367][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:16,372][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:16,377][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:16,382][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:16,387][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:16,391][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:16,399][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:16,404][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:16,408][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:16,418][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:16,422][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:16,426][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:16,430][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:16,434][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:16,444][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:16,448][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:16,451][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:16,455][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:16,459][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:16,462][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:16,466][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:16,470][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:16,473][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:16,477][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:16,480][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:16,484][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:16,487][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:16,491][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:16,499][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:16,502][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:16,505][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:16,508][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:16,511][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:16,515][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:16,518][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:16,521][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:16,524][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:16,527][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:16,530][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:16,534][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:16,536][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:16,539][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:16,542][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:16,545][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:16,548][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:16,551][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:16,559][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:16,561][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:16,564][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:16,567][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:16,570][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:16,572][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:16,575][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:16,578][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:16,581][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:16,583][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:16,586][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:16,588][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:16,591][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:16,594][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:16,596][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:16,599][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:16,602][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:16,604][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:16,607][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:16,610][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:16,612][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:16,615][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:16,617][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:16,620][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:16,623][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:16,625][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:16,628][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:16,634][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:16,636][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:16,639][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:16,644][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:16,649][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:16,652][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:16,656][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:16,659][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:16,661][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:16,664][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:16,667][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:16,669][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:16,672][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:16,674][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:16,677][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:17,723][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:17,750][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:17,775][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:17,778][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:17,781][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:17,798][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:17,800][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:17,821][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:17,824][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:17,826][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:17,828][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:17,844][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:17,847][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:17,869][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:17,872][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:17,875][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:17,878][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:17,881][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:17,891][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:17,894][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:17,897][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:17,900][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:17,902][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:17,918][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:17,921][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:17,924][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:17,926][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:17,928][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:17,930][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:17,940][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:17,943][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:17,945][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:17,946][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:17,948][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:17,950][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:17,963][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:17,965][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:17,967][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:17,969][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:17,988][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:17,991][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:17,993][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:18,008][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:18,011][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:18,013][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:18,016][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:18,018][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:18,020][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:18,023][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:18,025][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:18,027][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:18,030][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:18,032][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:18,040][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:18,042][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:18,044][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:18,046][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:18,049][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:18,051][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:18,053][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:18,055][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:18,057][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:18,060][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:18,062][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:18,064][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:18,066][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:18,073][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:18,076][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:18,078][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:18,080][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:18,082][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:18,084][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:18,086][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:18,088][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:18,090][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:18,092][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:18,094][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:18,096][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:18,098][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:18,100][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:18,102][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:18,105][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:18,107][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:18,109][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:18,119][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:18,121][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:18,123][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:18,125][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:18,127][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:18,129][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:18,130][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:18,132][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:18,134][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:18,136][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:18,138][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:18,147][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:18,165][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:18,186][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:18,188][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:18,190][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:18,202][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:18,205][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:18,219][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:18,222][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:18,225][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:18,227][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:18,237][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:18,239][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:18,255][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:18,258][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:18,261][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:18,263][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:18,273][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:18,276][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:18,278][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:18,281][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:18,283][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:18,286][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:18,294][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:18,297][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:18,300][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:18,302][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:18,304][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:18,313][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:18,316][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:18,318][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:18,320][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:18,323][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:18,325][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:18,327][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:18,333][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:18,336][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:18,338][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:18,340][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:18,353][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:18,355][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:18,358][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:18,371][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:18,373][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:18,375][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:18,377][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:18,380][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:18,382][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:18,388][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:18,390][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:18,392][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:18,394][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:18,396][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:18,398][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:18,401][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:18,403][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:18,409][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:18,411][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:18,413][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:18,415][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:18,417][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:18,419][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:18,421][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:18,423][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:18,425][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:18,427][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:18,433][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:18,435][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:18,437][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:18,439][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:18,441][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:18,444][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:18,446][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:18,448][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:18,450][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:18,455][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:18,457][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:18,459][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:18,461][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:18,463][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:18,465][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:18,467][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:18,469][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:18,471][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:18,476][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:18,478][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:18,480][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:18,482][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:18,484][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:18,486][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:18,491][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:18,493][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:18,495][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:18,497][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:18,499][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:18,507][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:18,528][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:18,532][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:18,536][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:18,546][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:18,550][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:18,562][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:18,567][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:18,571][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:18,584][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:18,588][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:18,593][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:18,601][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:18,606][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:18,610][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:18,614][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:18,622][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:18,626][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:18,630][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:18,634][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:18,642][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:18,646][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:18,650][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:18,654][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:18,658][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:18,667][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:18,671][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:18,674][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:18,678][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:18,682][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:18,685][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:18,692][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:18,696][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:18,699][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:18,703][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:18,706][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:18,710][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:18,713][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:18,719][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:18,722][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:18,726][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:18,746][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:18,749][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:18,753][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:18,756][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:18,770][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:18,774][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:18,777][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:18,781][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:18,785][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:18,788][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:18,792][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:18,798][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:18,802][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:18,805][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:18,809][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:18,812][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:18,816][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:18,819][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:18,822][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:18,826][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:18,832][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:18,835][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:18,838][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:18,841][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:18,844][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:18,847][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:18,851][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:18,854][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:18,859][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:18,863][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:18,866][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:18,869][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:18,872][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:18,875][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:18,883][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:18,886][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:18,889][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:18,892][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:18,895][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:18,898][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:18,901][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:18,904][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:18,907][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:18,913][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:18,916][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:18,919][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:18,922][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:18,924][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:18,927][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:18,930][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:18,933][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:18,936][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:19,930][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:19,934][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:20,009][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:20,045][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:20,048][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:20,082][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:20,084][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:20,087][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:20,090][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:20,105][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:20,108][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:20,131][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:20,134][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:20,137][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:20,140][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:20,154][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:20,157][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:20,160][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:20,163][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:20,179][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:20,182][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:20,185][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:20,187][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:20,190][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:20,204][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:20,206][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:20,209][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:20,212][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:20,214][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:20,228][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:20,231][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:20,234][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:20,236][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:20,239][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:20,241][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:20,254][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:20,256][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:20,259][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:20,262][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:20,265][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:20,275][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:20,278][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:20,281][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:20,283][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:20,298][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:20,301][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:20,304][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:20,307][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:20,309][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:20,319][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:20,322][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:20,324][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:20,326][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:20,329][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:20,331][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:20,344][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:20,346][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:20,349][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:20,352][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:20,354][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:20,356][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:20,359][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:20,361][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:20,369][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:20,371][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:20,374][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:20,376][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:20,378][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:20,381][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:20,383][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:20,385][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:20,388][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:20,390][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:20,397][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:20,399][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:20,401][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:20,403][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:20,405][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:20,407][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:20,410][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:20,412][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:20,414][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:20,416][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:20,419][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:20,425][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:20,427][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:20,430][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:20,432][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:20,434][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:20,439][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:20,441][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:20,443][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:20,445][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:20,457][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:20,459][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:20,461][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:20,478][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:20,480][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:20,495][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:20,497][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:20,499][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:20,502][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:20,513][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:20,516][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:20,529][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:20,532][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:20,534][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:20,536][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:20,548][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:20,551][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:20,553][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:20,556][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:20,570][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:20,573][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:20,575][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:20,578][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:20,580][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:20,589][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:20,592][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:20,594][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:20,597][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:20,599][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:20,609][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:20,612][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:20,614][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:20,616][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:20,618][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:20,627][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:20,630][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:20,632][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:20,634][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:20,636][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:20,648][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:20,650][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:20,652][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:20,655][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:20,657][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:20,666][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:20,668][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:20,671][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:20,673][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:20,685][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:20,687][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:20,689][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:20,691][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:20,693][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:20,695][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:20,705][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:20,707][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:20,710][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:20,712][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:20,714][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:20,723][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:20,726][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:20,728][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:20,730][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:20,732][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:20,735][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:20,737][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:20,740][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:20,747][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:20,749][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:20,751][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:20,754][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:20,756][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:20,758][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:20,760][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:20,762][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:20,765][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:20,767][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:20,773][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:20,775][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:20,777][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:20,780][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:20,782][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:20,784][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:20,786][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:20,793][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:20,795][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:20,797][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:20,799][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:20,801][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:20,803][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:20,808][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:20,812][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:20,814][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:20,819][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:20,824][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:20,826][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:20,832][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:20,836][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:20,853][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:20,858][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:20,862][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:20,867][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:20,874][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:20,879][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:20,889][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:20,893][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:20,898][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:20,902][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:20,910][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:20,914][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:20,918][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:20,929][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:20,934][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:20,938][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:20,942][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:20,946][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:20,952][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:20,956][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:20,960][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:20,964][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:20,967][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:20,974][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:20,977][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:20,981][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:20,985][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:20,988][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:20,999][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:21,003][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:21,006][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:21,010][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:21,013][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:21,021][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:21,024][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:21,027][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:21,031][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:21,034][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:21,046][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:21,049][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:21,053][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:21,056][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:21,060][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:21,071][mllm.models.large_language_model_local][WARNING] - Response Given the new per-item values where I value books and balls highly and Bob values hats and books highly, I should propose an allocation that takes advantage of these preferences. Since both of us value books and balls highly, we might want to divide them evenly to ensure we get fair shares. For hats, since I value them less than Bob, I should propose keeping fewer hats to avoid potentially splitting them proportionally and ending up with less. Proposal: 5 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:21,073][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:21,076][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:21,079][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:21,083][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:21,086][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:21,097][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:21,100][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:21,104][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:21,107][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:21,111][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:21,122][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:21,126][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:21,129][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:21,133][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:21,136][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:21,149][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:21,153][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:21,156][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:21,160][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:21,163][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:21,166][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:21,169][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:21,173][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:21,176][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:21,179][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:21,185][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:21,188][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:21,191][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:21,194][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:21,197][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:21,200][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:21,203][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:21,206][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:21,210][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:21,214][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:21,218][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:21,221][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:21,224][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:21,227][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:21,230][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:21,233][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:21,243][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:21,246][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:21,273][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:21,276][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:21,959][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:22,334][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:22,336][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:22,481][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:22,485][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:22,488][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:22,518][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:22,521][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:22,523][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:22,526][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:22,553][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:22,556][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:22,558][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:22,561][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:22,578][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:22,581][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:22,584][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:22,605][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:22,608][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:22,611][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:22,630][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:22,633][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:22,635][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:22,638][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:22,641][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:22,643][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:22,657][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:22,660][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:22,681][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:22,684][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:22,686][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:22,689][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:22,710][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:22,713][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:22,715][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:22,719][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:22,722][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:22,724][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:22,734][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:22,737][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:22,739][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:22,742][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:22,744][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:22,747][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:22,761][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:22,765][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:22,786][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:22,789][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:22,791][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:22,793][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:22,796][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:22,798][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:22,801][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:22,810][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:22,812][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:22,815][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:22,831][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:22,834][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:22,836][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:22,839][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:22,841][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:22,843][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:22,855][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:22,858][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:22,860][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:22,863][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:22,865][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:22,867][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:22,869][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:22,872][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:22,874][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:22,876][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:22,878][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:22,886][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:22,889][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:22,891][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:22,893][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:22,896][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:22,899][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:22,901][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:22,904][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:22,910][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:22,912][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:22,914][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:22,916][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:22,945][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:22,948][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:22,950][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:22,952][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:22,955][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:22,965][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:22,968][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:22,970][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:22,972][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:22,988][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:22,991][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:22,993][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:22,996][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:23,008][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:23,011][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:23,014][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:23,026][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:23,028][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:23,031][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:23,045][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:23,047][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:23,050][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:23,053][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:23,055][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:23,062][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:23,065][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:23,068][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:23,084][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:23,087][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:23,090][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:23,092][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:23,107][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:23,110][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:23,112][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:23,114][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:23,121][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:23,123][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:23,126][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:23,128][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:23,143][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:23,146][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:23,148][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:23,163][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:23,166][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:23,168][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:23,171][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:23,173][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:23,176][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:23,178][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:23,186][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:23,189][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:23,191][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:23,194][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:23,205][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:23,207][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:23,210][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:23,213][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:23,223][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:23,226][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:23,228][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:23,230][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:23,233][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:23,235][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:23,237][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:23,247][mllm.models.large_language_model_local][WARNING] - Response Given the values, I should propose to take all the items that I value highly and let Alice have the items she values highly. Here's the proposal: Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:23,250][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:23,252][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:23,255][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:23,257][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:23,259][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:23,261][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:23,263][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:23,265][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:23,272][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:23,274][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:23,276][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:23,278][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:23,280][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:23,283][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:23,285][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:23,290][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:23,299][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:23,301][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:23,318][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:23,323][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:23,328][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:23,333][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:23,341][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:23,347][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:23,352][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:23,357][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:23,365][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:23,369][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:23,374][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:23,386][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:23,391][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:23,395][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:23,400][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:23,409][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:23,413][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:23,418][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:23,433][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:23,437][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:23,443][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:23,457][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:23,462][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:23,467][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:23,471][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:23,475][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:23,483][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:23,488][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:23,492][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:23,506][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:23,510][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:23,515][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:23,529][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:23,534][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:23,538][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:23,542][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:23,545][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:23,549][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:23,555][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:23,558][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:23,576][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:23,578][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:23,600][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:23,605][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:23,609][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:23,614][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:23,618][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:23,623][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:23,630][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:23,634][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:23,639][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:23,643][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:23,646][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:23,653][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:23,656][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:23,660][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:23,663][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:23,679][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:23,683][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:23,687][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:23,691][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:23,692][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:23,696][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:23,703][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:23,706][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:23,710][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:23,714][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:23,717][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:23,720][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:23,724][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:23,727][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:23,731][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:23,734][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:23,740][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:23,743][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:24,155][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and the symmetry in the situation where both you and Alice value balls highly, it's crucial to ensure that we both get a fair share of the balls. Here is a proposal that maximizes the points for both of us in this round: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:24,304][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:24,359][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:24,517][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it seems both you and Bob value hats and books quite highly, while balls are less valuable for both. Here's a strategic proposal to ensure that both you and Bob get the items you value the most while splitting the less valuable items proportionally: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:25,192][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and equal expected value scenarios, a strategic approach would be to propose an equal split that maximizes the value based on the per-item values. Here, since both you and Alice have the same per-item value for books and balls, and different values for hats, the proposal should consider the value distribution. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:25,196][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:25,338][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:25,341][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:25,374][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:25,458][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:25,461][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:25,545][mllm.models.large_language_model_local][WARNING] - Response Given the values, you have a strong preference for hats and books, and Alice values balls highly. To maximize points, you should try to secure the items you value the most while considering Alice's preferences for balls. Here’s a proposal: Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:25,548][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:25,551][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:25,554][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:25,556][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:25,559][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:25,561][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:25,598][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:25,601][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:25,604][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:25,623][mllm.models.large_language_model_local][WARNING] - Response Given the new values, where your per-item values are hats=1, books=10, balls=10 and Alice's are hats=10, books=10, balls=1, it is to your advantage to maximize the items with the highest value to you, which are books and balls. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:25,626][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:25,629][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:25,632][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:25,635][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:25,650][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:25,654][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:25,673][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:25,676][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:25,679][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:25,702][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:25,705][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:25,708][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:25,711][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:25,713][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:25,757][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:25,759][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:25,797][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and Bob's likely strategy, I should focus on maximizing the value of the items I value the most, which are balls. Here's the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:25,804][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:25,807][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:25,823][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:25,886][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both hats and books are highly valued by both Alice and myself, while balls are valued less by both of us. To maximize points, it would be strategic to allocate hats and books efficiently while giving some balls to Alice since she values them more. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:25,889][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:25,910][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:25,913][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:25,916][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:25,919][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:25,921][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:25,924][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:25,926][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:25,929][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:25,932][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:25,935][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:25,938][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:25,945][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:25,948][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:25,974][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:25,977][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:25,979][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:25,980][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:25,984][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:25,986][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:26,028][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's beneficial to maximize the items with higher personal value. Here, both you and Alice value hats and books highly, but you value balls more than Alice values them. A strategic proposal could be to take as many items with your higher value as possible while considering Alice's responses. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:26,049][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:26,052][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:26,090][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:26,093][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:26,096][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:26,098][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:26,101][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:26,170][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:26,177][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:26,180][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:26,183][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:26,185][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:26,188][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:26,190][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:26,250][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:26,253][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:26,255][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:26,330][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:26,333][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:26,335][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:26,338][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:26,341][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:26,411][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it is clear that both you and Alice value hats and books highly, and both value balls less. To maximize points, it would be beneficial to negotiate based on these values. However, since both of you value hats and books highly, the allocation of these items will likely be competitive. Here, I will propose an allocation that attempts to balance the high-valued items while considering the lower-valued balls. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:26,414][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and Bob's strategy, I should focus on ensuring I get a significant portion of the high-value items (balls) while also securing some of the mid-value items (books). A balanced approach would be to propose slightly less of the high-value items to avoid overcommitting and validate my claim on the mid-value items. Proposal: 10 hats, 10 books, 9 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:26,417][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:26,419][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:26,490][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:26,493][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:26,496][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:26,570][mllm.models.large_language_model_local][WARNING] - Response Given the values, it appears that both you and Bob have complementary preferences. You value books highly, while Bob values balls highly. To maximize your points, it would be strategic to recognize your complementary preferences and propose a split that takes this into account. Proposal: 10 hats, 10 books, 9 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:26,576][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:26,579][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:26,582][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:26,585][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:26,587][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:26,649][mllm.models.large_language_model_local][WARNING] - Response Given the high value of books for both of us and the low value of balls for both of us, a strategic proposal would be to allocate most of the books and distribute the remaining items proportionally. However, since we have equal values for hats, we should also split those fairly. Here's a proposal that maximizes the value of books while ensuring a fair split of hats and minimizing the risk of splitting balls: Proposal: 5 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:26,652][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:26,728][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:26,731][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:26,808][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:26,891][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:26,894][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:26,897][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:26,980][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that books are highly valued by both of us, and balls are also valuable, but hats have much lower value for both of us. Since we both value books and balls more, it might be beneficial to propose a split that maximizes the value of these items. A possible strategy is to propose taking all the books and balls since they are valuable for both of us, and split the hats proportionally. Proposal: 5 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:27,000][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:27,003][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:27,006][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:27,008][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:27,011][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:27,023][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both "books" and "balls" are more valuable to Bob than to you. You should focus on securing more "hats" since they have a higher value for you. A strategic approach would be to propose a split that maximizes your points by effectively leveraging your higher valuation of hats. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:27,026][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:27,028][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:27,031][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:27,034][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:27,037][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:27,040][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:27,046][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:27,049][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:27,057][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:27,060][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:27,062][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:27,065][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:27,067][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:27,074][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:27,080][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:27,114][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:27,622][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's beneficial to propose a split that maximizes the points based on the higher values for hats and books. Here's a strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:27,696][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:27,756][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:27,759][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:27,823][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:27,852][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:27,858][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:27,865][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:27,886][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:27,893][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:27,900][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:27,906][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:27,926][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:27,932][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:27,938][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:27,943][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:27,959][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:27,963][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:27,968][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:27,973][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:27,977][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:27,982][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:27,994][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:28,030][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:28,032][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:28,034][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:28,037][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:28,039][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:28,083][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:28,085][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:28,087][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:28,089][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:28,092][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:28,095][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:28,097][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:28,099][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:28,154][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the values and the previous round's results, it seems that proposing the split proportional to the values would be a rational strategy. Here, you value hats and books the same (1) and place a higher value on balls (10), while Bob places a higher value on books (10) and values hats and balls the same (1). A strategic proposal would be to reflect these values. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:28,161][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:28,164][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:28,167][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:28,170][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:28,226][mllm.models.large_language_model_local][WARNING] - Response Given the values, the best strategy would be to propose a distribution that maximizes the benefit from the high value items while not overallocating all items to avoid potential proportional loss if the total proposed exceeds 10 for any item. Here, books and balls have the highest values for both of us, while hats have a moderate value for me and a low value for Alice. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:28,233][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:28,277][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:28,281][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:28,284][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:28,323][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:28,326][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:28,366][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:28,369][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:28,371][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:28,375][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:28,378][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:28,380][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:28,383][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:28,386][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:28,422][mllm.models.large_language_model_local][WARNING] - Response Considering the high value you place on hats and balls, and the comparable but lower values Alice places on these items, a strategic proposal would be to maximize your points by keeping the items you value highly. Here is the proposal: Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:28,425][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:28,427][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:28,430][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:28,469][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:28,553][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:28,560][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:28,667][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:28,688][mllm.models.large_language_model_local][WARNING] - Response Given the updated per-item values, it appears that both you and Alice place significant value on different items. You value hats and balls highly (10), while Alice values books and balls more (10). Books have much less value to you (1) compared to Alice (10). To maximize your points, it makes sense to propose keeping all items that you value highly and splitting the books proportionally since Alice values them more. Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:28,695][mllm.models.large_language_model_local][WARNING] - Response Given the values, it is beneficial to propose an allocation that maximizes the points based on the per-item values. Since both you and Bob value hats and books highly, it's strategic to propose a split that captures the maximum value for both items while also considering the low value of balls. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:28,775][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:28,778][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:28,827][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:28,834][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:28,837][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:28,879][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:28,885][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:28,922][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:28,925][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:28,928][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:29,011][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both hats and books are highly valued by both you and Bob, while balls have low individual value for both. To maximize points, we should try to claim as many of the highly valued items as possible while ensuring the total does not exceed the available items. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:29,174][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it is clear that both you and Bob value hats and books more than balls. Since both of you have similar valuations for hats and books, the key will be to claim as much of these items as possible while still ensuring the total does not exceed the available quantity. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:29,348][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both you and Bob value hats and books significantly higher than balls. Since both of you have similar valuations for hats and books, and balls are valued lower, a cooperative strategy where we each take half of the balls and all the hats and books would likely maximize our points. Proposal: 10 hats, 10 books, 5 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:29,351][mllm.models.large_language_model_local][WARNING] - Response Given the values, both you and Bob place high value on hats and books, while balls are valued less by both of you. However, since the assignment is independent and both of you are equally likely to have the same expected per-item value, a strategic proposal would be to maximize the distribution of high-value items. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:29,632][mllm.models.large_language_model_local][WARNING] - Response Given the new per-item values, where you value hats and balls at 10 and books at 1, while Alice values books at 10 and balls at 10, it makes sense to propose a distribution that reflects these values to maximize your points. Since both of you value books more than the other does, and hats and balls are valued highly by you but not by Alice, it might be beneficial to propose a higher amount of hats and balls while allocating books more evenly or leaving fewer to Alice. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:29,712][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Alice place high value on different items, which could be an advantage in proposing a split that maximizes your points. Here's a strategic approach: - You value books and balls highly, while Alice values hats. - You want to ensure you get a good portion of books and balls, which have higher values for you. A balanced proposal might be to take most of the books and balls while allowing Alice to take most of the hats. Let's split it as follows: Proposal: 10 hats, 10 books, 10 balls This proposal ensures you get the majority of the items you value highly. Given the values, this should secure you a good amount of points. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:29,749][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:29,752][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:29,755][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:29,798][mllm.models.large_language_model_local][WARNING] - Response Given the values, it is clear that both you and Alice have a higher value for hats and balls compared to the other items. Since there are no other proposals or specific strategies mentioned, a symmetric approach might be optimal if we assume that Alice will likely propose a similar distribution. Given that we have the same values and the same items, proposing a balanced distribution could be the best strategy. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:29,801][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:29,803][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:29,806][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:29,884][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:29,886][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:29,889][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:29,892][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:29,895][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:29,971][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:29,973][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:29,976][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:30,057][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:30,060][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:30,066][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:30,146][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the high value Bob places on books and balls and your high value on hats and balls, it's beneficial to propose a split that maximizes the use of the higher-value items while ensuring the total proposal doesn't exceed the total items available. However, since both of us are likely to value the items similarly, a fair split that ensures we both get a proportional share may be the best strategy. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:30,227][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:30,230][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:30,233][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:30,236][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:30,239][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:30,313][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both hats and books are more valuable to you than to Bob, while balls are more valuable to Bob than to you. A strategic proposal would be to maximize your share of the high-value items (hats and books) while still considering the proportional allocation rule. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:30,317][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:30,320][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:30,323][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:30,326][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:30,328][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:30,331][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:30,334][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:30,401][mllm.models.large_language_model_local][WARNING] - Response Given the high value for both hats and balls from both you and Alice, a balanced approach is likely to yield the best results. However, since your values for hats and balls are much higher, you should try to secure as many of those items as possible while ensuring the total proposed quantity does not exceed the available items. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:30,404][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:30,407][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:30,410][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:30,413][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:30,416][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:30,418][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:30,421][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:30,450][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:30,453][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:30,456][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:30,531][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:30,534][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:30,537][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:30,614][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it makes sense to focus on items that have higher value for either you or Bob. You value hats (10) and books (1) highly, while Bob values balls (10) highly. A strategic split would be to take all of the items you value highly to maximize your points. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:30,617][mllm.models.large_language_model_local][WARNING] - Response Given the values, both you and Bob have high value for hats and books, and low value for balls. Since both of us place a high value on the same items, we might consider proposing to split these items equitably to maximize our respective values. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:30,624][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:30,627][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:30,630][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:30,633][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:30,636][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:30,639][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:30,641][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:30,644][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:30,694][mllm.models.large_language_model_local][WARNING] - Response Given the values, you have a strong preference for hats and books, while Alice values books and balls highly. Splitting the items equally might not be the best approach since it may not account for the differing values each of you places on the items. One strategic approach is to try to get a slight advantage in the items you value more. Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:30,701][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:30,704][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:30,775][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:30,778][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:30,781][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:30,854][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:30,856][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:30,859][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:31,014][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:31,017][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:31,093][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:31,097][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:31,099][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:31,658][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:31,741][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:31,744][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:31,747][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:31,750][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:31,752][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:31,755][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:31,758][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:31,760][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:31,767][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:31,770][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:31,772][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:31,775][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:31,777][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:31,780][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:31,783][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:31,785][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:32,019][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in per-item values and the objective to maximize points, a strategic split would be to propose an allocation that respects the higher values of hats and books while still considering the lower value of balls. A reasonable proposal could be to split the items more evenly based on their values. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:32,022][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both you and Bob value hats and books more than balls. Distributing the items in a way that maximizes the value based on these per-item values would be beneficial. Here's a proposal that tries to maximize the points: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:32,093][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:32,132][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the previous rounds where both you and Alice are proposing the same allocation, it suggests that both of you are maximizing your points based on the information available. However, let's consider a slightly different proposal to ensure we are exploring all possibilities: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:32,269][mllm.models.large_language_model_local][WARNING] - Response Given thevalues, it's clear that both you and Bob have a strong preference for hats and books, respectively, and a much lower value for balls. A cooperative strategy might be to each take what we value most and split the balls proportionally. Proposal: 10 hats, 10 books, 10 balls / 2 = 5 balls each. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:32,396][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:32,476][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's advantageous to consider the high value items (hats and books) while still accounting for the low value item (balls). Since both your and Bob's values for hats and books are high, there's a balance to be struck. Here's a proposal that maximizes the value of high-scoring items while ensuring the total does not exceed the available items: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:32,565][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:32,568][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:32,571][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:32,573][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:32,580][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:32,582][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:32,585][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:32,588][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:32,591][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:32,593][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:32,596][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:32,599][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:32,602][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:32,604][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:32,607][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:32,610][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:32,612][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:32,634][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:32,636][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:32,639][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:32,642][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:32,645][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:32,767][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:32,769][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:32,862][mllm.models.large_language_model_local][WARNING] - Response Given the high value of balls (10 for you and 1 for Bob), it's important to secure as many balls as possible to maximize your points. However, we also need to consider the risk of Bob overvaluing hats and books. A strategic approach would be to propose a split that balances the items to ensure we get a fair share of the high-value balls while also considering the value Bob might place on hats and books. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:32,864][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:32,867][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:32,924][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:32,927][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:32,930][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:32,932][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:32,980][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:33,050][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the outcomes from previous rounds, it seems that proposing an equal split often results in a fair allocation. However, in this round, I should consider the higher value of hats and books for me and the higher value of balls for Bob. To maximize my points, I should prioritize collecting more hats and books while still acknowledging Bob's preference for balls. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:33,054][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:33,057][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:33,060][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:33,066][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:33,069][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:33,087][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the proposals from previous rounds, it seems both Alice and Bob have recognized the optimal strategy of proposing to take all of each item, which leads to both earning the same points. However, to ensure that we maximize our points in a way that cannot be exploited or gives Bob an advantage, we can consider a mixed strategy where we propose to take all of each item as we have in previous rounds. This strategy ensures that we get the maximum possible points for our highest-valued items while not giving Bob an advantage. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:33,090][mllm.models.large_language_model_local][WARNING] - Response Given the high value of hats and books for both me and Bob, while balls have a very low value for both, it makes sense to propose a division that maximizes the high-value items. Here’s a strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:33,093][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:33,096][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:33,099][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:33,101][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:33,122][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:33,125][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:33,189][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:33,192][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:33,195][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:33,210][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:33,213][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:33,216][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:33,219][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:33,221][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:33,224][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:33,231][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:33,234][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:33,241][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:33,244][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:33,260][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:33,282][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both we and Bob value hats and books highly, but balls less so. To maximize our points, we should aim to get as many hats and books as possible, as they have the highest individual values for us. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:33,285][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:33,288][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:33,290][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:33,324][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:33,326][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:33,333][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:33,340][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:33,346][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:33,349][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:33,352][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:33,355][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:33,357][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:33,360][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:33,362][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:33,365][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:33,368][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:33,371][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:33,373][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:33,395][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:33,397][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:33,400][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:33,402][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:33,404][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:33,479][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:33,481][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:33,484][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:33,565][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:33,568][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:33,571][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:33,574][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:33,577][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:33,580][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:33,583][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:33,615][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:33,618][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:33,621][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:33,624][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:33,627][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:33,629][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:33,648][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:33,675][mllm.models.large_language_model_local][WARNING] - Response Given the alignment of values, it appears that both Alice and Bob have a strong preference for hats and books, and a lower preference for balls. Proposing to take the maximum number of items in each category seems like a good strategy to maximize the points. Here is the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:33,830][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:33,860][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:33,863][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:33,865][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:33,868][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:33,871][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:33,946][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:34,015][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:34,018][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:34,021][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:34,038][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:34,041][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:34,044][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:34,046][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:34,049][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:34,063][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:34,065][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:34,068][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:34,071][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:34,094][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:34,097][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:34,182][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and strategies observed, it seems optimal to propose an equal split of all items to ensure both Alice and Bob receive a fair share of items they value highly. Here, Alice values books and balls more than hats, while Bob values hats and books more than balls. Thus, a fair and strategic proposal would be to split all items equally. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:34,261][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:34,263][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:34,265][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:34,269][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:34,273][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:34,278][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:34,282][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:34,284][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:34,286][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:34,326][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:34,328][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:34,331][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:34,333][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:34,335][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:34,338][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:34,341][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:34,379][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:34,382][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:34,385][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:34,388][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:34,391][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:34,393][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:34,441][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:34,444][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:34,487][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:34,490][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:34,493][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:34,500][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:34,503][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:34,506][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:34,508][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:34,559][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:34,716][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:34,720][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:34,723][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:34,726][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:34,732][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:34,739][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:34,745][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:34,766][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:34,808][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:34,814][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:34,820][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:34,851][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:34,858][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:34,865][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:34,871][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:34,877][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:34,889][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:34,895][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:34,900][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:34,906][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:34,916][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:34,918][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:34,924][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:34,935][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:34,937][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:34,958][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:34,960][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:34,962][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:34,964][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:34,966][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:34,969][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:34,985][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:34,987][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:34,990][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:34,993][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:34,995][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:34,998][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:35,000][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:35,003][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:35,005][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:35,008][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:35,015][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:35,018][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:35,021][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:35,026][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:35,032][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:35,037][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:35,205][mllm.models.large_language_model_local][WARNING] - Response Given that both you and Bob value the hats and balls minimally, and the books significantly, we should aim to maximize the number of books allocated while fairly splitting the hats and balls. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:35,315][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Bob place high value on books and balls, but you value hats more than Bob does. To maximize your points, you should try to secure as many books and balls as possible while giving Bob some hats. Here's a proposal that balances your value preferences: Proposal: 5 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:35,408][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the fact that both you and Bob are proposing to take all items in every round, it suggests a potential equilibrium where neither agent has an incentive to deviate from this strategy, as they are equally valued for the items and any deviation would not increase their expected utility. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:35,507][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:35,549][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the previous outcomes, it seems that proposing to take all items results in a proportional splitting when Bob also proposes to take all items. To maximize our points, we should again propose to take all items. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:35,621][mllm.models.large_language_model_local][WARNING] - Response Given the values, you have a strong preference for hats and books, while Bob has a strong preference for books and balls. Since both of you are likely to propose the same amounts and the items are split proportionally, it might be beneficial to maximize the allocation of items you value more. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:35,664][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:35,738][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:35,745][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:35,748][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:35,775][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:35,778][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:35,780][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:35,783][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:35,786][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:35,788][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:35,822][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:35,829][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:35,832][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:35,835][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:35,837][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:35,840][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:35,864][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:35,867][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:35,869][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:35,872][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:35,875][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:35,881][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:35,903][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:35,905][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:35,908][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:35,910][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:35,913][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:35,915][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:35,930][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:35,933][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:35,935][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:35,938][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:35,940][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:35,942][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:35,944][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:35,946][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:35,948][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:35,987][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:36,024][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:36,028][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:36,048][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:36,052][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:36,055][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:36,058][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:36,060][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:36,143][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric nature of the item values and the previous rounds' outcomes, where proposing to take all of each item led to equal and optimal outcomes for both players, it seems reasonable to continue this strategy. Therefore, I will propose to take all of each item as before. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:36,150][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:36,153][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:36,156][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:36,159][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:36,161][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:36,171][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:36,194][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:36,201][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:36,204][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:36,206][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:36,220][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric and equal distribution of item values between us, the optimal strategy would be to propose an equal split to maximize the points for both items we value highly. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:36,224][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:36,241][mllm.models.large_language_model_local][WARNING] - Response Given the values, I should focus on maximizing the items with higher personal value. Since my per-item values for hats and books are higher (10) compared to Bob's, and vice versa for balls, a strategic proposal would be to propose keeping most of the items that I value more. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:36,244][mllm.models.large_language_model_local][WARNING] - Response Given the repeated pattern and the symmetric nature of the per-item values, it seems that splitting the items evenly is the best strategy in this round as well. Therefore, I will propose to keep all items. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:36,247][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:36,250][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:36,253][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:36,256][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:36,270][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:36,276][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:36,278][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:36,293][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:36,299][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:36,305][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:36,321][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:36,325][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:36,330][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:36,335][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:36,340][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:36,346][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:36,348][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:36,354][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:36,359][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:36,363][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:36,368][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:36,406][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:36,410][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:36,414][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:36,460][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:36,462][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:36,467][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:36,470][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:36,473][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:36,487][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:36,489][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:36,492][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:36,495][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:36,497][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:36,500][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:36,503][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:36,505][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:36,508][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:36,510][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:36,513][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:36,515][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:36,518][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:36,527][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:36,578][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:36,581][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:36,604][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:36,607][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:36,610][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:36,613][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:36,706][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:36,710][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:36,732][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:36,735][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:36,737][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:36,740][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:36,809][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:36,812][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:36,815][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:36,892][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:36,895][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:36,898][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:36,945][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:36,948][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:36,966][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:36,969][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:36,972][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:36,975][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:36,978][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:36,981][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:37,014][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:37,017][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:37,039][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric nature of the values and the result of the previous rounds, it seems both Alice and Bob are proposing to take all items each round. Given your per-item values for hats=1, books=10, balls=1 and the fact that you and Alice have similar but symmetrical strategies, it's strategic to mirror Alice's proposal to ensure a fair split and maximize your points. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:37,042][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, the optimal strategy is to propose a split that maximizes the points based on our and Bob's values. Since both of us value hats and books highly, and balls relatively less, we should attempt to keep as many hats and books as possible. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:37,047][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:37,049][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:37,098][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:37,100][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:37,104][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:37,107][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:37,149][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Bob value hats and books highly (10 points each), and balls less (1 point each for you, 10 points for Bob). Since both of you are likely to have similar values, proposing an even split might lead to a fair distribution where both you and Bob can maximize your points. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:37,153][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:37,155][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:37,157][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:37,161][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:37,165][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:37,167][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:37,169][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:37,173][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:37,177][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:37,231][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:37,236][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:37,240][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:37,245][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:37,250][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:37,254][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:37,316][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:37,322][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:37,382][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:37,389][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:37,396][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:37,402][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:37,569][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:37,570][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:37,572][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:37,574][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:37,577][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:37,579][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:37,581][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:37,583][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:37,585][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:37,593][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:37,597][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:37,600][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:37,680][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:37,685][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:37,762][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:37,765][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:37,767][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:37,768][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:37,769][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:37,787][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and the symmetry in the situation, it's beneficial to cooperate by proposing to take all items. This maximizes the points for both parties. Therefore: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:37,789][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:37,907][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:37,935][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:37,938][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:37,940][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:37,943][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:37,957][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:37,960][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:37,961][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:37,962][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:37,963][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:37,964][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:37,966][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:37,968][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:37,971][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:37,976][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:38,140][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:38,222][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both hats and books are more valuable to you than to Bob, while balls are valued the same. To maximize your points, you should propose to take all the hats and books, while giving Bob all the balls. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:38,426][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Bob place a higher value on hats and books compared to balls. To maximize your points, you should try to get as many hats and books as possible, while still proposing a reasonable number of balls to avoid a proportional split that could reduce your total points. Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:38,592][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and the symmetry in the game, proposing to take all items seems like a reasonable strategy to maximize points. However, to ensure a fair split and avoid the risk of Alice proposing a different strategy that could lead to a lower proportional share, we should consider proposing a slightly more conservative split while still aiming to maximize our points. Proposal: 5 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:38,685][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:38,717][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:38,730][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:38,733][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:38,735][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:38,737][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:38,739][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:38,741][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:38,743][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:38,776][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Bob value hats and books highly, and balls slightly less. Proposing to take all items ensures that we maximize the points based on our high values for hats and books. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:38,782][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:38,785][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:38,787][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:38,790][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:38,820][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:38,822][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:38,825][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:38,837][mllm.models.large_language_model_local][WARNING] - Response Given the high value of balls for both you and Alice, and the similar per-item values, it's advantageously strategic to maximize the distribution of balls to ensure both you and Alice receive a fair share, while also securing the high-value books and hats. Here is a proposal that balances the distribution: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:38,918][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the fact that both you and Bob have opposite preferences for hats and books, it might be beneficial to consider a strategy that accounts for these preferences. Since both of your per-item values for hats and books are low, while Bob's are high, you might want to ensure you get the balls to maximize your points. Here's a proposal: Proposal: 10 hats, 10 balls, 10 balls This proposal ensures you get all of the hats and two-thirds of the balls, which should maximize your total points given your low per-item values for hats and books. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:38,954][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:38,955][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:38,957][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:38,958][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:38,960][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:38,972][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:38,991][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:38,992][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:38,993][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:38,994][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:39,012][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:39,032][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:39,676][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:39,680][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:39,683][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:39,687][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:39,695][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:39,703][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:39,706][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:39,709][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:39,712][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:39,715][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:39,718][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:39,720][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:39,726][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:39,730][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:39,735][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:39,738][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:39,749][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:39,752][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:39,754][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:39,928][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and the symmetric nature of the situation, it makes sense to propose a fair split that maximizes the total points for both Alice and Bob. Since both Alice and Bob have the same values for hats and books, and complementary values for balls, proposing a split where we each take half of the hats and books and all of the balls would be a reasonable strategy. This would ensure that both parties receive a balanced and valuable allocation. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:40,008][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's beneficial to maximize the items with higher per-item values. Here, both you and Bob value books the highest at 10, followed by hats and balls at 10 and 1 respectively. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:40,091][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:40,122][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the strategic positioning of the values, proposing the same allocation as before seems reasonable. However, to ensure we maximize our points, let's consider the distribution of values. Since both sets of values are quite different, allocating more based on our higher values for hats and books, and less on balls is logical. Given Bob's values, we can expect him to allocate hats and books more towards himself. Therefore, proposing a balanced but slightly less aggressive split might benefit us in this round. Proposal: 8 hats, 8 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:40,124][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:40,126][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:40,127][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:40,130][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:40,132][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:40,141][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:40,160][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:40,162][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:40,164][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:40,167][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:40,169][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:40,198][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:40,252][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the values, it makes sense to propose an equal split that maximizes the value based on your and Bob's valuations. Here's the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:40,259][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:40,263][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:40,274][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:40,277][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:40,294][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:40,297][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:40,302][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:40,306][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:40,309][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:40,317][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:40,320][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:40,322][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:40,411][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:40,414][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:40,427][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Bob have high values for different items. To maximize points, you should try to propose a distribution that aligns with your value distribution while also considering Bob's preferences. Here's a strategic proposal: Proposal: 10 hats, 10 books, 10 balls This proposal keeps the distribution as close as possible to your values, ensuring you get the maximum possible points from the items you value highly. If Bob also values books and balls significantly, this might lead to a fair and effective distribution. If you want to be more strategic, you could split the items based on your values more precisely: Proposal: 10 hats, 10 books, 10 balls This keeps the distribution symmetrical and aims to maximize your points by keeping items you value highly. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:40,434][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the values and the initial round results, it seems that proposing to take all items is likely to result in a proportional split if Bob does the same. However, a more strategic approach might involve attempting to split the items in a way that maximizes the individual gains given the difference in per-item values. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:40,437][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:40,449][mllm.models.large_language_model_local][WARNING] - Response Given that both you and Bob value hats and books quite highly, while valuing balls less, the best strategy is to cooperate and propose equally splitting all items to maximize the total points. This ensures that both you and Bob get a fair share of the items they value, which is likely to result in the highest possible combined points. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:40,486][mllm.models.large_language_model_local][WARNING] - Response Given the values and understanding that both you and Alice tend to value hats and books highly, and balls less, a cooperative strategy might not yield much benefit as both of you are likely to claim the same items. To maximize your points, you should still propose a split that reflects your values as accurately as possible while considering Alice's preferences. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:40,504][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:40,507][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:40,513][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:40,519][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:40,525][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:40,531][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:40,543][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:40,548][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:40,553][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:40,558][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:40,563][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:40,582][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:40,640][mllm.models.large_language_model_local][WARNING] - Response Given the high value of hats and books for you and the high value of balls for Bob, proposing to take all items seems to be a strategic decision. However, we should consider the potential risk of Bob proposing to take all the balls, which would leave very little for us. To balance this, let's propose to take a significant portion of the hats and books, and leave a small portion for Bob to prevent any extreme proposals from him. Proposal: 8 hats, 8 books, 2 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:40,644][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:40,651][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:40,669][mllm.models.large_language_model_local][WARNING] - Response Given the high value you place on hats and books compared to Bob, and vice versa, a cooperative strategy where we each take items that maximize our respective values might be beneficial. Here’s a proposal that aligns with maximizing our individual values: Proposal: 10 hats, 10 books, 10 balls This proposal ensures that we each take the items we value the most. Given the symmetry and the high value items we each seek, this should maximize our individual earnings. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:40,676][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:40,681][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:40,687][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:40,692][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:40,694][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:40,714][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:40,717][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:40,735][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:40,785][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:40,788][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:40,891][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:40,903][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both you and Bob value books the most, and hats and balls are valued lower by both. Considering the symmetry and the goal to maximize your points, the optimal strategy is to propose an equitable split that respects your and Bob's valuations. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:40,906][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:40,999][mllm.models.large_language_model_local][WARNING] - Response Given the values, both you and Alice have a high value for balls (10), and a low value for hats (1). Books are valued moderately by both of you. To maximize gains, let's focus on splitting the balls since they are most valuable for both of you. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:41,017][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's important to ensure that we maximize our points by targeting the items with the highest values for us. Since I value hats and balls more than books, and Alice values books more, a strategic proposal would be to maximize our share of hats and balls while taking some books to ensure we don't lose too much potential points. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:41,067][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it is important to maximize the points based on the values I have and Alice's. Since I value hats and balls more than Alice does, and books less, it makes sense to propose the maximum for hats and balls, and a balanced amount for books. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:41,086][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:41,089][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:41,101][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and the symmetric nature of the problem, it seems optimal to evenly split the items to maximize the points for both Alice and Bob. Here’s the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:41,108][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:41,111][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:41,134][mllm.models.large_language_model_local][WARNING] - Response Given the values, it seems that both hats and balls are less valuable to you compared to Alice, while books are more valuable to you than to Alice. To optimize your points, you should propose to take the books and leave the hats and balls to Alice. Here is the proposal: Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:41,136][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:41,140][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:41,142][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:41,144][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:41,147][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:41,223][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:41,239][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:41,256][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:41,259][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:41,293][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the equal distribution of per-item values, the optimal strategy is to propose an equal split of all items to maximize the points considering both your and Bob's values. Therefore, the proposal should be: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:41,454][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:41,539][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:41,543][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:41,621][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:41,625][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:41,704][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:41,784][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:41,790][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:41,793][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:41,864][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:41,945][mllm.models.large_language_model_local][WARNING] - Response Given the values: - You value hats at 10, books at 1, and balls at 1. - Bob values hats at 1, books at 10, and balls at 1. Since your value for hats and books is significantly higher than Bob's, and your value for balls is the same, it makes sense to ensure you get as many hats and books as possible to maximize your points. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:42,104][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:42,107][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:42,182][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the equal per-item values but different preferences between you and Bob, a strategic proposal would be to allocate the items in a way that maximizes your total value. Since you value hats and books higher and Bob values books and balls higher, we can propose an allocation that splits the items in a way that is close to even but tilts slightly in your favor for hats and books. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:42,266][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:42,273][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:42,278][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:42,281][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:42,346][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:42,349][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:42,354][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:42,358][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:42,361][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:42,364][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:42,371][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:42,584][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:42,588][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:42,668][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the values, proposing an even split is still a reasonable strategy. However, since the values are slightly different, we might want to slightly adjust our proposal to account for Bob's higher value on books and balls. Proposal: 10 hats, 10 books, 10 balls This proposal ensures that we claim the entire quantity of hats, which we value at 1, and split the books and balls proportionally, given Bob's higher valuation. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:43,035][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:43,267][mllm.models.large_language_model_local][WARNING] - Response Given the values, it seems Bob values books highly while you value hats and books moderately and balls lightly. To maximize your points, you should propose to take all the hats and books, or at least a significant portion of them, while letting Bob have the balls. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:43,303][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:43,365][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Bob have a strong valuation for books and balls, while hats have a much lower value. To maximize your points, you should propose to take all the books and balls since they are highly valuable to you. Proposal: 10 hats, 10 books, 10 balls However, given Bob's high valuation for balls as well, this may not be optimal. A better strategy might be to split the books and balls to ensure a fair distribution that maximizes both your and Bob's points. Proposal: 10 hats, 10 books, 10 balls / 2 = 10 hats, 5 books, 5 balls, 5 balls Since you can only propose whole items, let's propose to take all the books and half the balls, leaving the rest to Bob: Proposal: 10 hats, 10 books, 5 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:43,369][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:43,371][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:43,373][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:43,375][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:43,404][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:43,405][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:43,485][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:43,569][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:43,661][mllm.models.large_language_model_local][WARNING] - Response Given that both you and Bob have high values for hats and books, and low values for balls, a balanced proposal might be the best strategy to ensure neither of you overextends in the items with lower individual value. Here's a proposal that splits the items more evenly: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:43,675][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:43,679][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:43,691][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:43,694][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:43,697][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:43,702][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:43,761][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:43,776][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:43,779][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:43,793][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:43,796][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:43,799][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:43,825][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and the history of the game, it seems both you and Alice have a similar strategy of proposing to take all items, which leads to an equal split of the total value. However, to maximize your points, you should aim to take more items that you value more. Given your per-item values are: - Hats: 1 - Books: 10 - Balls: 1 And Alice's per-item values are: - Hats: 10 - Books: 1 - Balls: 1 You should propose to take most of the books and some hats to maximize your points. Here's a strategic proposal: Proposal: 5 hats, 10 books, 5 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:43,828][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:43,869][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it seems strategic to focus on items where both you and Bob have high values, as this can lead to higher points. In this round, both you and Bob have high values for hats (10 for you, 1 for Bob), and low values for books (1 for you, 10 for Bob). Therefore, it might be more effective to propose splitting the books and balls more evenly while keeping all the hats for yourself. Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:43,877][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:43,879][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:43,883][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:43,885][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:43,889][mllm.models.large_language_model_local][WARNING] - Response Given Bob's value for hats and balls are the same as my values for books and balls, it suggests a strategic approach where we both might benefit from a coordinated split. However, since Bob values hats and balls more than books, and I value hats and balls less, we need to find a compromise. A fair and competitive strategy would be to split items evenly while respecting the relative values. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:43,893][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both you and Alice value books and balls more than hats, but in slightly different ways. To maximize my points, I should try to get as many books and balls as possible while still proposing a fair split for hats to avoid idle competition. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:43,896][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:43,962][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:43,963][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:43,976][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:43,978][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:43,981][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:43,991][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:44,044][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:44,048][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:44,058][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:44,061][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:44,065][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:44,114][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:44,127][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:44,131][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:44,147][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:44,154][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:44,161][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:44,167][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:44,199][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the high value of balls for both of us, it might be beneficial to focus on ensuring we both get a good share of the balls while also taking into account hats and books. Since both of us value balls the most, we might benefit from making a proposal that ensures we both get some balls, while also considering the distribution of hats and books. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:44,248][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:44,255][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:44,264][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:44,271][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:44,277][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:44,286][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:44,301][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, the best strategy would be to allocate items in a way that maximizes the points based on your value for each item. Since you value hats (10), books (1), and balls (10) highly, and Alice values them differently, it makes sense to propose an allocation that aligns with these values to ensure you receive a high return. Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:44,348][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:44,355][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:44,365][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:44,371][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:44,377][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:44,428][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:44,444][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the high value of balls and hats for both players, it makes sense to propose an allocation that maximizes the value for both items with the highest individual values. Here is the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:44,447][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:44,449][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:44,461][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:44,464][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:44,495][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the previous round results, I will propose a distribution that takes advantage of my high value for hats and books while also considering Bob's high value for balls. Here is my proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:44,498][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:44,501][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:44,575][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:44,635][mllm.models.large_language_model_local][WARNING] - Response Given that both agents have similar valuations for hats and balls, but different valuations for books, a strategic proposal would be to allocate the books in a way that maximizes the points for both parties while maintaining a balanced distribution of the other items. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:44,670][mllm.models.large_language_model_local][WARNING] - Response Given that both you and Bob have similar valuations for hats and books, and your goal is to maximize your points, it's important to negotiate and ensure that you receive a fair share of the high-value items (hats and books). Here, proposing to take all of the high-value items while splitting the low-value items can be a strategic approach. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:44,672][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:44,684][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:44,687][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:44,698][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:44,701][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:44,712][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:44,713][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:44,728][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:44,730][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:44,732][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:44,741][mllm.models.large_language_model_local][WARNING] - Response Given the new per-item values where both you and Bob value hats and books similarly but balls are valued much higher by you, a strategic approach would be to prevent Bob from taking all the balls. Here's the proposal: Proposal: 10 hats, 10 books, 9 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:44,757][mllm.models.large_language_model_local][WARNING] - Response Given the values, it seems like both you and Bob have a strong preference for balls. However, to avoid proposing the same distribution that resulted in a proportional split last time, we can try a different approach. Let's propose a distribution that balances the items more evenly while still giving significant value to both of our preferred items. Proposal: 10 hats, 10 books, 5 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:44,758][mllm.models.large_language_model_local][WARNING] - Response Given the new values, it's clear that both you and Bob have a high value for books and balls, but you value hats more than Bob does. To maximize your points, it's strategic to propose keeping as many of the items with higher individual value for you. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:44,912][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:44,924][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the high value of hats and books for both players, proposing the full allocation of items seems reasonable to maximize the points. Here is the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:44,936][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:44,938][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:44,949][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:44,949][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:44,964][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:44,966][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:44,977][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:44,991][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:45,085][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:45,089][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:45,219][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in per-item values and the fact that we both have high values for balls and low values for hats, it's beneficial to propose equally splitting all items to ensure we maximize our points based on our individual values. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:45,269][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:45,332][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:45,337][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:45,376][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and equal per-item values for the items between us, proposing an even split is likely to maximize our points. Therefore: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:45,380][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:45,621][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:45,649][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:45,653][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:45,683][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:45,749][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:45,776][mllm.models.large_language_model_local][WARNING] - Response Given the high value of balls (10) for both of us, it's beneficial to secure as many balls as possible while also splitting the hats and books fairly. Here's a strategic proposal: Proposal: 5 hats, 5 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:45,784][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:45,790][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:45,794][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:45,879][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and equal valuation for the items by both Alice and Bob, the optimal strategy is to propose an even split to ensure both parties receive a fair share and maximize the points based on their individual valuations. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:45,981][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:45,982][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:46,074][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:46,078][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:46,148][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:46,162][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:46,557][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:46,560][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:46,569][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:46,571][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:46,581][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:46,583][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:46,614][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Alice place high value on different items. To maximize your points, you should try to get as many of the items you value highly (books) while also considering the value of hats and balls. Proposal: 10 hats, 10 books, 10 balls This proposal assumes that the distribution is fair and no items will be left out. However, to be more strategic, you might want to adjust based on the possibility of Alice splitting her lower-value hats among the items. Proposal: 10 hats, 10 books, 9 balls This way, you get a slightly higher number of books (which you value highly) and ensure that you do not miss out on any items altogether. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:46,646][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:46,657][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:46,750][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:46,754][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:46,763][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:46,767][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:46,776][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:46,779][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:46,846][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:46,860][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:46,954][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:46,961][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:46,970][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:46,976][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:46,984][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:46,991][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:47,054][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:47,066][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:47,381][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:47,392][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:47,489][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the fact that both you and Alice value hats at 1, books at 10, and balls at 1, it's clear that splitting the items evenly will maximize both of your points. Therefore, the optimal proposal is to suggest equal splits for all items. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:47,558][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:47,570][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:47,741][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:47,752][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:47,866][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:47,868][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:48,046][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:48,050][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:48,220][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:48,230][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:48,603][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:48,647][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 06:59:49,246][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:49,326][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 06:59:50,161][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:50,192][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 06:59:51,338][__main__][INFO] - Number of regex retries in iteration 568: 2422 [2025-11-13 06:59:51,339][__main__][INFO] - agents played in iteration 568 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 06:59:52,362][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:59:52,383][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:59:52,403][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:59:52,423][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 06:59:52,424][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 06:59:52,425][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 06:59:53,245][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 06:59:53,710][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 06:59:54,212][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 06:59:54,708][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 06:59:55,210][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 06:59:55,707][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 06:59:56,202][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 06:59:56,700][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 06:59:57,198][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 06:59:57,698][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 06:59:58,194][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 06:59:58,690][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 06:59:59,191][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 06:59:59,689][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 07:00:00,188][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 07:00:00,687][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 07:00:01,183][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 07:00:01,682][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 07:00:02,178][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 07:00:02,672][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 07:00:03,170][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 07:00:03,669][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 07:00:04,170][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 07:00:04,668][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 07:00:05,166][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 07:00:05,664][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 07:00:06,162][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 07:00:06,661][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 07:00:07,156][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 07:00:07,655][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 07:00:08,156][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 07:00:08,654][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 07:00:09,155][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 07:00:09,657][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 07:00:10,155][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 07:00:10,657][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 07:00:11,150][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 07:00:11,645][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 07:00:12,147][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 07:00:12,641][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 07:00:13,136][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 07:00:13,634][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 07:00:14,132][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 07:00:14,629][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 07:00:15,125][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 07:00:15,625][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 07:00:16,133][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 07:00:16,630][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 07:00:17,129][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 07:00:17,634][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 07:00:18,131][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 07:00:18,635][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 07:00:19,132][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 07:00:19,629][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 07:00:20,139][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 07:00:20,634][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 07:00:21,131][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 07:00:21,649][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 07:00:22,148][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 07:00:22,653][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 07:00:23,150][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 07:00:23,649][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 07:00:24,150][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 07:00:24,650][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 07:00:25,151][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10881 tokens. [2025-11-13 07:00:25,986][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.29%, Current % of VRAM taken: 59.74%, Block Peak % of device VRAM: 62.40%, ΔTime: 00:00:32 [2025-11-13 07:00:26,633][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 07:00:26,634][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 07:00:26,636][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 07:00:27,520][__main__][INFO] - Iteration 569 took 1m 33s (61.10% Gen, 37.95% Train). Generation: 56s, Training: 35s. Estimated remaining time: 68h 39m 29s. Estimated total time: 77h 30m 15s. Time estimates for 10 more iterations: 15m 30s, 100 more iterations: 2h 35m 0s, 500 more iterations: 12h 55m 2s. [2025-11-13 07:00:27,522][__main__][INFO] - Starting iteration 569. [2025-11-13 07:00:28,035][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 56 and human policies 1. [2025-11-13 07:00:28,035][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 07:00:51,463][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,489][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,492][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,494][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,497][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,500][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,504][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,507][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,510][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,513][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,515][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,517][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,520][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,522][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,523][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,525][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,527][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,530][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,533][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,535][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,538][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,540][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,553][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,555][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,557][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,558][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,560][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,561][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,562][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,564][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,564][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,566][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,566][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,568][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,571][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,573][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,575][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,577][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,578][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,579][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,581][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,583][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,585][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,587][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,588][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,590][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,592][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,594][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,596][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,598][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,599][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,600][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,605][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,606][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,607][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,609][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,610][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,611][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,613][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,614][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,616][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,617][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,618][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,619][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,620][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,621][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,621][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,622][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,623][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,624][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,625][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,627][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,627][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,628][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,629][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,630][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,630][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,631][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,631][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,632][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,632][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,632][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,633][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,633][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,636][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,637][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,638][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,640][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,641][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,642][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,643][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,644][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,645][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,646][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,646][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,648][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,649][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,649][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,650][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,651][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:51,793][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,813][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,816][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,818][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,821][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,824][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,831][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,834][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,836][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,839][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,841][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,843][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,846][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,848][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,850][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,852][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,853][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,858][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,861][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,864][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,866][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,868][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,874][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,876][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,878][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,880][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,882][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,884][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,885][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,887][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,889][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,892][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,894][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,896][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,899][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,906][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,908][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,908][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,909][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,910][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,911][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,912][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,912][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,913][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,914][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,915][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,917][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,918][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,920][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,922][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,923][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,925][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,928][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,931][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,933][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,934][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,936][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,938][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,940][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,941][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,943][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,944][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,945][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,948][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,950][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,951][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,952][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,954][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,955][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,955][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,956][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,957][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,958][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,960][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,961][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,962][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,963][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,965][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,966][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,966][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,967][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,967][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,968][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,969][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,970][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,975][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,976][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,977][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,978][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,979][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,981][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,982][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,983][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,984][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,985][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,986][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,987][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,988][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,988][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:51,990][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:52,100][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,141][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,147][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,153][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,159][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,165][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,171][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,176][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,190][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,195][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,199][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,203][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,207][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,211][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,215][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,219][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,223][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,227][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,231][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,236][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,239][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,242][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,246][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,250][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,253][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,255][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,258][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,260][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,268][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,271][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,274][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,277][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,279][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,281][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,284][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,287][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,288][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,291][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,293][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,295][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,298][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,301][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,304][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,306][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,309][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,312][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,314][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,317][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,319][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,321][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,323][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,326][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,328][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,330][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,333][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,335][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,337][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,339][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,341][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,343][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,346][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,348][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,350][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,352][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,355][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,357][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,358][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,360][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,363][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,365][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,367][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,370][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,372][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,374][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,381][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,383][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,385][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,386][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,388][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,390][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,392][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,394][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,396][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,398][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,400][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,402][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,404][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,406][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,408][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,411][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,413][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,415][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,418][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,420][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,423][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,425][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,427][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,429][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:52,432][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:00:53,428][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,450][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,453][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,456][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,460][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,474][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,477][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,479][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,482][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,496][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,499][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,502][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,504][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,521][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,524][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,526][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,529][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,543][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,545][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,547][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,570][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,574][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,578][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,581][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,584][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,586][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,589][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,592][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,594][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,598][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,600][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,602][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,604][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,606][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,608][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,610][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,617][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,618][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,620][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,622][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,624][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,626][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,629][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,631][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,634][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,636][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,642][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,644][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,646][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,648][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,650][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,652][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,654][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,655][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,657][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,667][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,670][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,671][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,673][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,673][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,675][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,678][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,679][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,681][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,683][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,690][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,692][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,694][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,696][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,698][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,700][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,701][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,703][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,704][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,705][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,715][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,717][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,718][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,720][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,722][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,724][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,726][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,727][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,729][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,731][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,733][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,735][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,736][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,737][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,738][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,741][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,743][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,744][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,745][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,747][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,748][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:53,905][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:53,924][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:53,927][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:53,930][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:53,934][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:53,944][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:53,947][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:53,950][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:53,953][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:53,962][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:53,965][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:53,968][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:53,970][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:53,978][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:53,981][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:53,984][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:53,986][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:53,994][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:53,996][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:53,998][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:54,039][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:54,041][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:54,043][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:54,046][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:54,057][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:54,060][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:54,063][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:54,065][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:54,068][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:54,071][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:54,073][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:54,075][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:54,076][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:54,077][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:54,078][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:54,080][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:54,085][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:54,087][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:54,089][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:54,091][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:54,093][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:54,095][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:54,097][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:54,099][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:54,120][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:54,122][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:54,124][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:54,126][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:54,129][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:54,131][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:54,134][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:54,136][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:54,138][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:54,140][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:54,141][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:54,182][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:54,185][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:54,187][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:54,189][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:54,191][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:54,194][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:54,196][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:54,199][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:54,202][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:54,225][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:54,227][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:54,229][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:54,231][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:54,233][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:54,234][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:54,237][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:54,238][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:54,266][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:54,269][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:54,271][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:55,142][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:55,144][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:55,145][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:55,148][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:55,215][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:55,218][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:55,222][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:55,225][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:55,228][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:55,232][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:55,235][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:55,238][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:55,240][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:55,242][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:55,245][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:55,248][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:55,250][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:55,276][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:55,279][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:55,282][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:55,284][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:55,478][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:55,504][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:55,507][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:55,537][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:55,540][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:55,542][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:55,576][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:55,579][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:55,602][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:55,605][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:55,608][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:55,626][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:55,629][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:55,632][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:55,636][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:55,638][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:55,641][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:55,649][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:55,652][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:55,655][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:55,710][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:55,712][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:55,715][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:55,719][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:55,735][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:55,738][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:55,741][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:55,743][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:55,746][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:55,748][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:55,751][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:55,796][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:55,799][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:55,801][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:55,802][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:55,803][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:55,805][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:55,808][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:55,810][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:55,811][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:55,812][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:55,814][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:55,816][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:55,818][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:55,821][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:55,823][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:55,824][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:55,866][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:55,869][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:55,871][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:55,874][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:55,877][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:55,879][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:55,882][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:55,885][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:55,888][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:55,890][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:55,893][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:55,895][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:55,948][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:55,950][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:55,953][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:55,956][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:55,960][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:55,962][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:55,966][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:55,968][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:55,970][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:55,973][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:56,011][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:56,015][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:56,017][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:56,020][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:00:56,023][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:56,025][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:56,028][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:57,004][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:57,006][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:57,009][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:57,078][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:57,082][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:57,086][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:57,089][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:57,092][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:57,095][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:57,135][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:57,138][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:57,141][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:57,144][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:57,146][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:57,149][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:57,153][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:57,267][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:57,295][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:57,297][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:57,331][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:57,335][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:57,370][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:57,373][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:57,400][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:57,428][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:57,431][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:57,456][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:57,460][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:57,463][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:57,466][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:57,469][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:57,472][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:57,503][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:57,506][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:57,508][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:57,510][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:57,550][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:57,553][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:57,556][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:57,559][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:57,562][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:57,565][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:57,593][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:57,597][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:57,600][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:57,602][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:57,605][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:57,608][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:57,612][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:57,614][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:57,617][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:57,620][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:57,624][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:57,626][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:57,629][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:57,632][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:57,634][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:57,637][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:57,652][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:57,654][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:57,658][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:57,661][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:57,663][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:57,666][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:57,668][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:57,671][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:57,673][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:57,675][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:57,676][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:57,678][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:57,708][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:57,710][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:57,712][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:57,714][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:57,716][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:57,718][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:57,764][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:57,767][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:57,771][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:57,774][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:57,777][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:57,780][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:57,782][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:57,785][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:57,787][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:57,788][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:57,789][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:57,790][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:58,797][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:58,800][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:58,803][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:58,893][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:58,896][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:58,899][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:58,902][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:58,905][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:58,944][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:58,947][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:58,950][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:58,952][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:58,955][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:58,958][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:58,962][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:59,072][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the values and the fact that both you and Bob value hats and books highly, while valuing balls less, a strategic approach would be to maximize your share of the items you value more while not overproposing. Let's propose an allocation that balances the items fairly, ensuring we get a significant portion of the items we value highly: Proposal: 10 hats, 10 books, 5 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:59,075][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:59,100][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:59,103][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:59,141][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:59,145][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:59,173][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:59,175][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:59,220][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:59,250][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:59,254][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 balls, 10 books, 10 hats did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:59,257][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:59,260][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:59,293][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 balls, 10 books, 10 hats did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:59,297][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:59,300][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:59,340][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:59,343][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:59,347][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:59,350][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:59,353][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:59,357][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:59,386][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:59,389][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:59,392][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:59,395][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:59,398][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:59,401][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:59,404][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:59,407][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:59,409][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:59,412][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:59,415][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:59,418][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:59,420][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:59,423][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:59,425][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:59,446][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:59,449][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:59,452][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:59,455][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:59,457][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:59,460][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:59,463][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:59,465][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:59,466][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:59,467][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:59,468][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:59,470][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:59,512][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:59,515][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:59,518][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:59,566][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the fact that both you and Bob value hats and books highly, while valuing balls less, it might be beneficial to slightly outbid Bob for the items you both value highly to secure them completely. Here's a strategy where you slightly propose one more item than Bob for the highly valued items, while ensuring the total still adds up to 10 for each type of item. Proposal: 11 hats, 11 books, 9 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:59,569][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:59,572][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:59,575][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:59,578][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:59,581][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:59,584][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:59,586][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:59,588][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:59,590][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:59,591][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:59,591][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:59,593][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:59,596][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:59,598][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:00:59,922][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and equal values for hats and books from both Alice and Bob, and the significantly lower value for balls, a strategic proposal would involve valuing items based on their respective per-item values. Here's a refined proposal that might offer a slight advantage: Proposal: 10 hats, 10 books, 5 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:00,068][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric nature of the situation, where both you and Bob have valuable preferences for hats and books, and less interest in balls, the optimal strategy would be to mirror Bob's proposal to avoid any proportional split that could reduce our total earnings. Since both you and Bob are likely to propose the same amounts, this could lead to a deadlock if both propose the full quantities, making a slight adjustment to ensure the total is within the limit of the items available. Proposal: 10 hats, 10 books, 9 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:00,087][mllm.models.large_language_model_local][WARNING] - Response Given the asymmetric values and the goal to maximize points, a strategic approach might be to take into account the disparity in values. Since both you and Bob value books and balls differently, it makes sense to propose a distribution that reflects these differences while still maximizing your score. Proposal: 10 hats, 10 books, 1 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:00,174][mllm.models.large_language_model_local][WARNING] - Response Proposals in previous rounds indicate a pattern where both Alice and Bob value books highly and hats and balls less. Given your values for hats (10), books (1), and balls (10), as well as Bob's values (hats=1, books=10, balls=10), it makes sense to maximize the allocation of items based on their perceived value. To maximize my points, I should prioritize books and balls since they have higher values for both of us, and hats have a relatively lower value for both, but it still has some worth. Here's the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:00,633][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:00,635][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:00,637][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:00,732][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:00,735][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:00,739][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:00,742][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:00,744][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:00,792][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:00,795][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:00,798][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:00,801][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:00,803][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:00,806][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:00,913][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:00,916][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:00,941][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:00,944][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:00,974][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:00,977][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:00,979][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:01,022][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:01,024][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:01,069][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:01,096][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:01,098][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:01,099][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:01,123][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:01,126][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:01,127][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:01,147][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:01,148][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:01,149][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:01,150][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:01,178][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:01,180][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:01,182][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:01,184][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:01,186][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:01,187][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:01,189][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:01,190][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:01,191][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:01,192][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:01,194][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:01,195][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:01,196][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:01,205][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:01,206][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:01,206][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:01,207][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:01,208][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:01,209][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:01,211][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:01,212][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:01,213][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:01,214][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:01,215][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:01,216][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:01,227][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:01,228][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:01,229][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:01,230][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:01,232][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:01,313][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the equal splitting rule, a rational strategy is to propose a fair share to avoid any potential proportional allocation penalties. Therefore, I will propose an equal split of the items. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:01,316][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:01,317][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:01,318][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:01,320][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:01,322][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:01,323][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:01,325][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:01,327][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:01,329][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:01,330][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:01,331][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:01,333][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:01,335][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:01,398][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:01,401][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:01,878][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:01,882][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:01,963][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the equal opportunity for allocation, a cooperative strategy might yield the best results. If both Alice and Bob both propose the same allocation, the items will be split equally, maximizing the points for both players. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:01,965][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:01,969][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:01,971][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:02,042][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:02,045][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:02,139][mllm.models.large_language_model_local][WARNING] - Response Given the values, both you and Bob have a high value for hats and balls, and a lower but equal value for books. A simple and effective strategy is to propose to take all items, as this aligns with Bob's interests as well. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:02,160][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:02,167][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:02,171][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:02,217][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:02,220][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:02,223][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:02,301][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:02,304][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:02,307][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:02,310][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:02,385][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:02,387][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:02,389][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:02,391][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:02,392][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:02,394][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:02,396][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:02,399][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:02,400][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:02,401][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:02,403][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:02,406][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:02,488][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:02,490][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:03,066][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:03,068][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:03,071][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:03,074][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:03,076][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:03,078][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:03,128][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:03,132][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:03,136][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:03,139][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:03,141][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:03,144][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:03,146][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:03,147][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:03,150][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:03,153][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:03,155][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:03,301][mllm.models.large_language_model_local][WARNING] - Response Given Alice's valuation of hats=1, books=1, and balls=10, proposing to take all the balls might be a strategic move, as it aligns with her higher valuation for balls. However, we should also consider sharing other items to ensure a balanced approach and avoid Alice proposing everything to herself in future rounds. Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:03,520][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values where I value hats at 10, books at 1, and balls at 1, and Alice values hats at 1, books at 10, and balls at 10, it's clear that we both have high values for different items. To maximize my points, I should aim to get as many balls as possible, followed by hats, and then books if necessary. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:03,579][mllm.models.large_language_model_local][WARNING] - Response Given the values, I should aim to maximize the points where my value for books is the highest at 10, followed by hats at 1, and balls at 1. Since Bob values hats and balls significantly more than books, and I value books the most, it makes sense to propose a distribution that maximizes my books while ensuring I also get some hats and balls. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:03,666][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:03,667][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:03,668][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:03,672][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:03,675][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:03,677][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:03,680][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:03,682][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:03,684][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:03,686][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:03,688][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:03,755][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and Alice's per-item values, the best strategy would be to exploit the mismatch in valuation to maximize points. Here's the reasoning: - **Hats**: Both of us and Alice value hats at 1. Since Alice values hats higher, she will want to take more hats if she can. We should take as few hats as possible to minimize the division. - **Books**: I value books at 10, while Alice values them at 1. This is a clear advantage for me. I should take as many books as I can. - **Balls**: Both of us and Alice value balls at 1. Since Alice values balls higher, she will want to take more balls if she can. We should take as few balls as possible to minimize the division. Given these valuations, I should propose the following: - Take all 10 books since my valuation of books is 10 and Alice's is 1. - Take as few hats and balls as possible to avoid sharing them disproportionately. A reasonable proposal would be: **Proposal: 0 hats, 10 books, 0 balls** This strategy aims to take full advantage of the high valuation of books and minimize the allocation of low-valued items. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:03,758][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:03,759][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:03,760][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:03,762][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:03,788][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the high value of balls for both of us, it's beneficial to coordinate and split the items evenly while maximizing the high-value items. Here's a strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:03,790][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:03,791][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:03,792][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:03,793][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:03,839][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Alice place the highest value on balls. However, since the items will be allocated proportionally if the sum of proposed items exceeds the available items, we need to ensure that the total number of items proposed does not exceed 30. A strategic proposal would be to maximize the value based on the perceived worth, but also to consider Alice's values. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:03,840][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:03,841][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:03,842][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:03,843][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:03,931][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:03,934][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:04,010][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:04,013][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:04,193][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:04,196][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:04,199][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:04,285][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:04,358][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:04,361][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:04,364][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:04,388][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:04,449][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:04,452][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:04,454][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:04,477][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:04,524][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:04,563][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:04,567][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:04,570][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:04,573][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:04,595][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:04,598][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:04,601][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:04,604][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:04,608][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:04,611][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:04,614][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:04,640][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:04,644][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:04,647][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:04,650][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:04,653][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:04,656][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:04,659][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:04,662][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:04,665][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:04,668][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:04,671][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:05,383][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the equal distribution of values, it makes sense to propose an equal split to ensure fairness and maximize the points. Therefore, I will propose the following: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:05,520][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:05,523][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:05,527][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:05,530][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:05,534][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:05,537][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:05,576][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:05,580][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:05,583][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:05,586][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:05,590][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:05,593][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:05,596][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:05,599][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:05,602][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:05,604][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:05,639][mllm.models.large_language_model_local][WARNING] - Response Given the values, both you and Bob place high value on the same items (books and hats) and low value on the other (balls). To maximize your points, you should try to get as many of the high-value items (hats and books) as possible while minimizing the allocation of low-value items (balls). Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:06,022][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:06,025][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:06,028][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:06,031][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:06,033][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:06,036][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:06,039][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:06,075][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the situation and the per-item values, it makes sense to propose an equitable split to maximize the points. If we both propose the same split, we can ensure we both get the maximum possible points based on our values. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:06,077][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:06,083][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:06,088][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:06,094][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:06,159][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:06,161][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:06,164][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:06,168][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:06,170][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:06,174][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:06,177][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:06,244][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:06,245][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:06,246][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:06,247][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:06,248][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:06,249][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:06,332][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:06,335][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:06,338][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:06,341][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:06,344][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:06,346][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:06,349][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:06,413][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:06,416][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:06,496][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's beneficial to propose keeping all items since both you and Alice value hats at 1, books at 10, and balls at 10. Here's the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:06,500][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:06,746][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:06,749][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:06,997][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:07,084][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:07,087][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:07,090][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:07,168][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:07,172][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:07,176][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:07,249][mllm.models.large_language_model_local][WARNING] - Response Given the values, you should propose an allocation that takes advantage of your high value for books and Bob's high value for balls. Here is a strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:07,253][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:07,333][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in our per-item values and Bob's, proposing to take the entire allocation each round seems to be an effective strategy. However, to diversify and ensure a balanced approach, we can propose a slightly different split. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:07,337][mllm.models.large_language_model_local][WARNING] - Response Given Alice's per-item values, she values hats and books much higher than balls. To maximize your points, you should propose a distribution that takes advantage of Alice's lower valuation of balls. Here's a strategic proposal: Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:07,341][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:07,410][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:07,412][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:07,414][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:07,418][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:07,690][mllm.models.large_language_model_local][WARNING] - Response Given the current values, it seems optimal to try to capture as much value as possible from the books and balls, since they have higher individual values for both of us. Here’s a strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:07,693][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the fact that both you and Bob value the same items similarly, it makes sense to propose a fair split to maximize the points for both parties. Here's the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:08,099][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the values and the fact that both strategies we have used so far have resulted in equal points, we can continue to propose the fair split strategy. This means each of us should propose to take all items to maximize our own value given our per-item preferences. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:08,180][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the fact that both Alice and I have identical per-item values for the items, proposing an equal distribution seems the most rational approach to ensure we both maximize our points. Thus, my proposal would be: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:08,344][mllm.models.large_language_model_local][WARNING] - Response Given the values and the symmetry in both our and Bob's preferences for hats and books, and the significantly lower value for balls, a strategic proposal would be to ensure we maximize our value while considering Bob's likely strategy. Here’s a refined proposal: Proposal: 10 hats, 10 books, 10 balls However, let's consider a more nuanced approach to ensure we do not leave too much value on the table: Proposal: 10 hats, 10 books, 9 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:08,591][mllm.models.large_language_model_local][WARNING] - Response Given the new values, my per-item values are significantly higher for hats (1) compared to Bob's, while Bob has a higher value for books and balls. To maximize my points, I should focus on claiming as many hats as possible, since it has the highest value for me. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:08,638][mllm.models.large_language_model_local][WARNING] - Response Given that both you and Bob value the items similarly and proposing to take all of each item ensures that you both receive the full allocation, I will continue to propose to take all of each item. This strategy maximizes the points based on the values provided. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:08,722][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:08,725][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:08,727][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:08,730][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:08,732][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:08,733][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:08,783][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:08,786][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:08,788][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:08,790][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:08,793][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:08,795][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:08,798][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:08,800][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:08,802][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:08,804][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:08,805][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:09,082][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric nature of the per-item values and the potential for mutual benefit, a cooperative strategy where we split the items evenly would likely maximize our points. Therefore, a balanced proposal would be: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:09,148][mllm.models.large_language_model_local][WARNING] - Response Given the new per-item values, where you value hats at 1, books at 10, and balls at 10, and Bob values hats at 10, books at 10, and balls at 1, it makes sense to maximize your points by prioritizing books and balls, which have higher values for you. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:09,153][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric and competitive nature of the per-item values, a strategic proposal would be to aim for an equitable split that maximizes the points for both agents. Since both you and Alice value the books and balls highly, it's important to ensure that both of you get a substantial share of these items. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:09,251][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:09,254][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:09,256][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:09,258][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:09,259][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:09,263][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:09,266][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:09,268][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:09,270][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:09,273][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:09,275][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:09,303][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:09,304][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:09,306][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:09,309][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:09,311][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:09,313][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:09,315][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:09,317][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:09,318][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:09,320][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:09,321][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:09,322][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:09,324][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:09,327][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:09,415][mllm.models.large_language_model_local][WARNING] - Response Given Alice's values, she will likely try to secure more books and balls while I should prioritize hats. To maximize my points, I should propose a distribution that ensures I get a significant portion of the hats since my value for hats is much higher than for books or balls, while also taking into account Alice's valuation of books and balls. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:09,417][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:09,420][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:09,503][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and equal per-item values between you and Bob, the optimal strategy would be to propose an equal split of the items to ensure fair distribution and maximize the points based on your per-item values. Therefore, the proposal should be: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:09,591][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:09,672][mllm.models.large_language_model_local][WARNING] - Response Given the new values, Alice values hats and books highly, while you value hats and balls highly. A competitive strategy would be to maximize your value by ensuring you get the items you value the most, considering the proportional distribution rule. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:09,675][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:09,679][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:09,681][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:09,755][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:09,759][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:09,837][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:09,839][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:09,841][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:09,920][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:09,923][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:09,925][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:09,926][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:09,926][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:09,927][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:09,929][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:10,001][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the fact that both you and Alice have the same total value for each item (10 + 10 = 20 for hats and balls, and 1 + 10 = 11 for books), it's in your best interest to maximize your gains by proposing a split that reflects these values. Here, proposing an equal split seems fair and strategic. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:10,003][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:10,090][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:10,175][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both you and Alice have similar values for hats, but I value hats more and Alice values books and balls more. Since your per-item values for books and balls are both 1, while Alice's are 10 for books and 10 for balls, it might be strategically beneficial to propose taking more of the items you value higher to maximize your points. Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:10,176][mllm.models.large_language_model_local][WARNING] - Response Given the new values, you have a higher value for hats and books compared to Bob, while Bob values balls significantly more. To maximize your points, it would be strategic to propose a distribution that ensures you get a significant portion of the items you value highly. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:10,267][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:10,268][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:10,359][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:10,447][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:10,451][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:10,527][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:10,617][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:11,203][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:11,207][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:11,211][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:11,214][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:11,218][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:11,264][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:11,267][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:11,269][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:11,272][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:11,275][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:11,281][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:11,284][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:11,290][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:11,292][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:11,295][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:11,298][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:11,300][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:11,302][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:11,303][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:11,305][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:11,348][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Alice place high value on books and balls, while hats have much less value for both. To maximize your points, you should try to get as many books and balls as possible while sharing some hats. Since Alice values books and balls highly, you might need to give her some of these items to reach an agreement. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:11,513][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Alice place high value on books and balls, but not on hats. To maximize your points, you should aim to get as many books and balls as possible since they have higher individual values for you. Here's a proposed strategy: - You should propose to take all the books (10) and balls (10). - Share the hats (10) proportionally, as neither you nor Alice values hats highly. Proposal: 5 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:11,640][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the values and the previous outcomes where proposing to take all items results in an equal split and maximum points, I will continue this strategy to maximize my points. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:11,643][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:11,646][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:11,649][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:11,653][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:11,656][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:11,658][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:11,660][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:11,661][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:11,663][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:11,713][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:11,716][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:11,787][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:11,965][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:11,969][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:11,991][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:11,994][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:12,033][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the strategic importance of splitting the items equally while maximizing the points based on your and Bob's per-item values, the optimal proposal remains consistent. Here's the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:12,037][mllm.models.large_language_model_local][WARNING] - Response Given Alice's valuation of books and balls, and your own valuation, allocating more books and balls to you and more hats to Alice seems logical. Here's the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:12,077][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:12,081][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:12,165][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:12,169][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:12,172][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:12,175][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:12,179][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:12,182][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:12,185][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:12,227][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:12,230][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:12,238][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:12,245][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:12,249][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:12,254][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:12,258][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:12,263][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:12,266][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:12,269][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:12,271][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:12,273][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:12,275][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:12,308][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:12,311][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:12,313][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:12,316][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:12,318][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:12,321][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:12,323][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:12,325][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:12,328][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:12,330][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:12,332][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:12,335][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:12,412][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the previous outcomes where proposing to take all items led to equal splits and maximized points, I will continue this strategy. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:12,415][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:12,496][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:12,500][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:12,502][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:12,505][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:12,581][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:12,585][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:12,667][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:12,750][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:12,753][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:12,835][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:12,838][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:12,841][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:12,843][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:12,846][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:12,849][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:12,851][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:12,854][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:12,939][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:12,942][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:12,945][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:13,034][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:13,037][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:13,039][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:13,042][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:13,045][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:13,047][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:13,050][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:13,052][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:13,056][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:13,059][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:13,105][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:13,108][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:13,132][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the values of the items, proposing to take all of each item seems like the most rational strategy. Here's my proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:13,226][mllm.models.large_language_model_local][WARNING] - Response Given Alice's high value for books and balls, it's crucial to secure as many items with higher personal value for myself. Here's a strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:13,393][mllm.models.large_language_model_local][WARNING] - Response Given the values, it seems that splitting the items equally is beneficial, as it maximizes the product of the quantity proposed and the individual values for both Alice and Bob. Therefore, the optimal proposal would be to split all items equally. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:13,532][mllm.models.large_language_model_local][WARNING] - Response Given the values, both you and Bob place high importance on different items. To maximize your points, you should propose a split that aligns with your values while also considering Bob's likely response. Since you value hats (1) much less than books (10) and balls (10), and Bob values books and balls more, a mixed strategy is needed. A reasonable proposal would be to take the items that you value the most and let Bob take the rest. Here's a proposal that tries to balance this: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:13,785][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:13,789][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:13,791][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:13,794][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:13,797][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:13,841][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:13,844][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:13,847][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:13,849][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:13,852][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:13,855][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:13,857][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:13,860][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:13,862][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:13,865][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:13,868][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:13,870][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:13,873][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:13,876][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:13,878][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:13,881][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:13,884][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:13,886][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:13,889][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:13,896][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:13,902][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:13,909][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:13,915][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:13,921][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:13,927][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:13,932][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:13,937][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:13,940][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:13,978][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:13,981][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:13,983][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:13,989][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:13,996][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:13,999][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:14,002][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:14,004][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:14,007][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:14,087][mllm.models.large_language_model_local][WARNING] - Response Given the new per-item values, where my values are higher for hats and books, and lower for balls compared to Bob, a more strategic approach might be necessary. Since my values for hats and books are significantly higher, I should try to maximize those items while still accounting for the balls. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:14,090][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:14,093][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:14,096][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:14,433][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:14,437][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:14,440][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:14,442][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:14,517][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:14,520][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:14,602][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:14,605][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:14,688][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:14,691][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:14,694][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:14,697][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:14,700][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:14,703][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:14,706][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:14,778][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:14,781][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:14,857][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:14,938][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:14,941][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:15,097][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:15,100][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:15,181][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:15,263][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:15,266][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:15,269][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:15,350][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:15,353][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:15,359][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:15,366][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:15,372][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:15,379][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:15,432][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the high values both you and Alice place on different items, a strategic approach would be to propose a split that maximizes the points for the items you both value highly, while ensuring that neither party gains an unfair advantage. Here, since you both have high values for "books" and "balls," it might be wise to allocate more of these items to yourself and Alice respectively, while ensuring a fair split of "hats." Proposal: 5 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:15,834][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric nature of the values and the propositional strategy in previous rounds, it's clear that splitting the items equally maximizes points for both players. Therefore, I will continue to propose a fair distribution: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:15,917][mllm.models.large_language_model_local][WARNING] - Response Given the high value that both you and Alice place on books and balls, and the low value on hats, a strategic proposal would be to try to maximize the distribution of items that have the highest value for you. Here's a revised proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:16,107][mllm.models.large_language_model_local][WARNING] - Response Given the pattern of the previous rounds and the values, it seems that both you and Bob have strong preferences for different items, but you both have a balanced approach to ensure you get what you value highly. Since you value hats and balls highly and books moderately, and Bob values books and balls highly and hats moderately, it makes sense to propose a balanced distribution that maximizes the points for both parties, especially when the total quantity of items is the same as the number of items each values highly. Proposal: 10 hats, 10 books, 10 balls This proposal ensures that both of you get a fair share of the items you value highly, and the books are split evenly. Given the symmetry and the high value of hats and balls for you, and books and balls for Bob, this proposal should be acceptable and fair for both parties. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:16,111][mllm.models.large_language_model_local][WARNING] - Response Given the values and the symmetry in the situation, splitting the items evenly is likely the most strategic approach. Here is the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:16,207][mllm.models.large_language_model_local][WARNING] - Response Given the values, Alice values hats and books highly and balls minimally, while you value books and balls highly and hats minimally. To maximize your points, you should propose to take as many books and balls as possible, while allowing Alice to take the hats. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:16,414][mllm.models.large_language_model_local][WARNING] - Response Given the current values, Alice values books and balls more than hats, while you value hats more than books and balls. To ensure a fair split and maximize points, you should propose a distribution that balances the items. A reasonable proposal would be to split the items as evenly as possible, considering the different valuations. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:16,543][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the previous rounds and the per-item values, it seems that proposing to take all items is a reasonable strategy since the total value you would receive is maximized this way. Here is the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:16,546][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:16,549][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:16,552][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:16,555][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:16,557][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:16,560][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:16,563][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:16,565][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:16,569][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:16,572][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:16,575][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:16,577][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:16,580][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:16,583][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:16,586][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:16,588][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:16,591][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:16,594][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:16,596][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:16,599][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:16,602][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:16,605][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:16,607][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:16,610][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:16,613][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:16,616][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:16,618][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:16,626][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:16,628][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:16,631][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:16,634][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:16,636][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:16,638][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:16,641][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:16,908][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric values and the pattern in previous rounds, it seems optimal to propose a split that reflects the valuation of both parties equally to ensure a fair distribution. Here’s the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:16,952][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:16,955][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:16,973][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:16,976][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:16,979][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:16,996][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:16,999][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:17,002][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:17,005][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:17,008][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:17,024][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:17,027][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:17,068][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:17,090][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:17,093][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:17,209][mllm.models.large_language_model_local][WARNING] - Response Given the uneven value distribution, where you and Bob have complementary values, it's in your best interest to propose a split that maximizes your points. Since you value hats highly and Bob values books and balls highly, proposing an equal split might not be ideal. Here’s a more strategic proposal: Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:17,216][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:17,218][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:17,248][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:17,250][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:17,274][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:17,277][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:17,279][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:17,286][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:17,292][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:17,299][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:17,305][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:17,311][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:17,317][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:17,323][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:17,329][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:17,342][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:17,348][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:17,353][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:17,358][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:17,363][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:17,368][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:17,372][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:17,377][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:17,381][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:17,386][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:17,391][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:17,395][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:17,399][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:17,404][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:17,408][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:17,410][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:17,412][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:17,414][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:17,416][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:17,418][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's beneficial to recognize that Alice values books and balls more, while you value books the most and balls second most. To maximize your points, you should propose to take the majority of the items you value the most. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:17,420][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:17,422][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:17,423][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:17,425][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:17,427][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:17,429][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:17,431][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:17,433][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:17,441][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:17,443][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in per-item values and the previous outcomes, it appears that proposing to take all items is leading to a fair split. However, to explore if there's a better strategy, let's consider a different approach. Since we have a high value for books and Bob has a high value for balls, we might consider proposing a split that leverages these differences. Proposal: 10 hats, 10 books, 10 balls This proposal assumes that Bob will likely propose the same, leading to a fair split. Given the values, this should be a balanced approach. However, to deviate slightly and try to maximize our points by taking more of the items with higher individual values, we could propose: Proposal: 10 hats, 10 books, 5 balls This proposal assumes Bob might propose a similar distribution but might slightly adjust to maximize his points. By taking more books and fewer balls, we can potentially increase our points. Let's go with this proposal. Proposal: 10 hats, 10 books, 5 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:17,523][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:17,525][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:17,555][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:17,558][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:17,561][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:17,645][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:17,648][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:17,651][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:17,654][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:17,657][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:17,735][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and the symmetric nature of the situation, a reasonable strategy is to propose an allocation that ensures both parties receive an equitable share, maximizing the total points for both in this round. Since both you and Bob value hats and books highly, and balls less so, here is a balanced proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:17,738][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:17,741][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:17,899][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:17,983][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both hats and books are of relatively low value to you, while balls are of high value. However, Bob values books very highly and balls relatively low. To maximize your points, it's strategic to keep the high-value items (balls) and share the low-value items (hats and books) more evenly. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:17,986][mllm.models.large_language_model_local][WARNING] - Response Given the values, the best strategy is to propose a split that reflects the high value you place on books and balls compared to hats. Here's the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:17,990][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:17,992][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:17,995][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:18,069][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both you and Alice value books and balls more than hats. Since Alice values hats more than books, and both of you value books significantly more than hats, a balanced proposal would be to split the items as evenly as possible while maximizing the items with higher personal value. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:18,156][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:18,159][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:18,315][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:18,322][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:18,396][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:18,402][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:18,790][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:18,793][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:18,795][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:18,799][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:18,802][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:18,805][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:18,807][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:18,809][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:18,811][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:18,813][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:18,815][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:18,817][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:18,819][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:18,821][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:18,823][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:18,958][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:18,962][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:18,965][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:19,149][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both hats and balls are more valuable to Bob, and books are more valuable to you. To maximize your points, you should propose to take the majority of the books and leave the hats and balls for Bob. Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:19,409][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:19,412][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:19,416][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:19,419][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:19,422][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:19,424][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:19,427][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:19,432][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:19,437][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:19,442][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:19,446][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:19,451][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:19,455][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:19,460][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:19,463][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:19,467][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:19,474][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:19,613][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:19,725][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:19,745][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:19,748][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:19,764][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:19,786][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:19,789][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:19,792][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:19,806][mllm.models.large_language_model_local][WARNING] - Response Given the high value of books and balls for both parties, and the low value of hats, a balanced proposal might be optimal to ensure both agents benefit from the high-value items. However, since Alice values hats the most and we value books and balls the most, we need to be strategic to capture as much value from these items as possible. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:19,809][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:19,847][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:19,851][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:19,864][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's strategic to consider the high value items. Since both of us have high values for books and balls, it's important to secure these items. Here’s a proposal that aims to maximize the points by focusing on the high-value items: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:19,882][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:19,885][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:19,917][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:19,953][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:20,006][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:20,008][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:20,009][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:20,012][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:20,015][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:20,017][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:20,020][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:20,022][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:20,034][mllm.models.large_language_model_local][WARNING] - Response Given the high value of books and balls for both of us, and the low value of hats, the optimal strategy would be to maximize the分配给书籍和球的比例，因为我们对这两类物品的评价较高。考虑到对书籍和球的高价值，我们可以提议尽量多保留这些物品。一个合理的策略是保留所有书籍和大部分或全部的球，同时分担一部分帽子。 Proposal: 5 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:20,039][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:20,043][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:20,045][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:20,129][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:20,144][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:20,148][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:20,161][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:20,178][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:20,181][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:20,194][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:20,201][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:20,227][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:20,230][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:20,259][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:20,262][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:20,291][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:20,334][mllm.models.large_language_model_local][WARNING] - Response Given the values, it is clear that Bob values books the most and balls the least, while you value books and balls more than hats but not as much as Bob. A strategic proposal would be to maximize the distribution of items in a way that leverages these values. Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:20,350][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:20,353][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:20,356][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:20,358][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:20,371][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:20,375][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:20,377][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:20,379][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:20,382][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:20,385][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:20,388][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:20,391][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:20,393][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:20,396][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:20,398][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:20,409][mllm.models.large_language_model_local][WARNING] - Response Given the new values, where you have a high value for books and balls, and Bob has a high value for hats and books, it makes sense to propose keeping the items that align with your preferences. Here’s a proposing strategy: Proposal: 10 hats, 10 books, 10 balls However, to further optimize, considering the values, you might want to propose slightly less hats to align more with Bob's valuations while maximizing books and balls which you value highly. Proposal: 7 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:20,552][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:20,559][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:20,595][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:20,614][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:20,621][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:20,631][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:20,668][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:20,675][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:20,710][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:20,743][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:20,806][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:20,810][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:20,813][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:20,816][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:20,830][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:20,833][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:20,839][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:20,842][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:20,845][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:20,848][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:20,855][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:20,859][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:20,861][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:20,865][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:20,869][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:20,905][mllm.models.large_language_model_local][WARNING] - Response Given Alice's values, proposing to take all items might not be the best strategy as it could lead to a proportional split if Alice predicts my move. Instead, I should consider splitting the items to maximize my utility given Alice's values. - Alice values hats highly (10), books moderately (10), and balls slightly (1). - I value hats slightly (1), books highly (10), and balls highly (10). A balanced approach would be to propose an allocation that Alice might accept while still maximizing my utility. Let's propose: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:20,910][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:20,972][mllm.models.large_language_model_local][WARNING] - Response Given the values, it makes sense to focus on the items that have higher personal value while ensuring a balanced proposal. Since I value hats and books more and Bob values balls more, a balanced approach would be to propose slightly more of the items I value and ensure the total does not exceed the available items. Proposal: 7 hats, 7 books, 6 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:21,158][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:21,166][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:21,173][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:21,179][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:21,189][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:21,192][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:21,195][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:21,206][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:21,230][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:21,232][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:21,244][mllm.models.large_language_model_local][WARNING] - Response Given the high value of hats for both of us, and the high value of books and balls for Bob, proposing an equal distribution seems to be a fair strategy. However, to maximize my points, I should consider proposing to take more items that I value highly (hats and balls). Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:21,246][mllm.models.large_language_model_local][WARNING] - Response Given the updated per-item values, you should consider proposing an allocation that takes into account the lower values for books and balls compared to hats. To maximize your points, it's crucial to get as many hats as possible since they offer the highest value for you. Proposal: 10 hats, 10 books, 1 ball did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:21,247][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:21,296][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in per-item values and the previous outcomes, it seems both Alice and I have optimal proposals when we allocate all items equally. This ensures we maximize our individual scores based on our values. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:21,345][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:21,361][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:21,408][mllm.models.large_language_model_local][WARNING] - Response Given the values, you should propose a strategy that maximizes your points. Since you value books and balls highly, while Alice values hats more, you should try to keep as many books and balls for yourself. Here’s a balanced proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:21,450][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:21,522][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:21,525][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:21,555][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:21,559][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:21,561][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:21,565][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:21,568][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:21,583][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:21,585][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:21,589][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:21,593][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:21,595][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:21,597][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:21,668][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:21,681][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:21,728][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:21,757][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it is important to ensure that both you and Alice get a fair share that maximizes your points. In this round, you value books and balls highly, while Alice values hats and books more. Here's a proposal that might help balance the distribution: Proposal: 10 hats, 10 books, 10 balls This proposal keeps things symmetric, which might help avoid disputes and ensure that both you and Alice receive a fair share. However, if you want to try a different approach to ensure you get more of the items you value, you could consider proposing to take more of the items you value highly. Proposal: 10 hats, 10 books, 10 balls This keeps the symmetry and ensures that both you and Alice can potentially earn high points. If Alice responds similarly, you can both end up with high scores. If you decide to try a different approach, you could propose something like: Proposal: 0 hats, 10 books, 10 balls This would give you the books and balls entirely, which are high in value for you, but might not be optimal if Alice proposes similarly, leading to a split. Given the symmetry and the goal of maximizing points, I will stick with the original symmetric proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:21,771][mllm.models.large_language_model_local][WARNING] - Response Given the values, you have a higher valuation for books and balls compared to hats, while Bob has a higher valuation for hats and books. Since we both propose to take essentially all items, let's consider a more strategic approach. You might want to offer a split that gives you a better ratio of books and balls, while still accounting for hats reasonably. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:21,774][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:21,827][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:21,841][mllm.models.large_language_model_local][WARNING] - Response Given the values, a strategic approach might involve noting that both you and Bob value hats and books highly, but Bob values balls much more. To maximize your points, you could propose keeping more hats and books while giving Bob the balls to leverage his higher valuation of them. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:21,848][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:21,852][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:21,858][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:21,866][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:21,869][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:21,873][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:21,878][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:21,883][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:21,889][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:21,892][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:21,895][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:21,900][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in values and the equal distribution of item quantities, the optimal strategy is to propose a split that matches or closely approximates the distribution of values. Since both you and Alice have similar values for hats and books, and balls are valued similarly but differently by each of you, a competitive yet cooperative approach seems appropriate. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:21,902][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:21,905][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:21,906][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:22,030][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:22,074][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:22,118][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:22,125][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:22,182][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:22,197][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:22,232][mllm.models.large_language_model_local][WARNING] - Response Given the updated values, where you have a high value for hats and balls, and Alice has a high value for books, proposing to take most of the items that you value highly should maximize your points. Here's a strategy: - You value hats and balls highly (10 each), while Alice values books the most (10). - Proposing to take all the hats and balls and none of the books would align with your high values. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:22,239][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:22,245][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:22,251][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:22,258][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:22,265][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:22,267][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:22,268][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:22,272][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:22,277][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:22,279][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:22,368][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in per-item values and the outcomes of previous rounds, it seems that proposing to take all items is a stable strategy. However, let's consider a more strategic approach to maximize points while also ensuring a balanced exchange. Since you and Bob have complementary valuations, proposing a fair split might lead to higher individual earnings by leveraging the complementary values. Proposal: 10 hats, 10 books, 10 balls This proposal assumes that since both of you will get 100% of the items, you can benefit from the high values of the items you both desire. Let's split the items evenly. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:22,440][mllm.models.large_language_model_local][WARNING] - Response Given the values, you have a high value for hats and books, while Alice values books and balls highly. To maximize your points, you should try to keep more of the items you value more highly. Here's a strategic proposal: Proposal: 10 hats, 10 books, 1 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:22,575][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:22,590][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:22,668][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both Alice and I have high values for books and balls, while we value hats the least. However, proposing to take all items might not always divide the surplus in our favor if Alice anticipates the same and also proposes to take all items. A strategic approach would be to recognize that splitting the items might result in a better outcome if we can secure a fair share. If we assume Alice will also try to maximize her points by taking all items, we should propose an allocation that reflects this understanding and allows us to maximize our specific valuable items. Proposal: 10 hats, 10 books, 10 balls This equal distribution ensures that we both have a chance to secure high-value items, given Alice's preference for books and balls as well. If Alice also proposes to take all items, this proposal might lead to a proportional distribution that is more favorable than taking all items ourselves. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:22,671][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:22,674][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:22,824][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:22,831][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:22,835][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:22,838][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:22,845][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:22,901][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:22,904][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:22,906][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:22,908][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:22,911][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:23,298][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:23,589][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric nature of the per-item values and Alice's likely strategy, a collaborative and equal distribution might be the best approach to maximize points. Here, I will propose an equitable split: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:23,802][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:23,806][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:23,809][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:23,833][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:23,861][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:23,864][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:23,891][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Bob value balls the most at 10 each. It's also worth noting that you both value hats and books less, with values of 10 for hats and 10 for books from Bob's perspective, and 10 for books and 1 for hats from your perspective. To maximize your points, you should propose to take all the balls, and split the hats and books proportionally, as they are less valued but still have non-zero value. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:23,894][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and equal expected value per item, and considering Alice might be strategic, a balanced approach might be optimal. Here is the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:23,966][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the equal expected values, it makes sense to propose the same distribution as Bob to ensure a fair split and maximize points. Therefore, the proposal will be: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:24,002][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:24,037][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:24,041][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:24,043][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:24,049][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:24,051][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:24,070][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:24,095][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:24,098][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:24,107][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:24,120][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:24,123][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:24,130][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:24,230][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:24,266][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:24,273][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:24,281][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:24,284][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:24,299][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:24,330][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:24,337][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:24,371][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:24,413][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:24,491][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:24,636][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and the symmetry in the problem, it seems that proposing the same amounts for each item as Bob would lead to a fair distribution. Let's propose: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:24,650][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:24,664][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and the previous round results, a strategic approach would be to maximize the points by capturing the high-value items for yourself. Since you value books and balls at 10 and Alice values them at only 1, and hats are valued equally, it might be beneficial to propose slightly more of the high-value items to ensure you get a higher share. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:24,790][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:24,957][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:25,151][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:25,164][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:25,178][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:25,182][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:25,204][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:25,216][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:25,321][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:25,325][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:25,346][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:25,369][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:25,393][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:25,397][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:25,423][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:25,435][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:25,539][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:25,542][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:25,563][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:25,586][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:25,611][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:25,618][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:25,629][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the previous outcomes, it makes sense to propose an allocation that reflects mutual benefit. Since both Alice and Bob have high values for books and balls, and low values for hats, splitting all items equally would be a fair and strategic move. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:25,642][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:25,741][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, the optimal strategy would be to propose the distribution that maximizes the product of the amount kept and the respective per-item values. Here, the values are quite different, with hats being valued highly by both of us, and books being valued equally. Balls are valued very highly by me but not by Alice. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:25,769][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:25,776][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:25,793][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:25,820][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in per-item values and the previous rounds' outcomes, it seems that proposing to take all items is a tactic that both Bob and I find beneficial. However, to explore a more strategic approach, we should consider that proposing a split that takes into account the relative values can sometimes lead to a more balanced and potentially higher total value in the long run. Proposal: 1 hat, 10 books, 10 balls This proposal reflects an attempt to maximize the points based on the higher values of books and balls, while still taking one hat. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:25,824][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that books are highly valued by both you and Alice, while hats and balls have relatively low values. To maximize points, you should propose to keep as many books as possible while splitting the hats and balls more evenly. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:25,857][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's advantageous to propose a distribution that maximizes the value based on our and Alice's per-item valuations. Since both of us value books and balls highly, and Alice values hats highly, it's best to propose a split that maximizes our combined value. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:25,860][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:25,986][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both you and Bob place high value on different items. To maximize your score, you should propose a split that maximizes your points based on your values. Here's the proposal: Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:26,094][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:26,125][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:26,128][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:26,137][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:26,220][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:26,267][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:26,279][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:26,302][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:26,339][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:26,351][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:26,429][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the equal per-item values between us, proposing an equal distribution is a fair and strategic approach. Let's propose the following: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:26,522][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:26,535][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:26,775][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:26,868][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:26,955][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:26,958][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:27,070][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:27,073][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:27,076][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:27,146][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:27,149][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:27,158][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, you have a high valuation for hats and books, and Alice values balls highly. If both of you propose the same amounts for each item, the items will be split proportionally based on your proposals. To maximize your points, it's crucial to offer a higher quantity of items you value more, which are hats and books. Proposal: 10 hats, 10 books, 1 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:27,264][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:27,269][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:27,275][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:27,345][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:27,352][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:27,360][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:27,474][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:27,487][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:27,568][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:27,868][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:27,879][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:28,045][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:28,056][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:28,226][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:28,237][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:28,602][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:28,613][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:01:28,778][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:28,790][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:01:28,953][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:28,966][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:01:30,046][__main__][INFO] - Number of regex retries in iteration 569: 1743 [2025-11-13 07:01:30,047][__main__][INFO] - agents played in iteration 569 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 07:01:31,248][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 07:01:31,267][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 07:01:31,289][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 07:01:31,310][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.60%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 07:01:31,310][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 07:01:31,311][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 07:01:32,114][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 07:01:32,571][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 07:01:33,074][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 07:01:33,575][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 07:01:34,080][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 07:01:34,573][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 07:01:35,066][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 07:01:35,557][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 07:01:36,050][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 07:01:36,555][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 07:01:37,046][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 07:01:37,536][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 07:01:38,031][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 07:01:38,520][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 07:01:39,013][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 07:01:39,506][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 07:01:39,997][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 07:01:40,488][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 07:01:40,983][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 07:01:41,473][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 07:01:41,967][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 07:01:42,457][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 07:01:42,947][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 07:01:43,450][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 07:01:43,943][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 07:01:44,434][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 07:01:44,935][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 07:01:45,424][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 07:01:45,915][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 07:01:46,408][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 07:01:46,902][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 07:01:47,394][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 07:01:47,886][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 07:01:48,379][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 07:01:48,868][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 07:01:49,358][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 07:01:49,856][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 07:01:50,347][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 07:01:50,835][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 07:01:51,327][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 07:01:51,821][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 07:01:52,317][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 07:01:52,814][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 07:01:53,312][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 07:01:53,811][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 07:01:54,311][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 07:01:54,810][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 07:01:55,308][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 07:01:55,805][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 07:01:56,302][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 07:01:56,801][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 07:01:57,294][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 07:01:57,794][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 07:01:58,294][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 07:01:58,791][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 07:02:00,670][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 07:02:01,724][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 07:02:02,220][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 07:02:02,716][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 07:02:03,213][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 07:02:03,710][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 07:02:04,212][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 07:02:04,710][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 07:02:05,211][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 07:02:05,711][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10888 tokens. [2025-11-13 07:02:06,589][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.25%, Current % of VRAM taken: 59.70%, Block Peak % of device VRAM: 62.52%, ΔTime: 00:00:34 [2025-11-13 07:02:07,242][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 07:02:07,244][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 07:02:07,246][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 07:02:08,327][__main__][INFO] - Iteration 570 took 1m 40s (61.83% Gen, 37.09% Train). Generation: 1m 2s, Training: 37s. Estimated remaining time: 74h 42m 11s. Estimated total time: 83h 34m 38s. Time estimates for 10 more iterations: 16m 42s, 100 more iterations: 2h 47m 9s, 500 more iterations: 13h 55m 46s. [2025-11-13 07:02:08,329][__main__][INFO] - Starting iteration 570. [2025-11-13 07:02:08,830][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 56 and human policies 1. [2025-11-13 07:02:08,831][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 07:02:30,754][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:30,777][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:30,780][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:30,781][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:30,783][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:30,784][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:30,784][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:30,785][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:30,785][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:30,801][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:30,804][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:30,807][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:30,810][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:30,813][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:30,815][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:30,818][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:30,830][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:30,833][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:30,835][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:30,837][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:30,839][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:30,841][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:30,842][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:30,844][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:30,845][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:30,847][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:30,849][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:30,851][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:30,852][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:30,856][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:30,858][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:30,859][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:30,861][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:30,862][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:30,864][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:30,866][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:30,868][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:30,870][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:30,872][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:30,873][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:30,875][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:30,876][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:30,877][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:30,879][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:30,887][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:30,889][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:30,891][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:30,892][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:30,894][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:30,896][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:30,898][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:30,899][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:30,900][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:30,900][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:30,902][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:30,903][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:30,906][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:30,907][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:30,909][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:30,910][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:30,911][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:30,913][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:30,914][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:30,915][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:30,916][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:30,918][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:30,919][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:30,921][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:30,922][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:30,923][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:30,924][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:30,925][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:30,926][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:30,928][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:30,929][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:30,931][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:30,933][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:30,935][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:30,936][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:30,948][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:30,949][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:30,951][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:30,953][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:30,954][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:30,955][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:30,957][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:30,958][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:30,960][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:30,961][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:30,963][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:30,965][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:30,967][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:31,099][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:31,116][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:31,119][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:31,121][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:31,124][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:31,127][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:31,129][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:31,132][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:31,134][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:31,142][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:31,144][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:31,147][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:31,149][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:31,151][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:31,154][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:31,157][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:31,168][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:31,171][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:31,173][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:31,176][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:31,177][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:31,179][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:31,181][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:31,184][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:31,184][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:31,185][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:31,185][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:31,185][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:31,186][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:31,186][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:31,187][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:31,187][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:31,189][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:31,191][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:31,192][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:31,194][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:31,201][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:31,203][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:31,205][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:31,207][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:31,209][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:31,211][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:31,212][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:31,214][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:31,215][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:31,217][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:31,218][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:31,220][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:31,224][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:31,225][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:31,226][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:31,227][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:31,229][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:31,231][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:31,234][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:31,235][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:31,235][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:31,236][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:31,236][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:31,239][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:31,241][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:31,245][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:31,246][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:31,247][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:31,249][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:31,251][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:31,252][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:31,253][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:31,254][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:31,256][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:31,257][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:31,259][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:31,260][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:31,266][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:31,267][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:31,268][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:31,269][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:31,270][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:31,272][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:31,273][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:31,703][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:32,054][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:32,055][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:32,056][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:32,059][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:32,060][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:32,062][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:32,063][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:32,064][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:32,065][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:32,066][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:32,068][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:32,070][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:32,071][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:32,343][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:32,368][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:32,372][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:32,375][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:32,407][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:32,411][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:32,415][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:32,417][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:32,420][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:32,429][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:32,432][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:32,434][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:32,436][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:32,439][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:32,455][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:32,457][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:32,460][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:32,522][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:32,526][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:32,528][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:32,551][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:32,554][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:32,557][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:32,560][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:32,563][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:32,565][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:32,567][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:32,570][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:32,577][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:32,580][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:32,582][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:32,584][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:32,586][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:32,589][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:32,590][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:32,592][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:32,594][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:32,595][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:32,597][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:32,598][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:32,600][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:32,603][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:32,605][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:32,611][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:32,612][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:32,615][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:32,616][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:32,618][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:32,620][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:32,622][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:32,623][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:32,625][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:32,627][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:32,628][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:32,631][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:32,632][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:32,634][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:32,635][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:32,636][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:32,637][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:32,642][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:32,643][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:32,644][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:32,645][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:32,646][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:32,647][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:32,648][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:32,649][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:32,650][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:32,651][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:32,652][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:32,653][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:32,655][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:32,656][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:32,657][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:32,659][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:32,660][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:32,661][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:33,115][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:33,139][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:33,140][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:33,142][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:33,145][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:33,168][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:33,171][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:33,173][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:33,176][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:33,178][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:33,180][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:33,183][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:33,185][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:33,187][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:33,195][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:33,198][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:33,200][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:33,203][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:33,205][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:33,207][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:33,210][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:33,212][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:33,215][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:33,217][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:33,219][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:33,227][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:33,230][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:33,232][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:33,234][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:33,236][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:33,238][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:33,240][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:33,243][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:33,244][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:33,246][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:33,249][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:33,251][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:33,259][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:33,261][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:33,263][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:33,265][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:33,267][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:33,269][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:33,271][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:33,273][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:33,275][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:33,278][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:33,279][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:33,280][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:33,281][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:33,282][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:33,283][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:33,284][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:33,285][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:33,286][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:33,289][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:33,291][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:33,293][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:33,300][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:33,303][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:33,304][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:33,306][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:33,308][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:33,309][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:33,309][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:33,310][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:33,311][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:33,312][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:33,314][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:33,314][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:33,316][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:33,317][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:33,319][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:33,392][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:33,415][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:33,418][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:33,421][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:33,438][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:33,442][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:33,445][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:33,447][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:33,450][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:33,459][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:33,462][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:33,464][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:33,467][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:33,469][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:33,477][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:33,478][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:33,480][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:33,510][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:33,512][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:33,529][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:33,532][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:33,537][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:33,543][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:33,551][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:33,555][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:33,558][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:33,560][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:33,563][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:33,565][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:33,567][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:33,570][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:33,574][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:33,578][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:33,582][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:33,585][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:33,590][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:33,592][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:33,595][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:33,598][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:33,601][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:33,603][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:33,606][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:33,608][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:33,612][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:33,615][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:33,618][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:33,621][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:33,623][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:33,626][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:33,628][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:33,630][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:33,632][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:33,635][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:33,638][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:33,640][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:33,642][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:33,645][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:33,647][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:33,649][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:33,651][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:33,654][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:33,658][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:33,660][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:33,662][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:33,664][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:33,667][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:33,669][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:33,671][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:33,674][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:33,676][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:33,678][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:33,680][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:33,682][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:33,684][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:33,686][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:33,688][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:33,691][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:33,693][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:33,695][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:33,696][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:33,696][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:33,697][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:33,699][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:33,700][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:33,701][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:33,702][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:33,703][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:33,704][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:33,706][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:33,708][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:33,794][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:33,816][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:33,818][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:33,820][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:33,846][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:33,848][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:33,850][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:33,853][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:33,868][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:33,871][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:33,874][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:33,877][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:33,880][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:33,889][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:33,891][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:33,893][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:33,895][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:33,927][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:33,933][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:33,943][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:34,272][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:34,275][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:34,278][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:34,281][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:34,284][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:34,287][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:34,290][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:34,293][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:34,296][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:34,299][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:34,303][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:34,520][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:34,552][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:34,559][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:34,567][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:34,595][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:34,601][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:34,608][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:34,615][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:34,626][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:34,632][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:34,636][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:34,641][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:34,651][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:34,655][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:34,659][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:34,664][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:34,668][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:34,729][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:34,792][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:34,795][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:34,818][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:34,822][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:34,825][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:34,829][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:34,831][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:34,832][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:34,834][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:34,847][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:34,850][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:34,853][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:34,856][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:34,858][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:34,861][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:34,876][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:34,880][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:34,882][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:34,885][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:34,887][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:34,890][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:34,892][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:34,894][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:34,897][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:34,905][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:34,907][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:34,910][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:34,912][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:34,915][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:34,917][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:34,918][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:34,919][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:34,920][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:34,921][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:34,924][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:34,927][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:34,929][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:34,936][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:34,938][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:34,940][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:34,942][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:34,944][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:34,946][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:34,948][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:34,950][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:34,959][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:34,962][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:34,965][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:34,966][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:34,967][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:34,968][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:34,970][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:34,973][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:34,976][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:34,978][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:34,980][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:35,009][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:35,408][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:35,410][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:35,436][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:35,438][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:35,454][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:35,457][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:35,460][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:35,463][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:35,466][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:35,468][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:35,471][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:35,483][mllm.models.large_language_model_local][WARNING] - Response Given the new per-item values, where your values for hats and books are significantly lower than Alice's, but much higher for balls, a strategic proposal would be to maximize your points from the items where you have a high value. Here's the proposal: Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:35,486][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:35,488][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:35,491][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:35,494][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:35,496][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:35,499][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:35,505][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:35,508][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:35,510][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:35,511][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:35,514][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:35,529][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:35,531][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:35,534][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:35,536][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:35,539][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:35,541][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:35,544][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:35,546][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:35,548][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:35,551][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:35,553][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:35,555][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:35,557][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:35,559][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:35,562][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:35,564][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:35,566][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:35,567][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:35,568][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:35,569][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:35,571][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:35,573][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:35,575][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:35,576][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:35,581][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:35,583][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:35,585][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:35,587][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:35,589][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:35,591][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:35,592][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:35,592][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:35,605][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:35,606][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:35,608][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:35,609][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:35,611][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:35,613][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:35,614][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:35,615][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:35,616][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:35,631][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:35,633][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:35,635][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:35,637][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:35,639][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:35,642][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:35,679][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:35,702][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:35,705][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:35,708][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:35,726][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:35,749][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:35,753][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:35,756][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:35,758][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:35,769][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:35,772][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:35,774][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:35,777][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:35,779][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:35,788][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:35,791][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:35,793][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:35,823][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:35,826][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:35,839][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:35,843][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:35,856][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:35,861][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:35,866][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:35,871][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:35,876][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:35,880][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:35,885][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:35,892][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:35,897][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:35,899][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:35,901][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:35,903][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:35,908][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:35,912][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:35,915][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:35,918][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:35,922][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:35,925][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:35,928][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:35,932][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:35,935][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:35,938][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:35,941][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:35,943][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:35,946][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:35,950][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:35,953][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:35,955][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:35,957][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:35,959][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:35,961][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:35,964][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:35,967][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:35,970][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:35,972][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:35,974][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:35,976][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:35,979][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:35,983][mllm.models.large_language_model_local][WARNING] - Response Given the new per-item values where both you and Alice highly value different items, a strategic proposal would be to maximize the points based on your higher valuations. Here's the proposal: Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:35,984][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:35,986][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:35,988][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:35,991][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:35,993][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:35,996][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:35,998][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:36,000][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:36,002][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:36,005][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:36,007][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:36,009][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:36,011][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:36,016][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:36,019][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:36,021][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:36,023][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:36,024][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:36,025][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:36,026][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:36,027][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:36,029][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:36,030][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:36,031][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:36,032][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:36,033][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:36,034][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:36,078][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:36,101][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:36,102][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:36,104][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:36,128][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:36,153][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:36,155][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:36,157][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:36,159][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:36,175][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:36,177][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:36,179][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:36,181][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:36,184][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:36,198][mllm.models.large_language_model_local][WARNING] - Response Given the new per-item values, where you value hats and books at 1 and balls at 10, while Alice values hats at 10, books at 1, and balls at 1, it's in your best interest to maximize the value from balls since they have a significantly higher value for you. Proposal: 0 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:36,202][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:36,204][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:36,205][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:36,240][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:36,245][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:36,573][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:36,637][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:36,641][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:36,645][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:36,650][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:36,655][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:36,659][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:36,663][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:36,667][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:36,671][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:36,675][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:36,817][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:36,862][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:36,869][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:36,875][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:36,907][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:36,929][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:36,932][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:36,961][mllm.models.large_language_model_local][WARNING] - Response Given the updated values, both you and Alice place a high value on books and balls. To maximize your points, you should propose to take a significant portion of the items while ensuring the total does not exceed the available quantity. Here is a proposed strategy: - You value hats less (10) compared to Alice (10), and books and balls equally (10 for you, 10 for Alice). - A balanced approach would be to propose taking a majority of the books and balls, and splitting the hats roughly. Proposal: 5 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:36,964][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:36,970][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:36,976][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:36,981][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:36,988][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:37,000][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:37,005][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:37,011][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:37,095][mllm.models.large_language_model_local][WARNING] - Response Given the new per-item values, I should focus more on books and balls since they have higher personal values. Here's the optimal proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:37,159][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:37,161][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:37,187][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:37,190][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:37,193][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:37,195][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:37,221][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:37,224][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:37,226][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:37,229][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:37,231][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:37,233][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:37,234][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:37,247][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:37,250][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:37,253][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:37,256][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:37,259][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:37,261][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:37,276][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:37,279][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:37,281][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:37,284][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:37,287][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:37,290][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:37,304][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:37,307][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:37,309][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:37,312][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:37,314][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:37,315][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:37,316][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:37,319][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:37,321][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:37,332][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:37,334][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:37,336][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:37,338][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:37,340][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:37,342][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:37,344][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:37,346][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:37,348][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:37,360][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:37,362][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:37,363][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:37,364][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:37,366][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:37,367][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:37,368][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:37,370][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:37,397][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:37,647][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:37,650][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:37,805][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:37,807][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:37,830][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:37,832][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:37,852][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:37,856][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:37,858][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:37,861][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:37,873][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:37,875][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:37,878][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:37,881][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:37,884][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:37,885][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:37,888][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:37,897][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:37,899][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:37,901][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:37,903][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:37,905][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:37,907][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:37,920][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:37,923][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:37,925][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:37,927][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:37,929][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:37,931][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:37,941][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:37,944][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:37,947][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:37,949][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:37,951][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:37,953][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:37,956][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:37,958][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:37,960][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:37,969][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:37,970][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:37,973][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:37,975][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:37,977][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:37,980][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:37,982][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:37,993][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:37,995][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:37,997][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:37,999][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:38,002][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:38,004][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:38,006][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:38,008][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:38,009][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:38,011][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:38,012][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:38,017][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:38,020][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:38,022][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:38,024][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:38,026][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:38,028][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:38,029][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:38,031][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:38,033][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:38,034][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:38,036][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:38,043][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:38,065][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:38,067][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:38,087][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:38,114][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:38,131][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:38,135][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:38,152][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:38,155][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:38,158][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:38,161][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:38,164][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:38,167][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:38,169][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:38,198][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:38,200][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:38,214][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:38,216][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:38,234][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:38,238][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:38,254][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:38,259][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:38,264][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:38,274][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:38,279][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:38,284][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:38,291][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:38,295][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:38,300][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:38,307][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:38,311][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:38,315][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:38,318][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:38,322][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:38,326][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:38,330][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:38,333][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:38,336][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:38,339][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:38,342][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:38,345][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:38,353][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:38,356][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:38,358][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:38,361][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:38,363][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:38,366][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:38,369][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:38,378][mllm.models.large_language_model_local][WARNING] - Response Given the updated values, both you and Bob value hats and books highly, and balls less. To maximize your points, it's best to secure as many hats and books as possible while still considering the possibility of Bob proposing a similar split. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:38,381][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:38,387][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:38,392][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:38,396][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:38,399][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:38,402][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:38,404][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:38,407][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:38,410][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:38,414][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:38,417][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:38,419][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:38,420][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:38,421][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:38,423][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:38,426][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:38,435][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:38,437][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:38,439][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:38,441][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:38,444][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:38,444][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:38,445][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:38,446][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:38,447][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:38,448][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:38,448][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:38,449][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:38,467][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:38,468][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:38,470][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:38,473][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:38,502][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:38,503][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:38,532][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:38,555][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:38,580][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:38,583][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:38,601][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:38,607][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:38,622][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:38,625][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:38,628][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:38,631][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:38,634][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:38,642][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:38,645][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:38,664][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:38,669][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:38,726][mllm.models.large_language_model_local][WARNING] - Response Given the updated values, where you value books at 10 and balls at 1, and Bob values books and balls much lower, it might be more beneficial to focus on securing more books and balls to maximize your points. Here's a strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:39,063][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:39,070][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:39,131][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:39,135][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:39,139][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:39,141][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:39,144][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:39,147][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:39,151][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:39,155][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:39,224][mllm.models.large_language_model_local][WARNING] - Response Given the dynamic values in this round, it's crucial to align the proposal with the high value that Bob places on books and your own value for hats and balls. Here is the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:39,225][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:39,230][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:39,317][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:39,326][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:39,406][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:39,496][mllm.models.large_language_model_local][WARNING] - Response Given the values, the optimal strategy would be to propose a split that balances the distribution of items to maximize the weighted sum of values for both you and Bob. Since both you and Bob value balls highly but differ in valuing hats and books, a fair proposal that considers the differences in values could be: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:39,499][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:39,526][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:39,533][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:39,615][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:39,622][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:39,629][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:39,650][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:39,656][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:39,661][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:39,772][mllm.models.large_language_model_local][WARNING] - Response Given the new values, where your per-item values are hats=10, books=1, and balls=1, and Alice's are hats=1, books=1, and balls=10, it's clear that you value hats significantly more than Alice does, while she values balls more than you do. To maximize your points, you should propose to take all the hats and a significant portion of the balls, while giving Alice a small portion of the books. Since the total number of items for each type is 10, a reasonable proposal would be to take all the hats and most of the balls, ensuring that the total proposed amount does not exceed the available quantity. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:39,775][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:39,802][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:39,805][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:39,808][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:39,810][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:39,836][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:39,839][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:39,842][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:39,863][mllm.models.large_language_model_local][WARNING] - Response Given the new values, I should propose a strategy that accounts for the high value of books and balls for Bob, and the high value of hats and books for me. A competitive but fair proposal would be to split the items as follows: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:39,866][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:39,869][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:39,872][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:39,875][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:39,878][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:39,892][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:39,895][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:39,898][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:39,920][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:39,923][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:39,926][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:39,947][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:39,950][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:39,975][mllm.models.large_language_model_local][WARNING] - Response Given the values, the optimal strategy would be to propose the entire items to yourself since you have a significantly higher value for hats and a slightly higher value for books compared to Alice. Here is the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:39,981][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:39,984][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:39,986][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:39,987][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:39,989][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:39,990][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:39,991][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:39,993][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:40,017][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:40,019][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:40,021][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:40,022][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:40,038][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:40,311][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:40,313][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:40,367][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:40,370][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:40,428][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:40,432][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:40,453][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:40,456][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:40,459][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:40,462][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:40,478][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:40,481][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:40,483][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:40,497][mllm.models.large_language_model_local][WARNING] - Response Given the values, you should propose to take the items that match your higher valuation while leaving the lower valued items for Alice. Here, you value hats and books more, and Alice values books more. To maximize your points, you should propose to take all the hats and books, and leave the balls for Alice. Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:40,498][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:40,500][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:40,502][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:40,505][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:40,507][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:40,510][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:40,526][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:40,528][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:40,552][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:40,554][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:40,577][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:40,580][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:40,582][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:40,604][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:40,607][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:40,609][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:40,612][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:40,614][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:40,617][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:40,619][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:40,622][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:40,639][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:40,642][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:40,644][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:40,646][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:40,648][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:40,650][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:40,653][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:40,656][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:40,657][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:40,658][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:40,659][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:40,661][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:40,713][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:40,732][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:40,734][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:40,736][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:40,776][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:40,792][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:40,796][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:40,799][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:40,802][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:40,805][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:40,850][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:40,857][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:40,867][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:40,873][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:40,879][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:40,881][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:40,884][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:40,886][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:40,908][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:40,910][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:40,915][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:40,919][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:40,922][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:40,927][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:40,934][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:40,953][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:40,957][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:40,970][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:40,994][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:40,999][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:41,003][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:41,008][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:41,012][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:41,020][mllm.models.large_language_model_local][WARNING] - Response Given the updated values where you have a higher value for hats and Bob has a higher value for balls, a strategic approach would be to maximize your points by claiming the items you value more. Here’s the proposal: Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:41,022][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:41,026][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:41,030][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:41,033][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:41,045][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric and alternating nature of the per-item values, a cooperative strategy might be effective. We can propose to split the items evenly, ensuring both agents get a fair share regardless of the values. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:41,046][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:41,049][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:41,050][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:41,053][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:41,056][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:41,060][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:41,064][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:41,067][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:41,069][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:41,071][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:41,123][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:41,124][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:41,141][mllm.models.large_language_model_local][WARNING] - Response Given the current values, Alice values hats significantly more than books or balls, while you value balls more than other items. To maximize your points, you should focus on claiming as many balls as possible since they have the highest value for you. Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:41,180][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both hats and books have higher value for you compared to balls. Similarly, both hats and books have higher value for Bob compared to balls. Allocating items that maximize your value while considering the proportional allocation rule would be beneficial. Here's the optimal proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:41,187][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:41,193][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:41,203][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:41,206][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:41,208][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:41,246][mllm.models.large_language_model_local][WARNING] - Response Given the new values, where you have a high value for hats and balls, and Alice has a high value for books, it's important to maximize your points by proposing more items that you value highly. Here is a strategic proposal: Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:41,285][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:41,292][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:41,310][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the random assignment of items, a fair and strategic proposal would be to split the items equally. This ensures that both agents receive an equal share of the items, which is reasonable given the similar per-item values. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:41,311][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:41,332][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that hats are highly valued by both Alice and you, while books and balls have much lower values. To maximize your points, you should propose to keep more hats and books, and a minimal number of balls. Here's a strategic proposal: Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:41,396][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric situation where both you and Alice have complementary valuations for the items, proposing a fair split seems to be the best strategy. Here's the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:41,524][mllm.models.large_language_model_local][WARNING] - Response Given the pattern in Alice's value assignments and the past rounds, it seems Alice values hats the most, followed by balls, and then books. To maximize my points, I should focus on securing more hats and balls since I value these items highly, while minimizing the allocation of books. Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:41,578][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:41,633][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:41,639][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:41,704][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric and mutually beneficial nature of the per-item values in this round, proposing an equal split would maximize the points for both Alice and Bob. Therefore, the proposal will be: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:41,708][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:41,714][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:41,719][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:41,759][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both hats and balls are equally valuable to Bob, and both books are valuable to you. To maximize points, you should try to capture the items that are more valuable to you while also considering the proportional distribution if both of you claim the same items. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:41,847][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:41,854][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:41,936][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:42,099][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:42,107][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:42,114][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:42,167][mllm.models.large_language_model_local][WARNING] - Response Given the new valuation where I prefer hats and books over balls, and considering Bob's preferences are reversed, it makes sense to assert control over the items I value higher. A strategic proposal would be to take all items I value more to ensure I maximize my points. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:42,170][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:42,231][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values where you have high value for hats (1) and books (10), and Bob has high value for balls (10), a strategic proposal would be to take the items with your higher values and leave the ones with Bob's higher values for him. Here's the proposal: Proposal: 10 hats, 10 books, 10 balls However, since Bob values balls highly and you have low value for balls, it might be more strategic to give him the balls and take the other items. Let's adjust the proposal: Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:42,235][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric and high value of the items for both of us, proposing an even split would likely maximize our points. Therefore: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:42,238][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:42,241][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:42,278][mllm.models.large_language_model_local][WARNING] - Response Given the high value of books and balls for both of us, and the low value of hats, a strategy of keeping most of the books and balls while dividing hats equitably would be optimal. Here's the proposal: Proposal: 5 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:42,280][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:42,314][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:42,317][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:42,321][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:42,324][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:42,358][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:42,361][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:42,398][mllm.models.large_language_model_local][WARNING] - Response Given the values, you have a high value for books (10) and Bob values hats highly (10), which means books and hats are likely to be contested items. To maximize your points, you should propose keeping all the books and a significant portion of the hats, while still allowing Bob to keep at least one ball to avoid the total allocation exceeding the available items. Here's a strategic proposal: Proposal: 1 hat, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:42,405][mllm.models.large_language_model_local][WARNING] - Response Given the aligned values where both you and Bob place high value on hats and books respectively, and low value on balls, the optimal strategy would be to maximize the items with high mutual value while ensuring the total quantity proposed does not exceed the available items. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:42,422][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:42,427][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:42,430][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:42,432][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:42,447][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:42,454][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:42,457][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:42,460][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:42,462][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:42,472][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both hats and balls are more valuable to me, while books are more valuable to Alice. To maximize my points, I should aim to take most of the hats and balls, and a reasonable portion of the books. Here is a strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:42,476][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:42,479][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:42,481][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:42,518][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both you and Bob value hats and books more than balls. However, since your value for hats is very low (1), you might want to ensure you get enough books and balls to maximize your score. A good strategy might be to propose an allocation that values your higher per-item items (books and balls) while still getting some hats. Here is a proposal: Proposal: 5 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:42,521][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Alice have a strong preference for different items. You value hats and books highly, while Alice values balls highly. However, since the total items and their quantities are the same, and the per-item values are the same for each type of item, it is optimal to propose a fair distribution that maximizes the points based on your values. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:42,524][mllm.models.large_language_model_local][WARNING] - Response Given the current item values and the previous round's results, where Alice values books more highly, it makes sense to adjust the proposal to account for her stronger valuation of books. Here's a revised proposal that tries to secure more books for myself: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:42,560][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that you and Bob have different preferences for the items. You value books highly (10 points each) and balls moderately (1 point each), while Bob values hats and books more (10 points each for books and 1 point each for balls). Since both of you propose the same distribution every round, it suggests a symmetric game where the outcome is the same regardless of your proposal as long as the total is met. However, to maximize your points, you should take advantage of your higher value for books. Here is a revised proposal to maximize your points: Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:42,566][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Bob value balls the most at 10 each. However, you value hats and books less than Bob does. Proposaling to take 10 balls will ensure you capture the highest value items for you. Here is a more strategic proposal: Proposal: 0 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:42,569][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:42,572][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:42,722][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the fact that both Alice and Bob have the same expected per-item value, a cooperative strategy would likely yield the best results. Since both of us value balls the most and hats the least, proposing to take all the balls and splitting the hats and books might be a good approach. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:42,725][mllm.models.large_language_model_local][WARNING] - Response Given the values, it seems that both you and Bob have a preference for different items, but since the allocations are random, it might be wise to propose a balanced distribution to ensure you get a fair share of the items you value. Here's the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:42,806][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and the previous outcomes, it seems that both you and Alice have opposing preferences. You value hats and books highly, while Alice values hats and balls highly. To maximize your points, you should attempt to secure as many hats and books as possible while leaving Alice with the balls. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:42,888][mllm.models.large_language_model_local][WARNING] - Response Given the values, a strategic approach would be to prioritize items where you have a higher value. Since you value hats (10) and books (1) more than balls (1), and Bob values balls (10) much higher than hats and books, it makes sense to propose keeping as many hats and books as possible, and a small portion of balls if necessary to ensure the total does not exceed the available items. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:42,971][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:42,975][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:43,020][mllm.models.large_language_model_local][WARNING] - Response Given the values and the symmetry in the situation, it might be beneficial to propose a balanced distribution to ensure we get a fair share. Since both of us have the same per-item values for hats and balls, and books have a very low value for both of us, we can propose to take all the items to maximize our share. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:43,023][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:43,030][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:43,042][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:43,045][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:43,052][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:43,054][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:43,085][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:43,089][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:43,092][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:43,094][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:43,109][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:43,112][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:43,114][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:43,118][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:43,850][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:43,854][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:43,883][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:43,907][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:43,910][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:43,912][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:43,961][mllm.models.large_language_model_local][WARNING] - Response Given Bob's valuation, he values hats and books much more than balls, while you value hats and balls more than books. To maximize your points, it's important to propose a distribution that reflects your true valuations and that Bob is unlikely to fully accept in proportion. Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:43,964][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:43,967][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:43,969][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:43,983][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:43,986][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:43,988][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:44,003][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Bob place the highest value on balls (10). However, since both of you value hats and books relatively low (1), it might be a strategic advantage to try to secure as many balls as possible while still leaving some for Bob to ensure the allocation is fair and both of you get some points. Proposal: 10 hats, 10 books, 9 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:44,006][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:44,022][mllm.models.large_language_model_local][WARNING] - Response Given Alice's high value for hats and the lower but equal values for books and balls, it's important to secure as many high-value items as possible. However, we also need to consider Alice's valuation of hats to ensure she doesn't get all of them. A strategic proposal would be to maximize our points on balls while also taking a significant number of hats to counterbalance Alice's higher valuation for them. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:44,066][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Bob place a high value on different items. You value balls the most at 10, while Bob values hats the most at 10. The current optimal strategy would be to propose a split that aligns with your high value for balls while ensuring you don't overestimate the allocation of the items you both value less. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:44,070][mllm.models.large_language_model_local][WARNING] - Response Given Alice's high value for hats and books, and your high value for balls, a strategic proposal might involve splitting the items in a way that maximizes your score based on your individual values. Since both of you are likely to propose the same quantities, a balanced approach could still be effective. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:44,072][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and Bob's likely strategy, a good approach is to mirror Bob's proposals to ensure fair allocation and avoid leaving any balls, which have the highest value for you. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:44,157][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:44,160][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:44,163][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:44,165][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:44,167][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:44,209][mllm.models.large_language_model_local][WARNING] - Response Given the values, it appears that the optimal strategy in this round would be to focus on the items with the highest value for both you and Bob, and split the less valuable items proportionally. Here is the proposal: Proposal: 10 hats, 10 books, 10 balls This proposal keeps all items, which seems fair given the symmetry and equal per-item values for the less valuable items (hats and books) while splitting the more valuable items (balls) equally. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:44,212][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and equal values for hats and balls between you and Alice, and the high value of books for both of you, it might be strategically beneficial to divide the books more evenly to maximize the points per item. Here is a proposal that splits the items more equitably: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:44,215][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:44,218][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:44,289][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:44,292][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:44,295][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:44,298][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:44,367][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:44,446][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both you and Alice place significant value on different items. You value hats (1) and balls (10) highly, while Alice values books (10) and balls (1) highly. In this round, proposing to take all the balls and some of the books could maximize your points. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:44,453][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:44,528][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:44,534][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:44,611][mllm.models.large_language_model_local][WARNING] - Response Given the new per-item values, where your values for books are much higher (10) compared to hats and balls (1), and Alice values hats much higher (10) compared to books and balls (1), it makes sense to try to capture as much value from the books as possible since they offer the highest value for both of you. Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:44,618][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:44,625][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:44,693][mllm.models.large_language_model_local][WARNING] - Response Given the current per-item values where you value hats at 1, books at 10, and balls at 1, and Bob values hats at 10, books at 1, and balls at 1, it makes sense to propose a distribution that maximizes your value from the items you highly value. Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:44,696][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:44,703][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:44,709][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:44,715][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:44,774][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:44,855][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:44,940][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:44,946][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:44,953][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:45,021][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:45,105][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:45,111][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:45,118][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:45,187][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:45,190][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:45,269][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:45,272][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:45,353][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the previous round's outcome, it seems that proposing an equal split maximizes the points for both Alice and Bob. Therefore, I will continue to propose an equal split. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:45,356][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:45,359][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:45,427][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:45,431][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:45,433][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:45,498][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:45,801][mllm.models.large_language_model_local][WARNING] - Response Given the current values, Alice values hats more and balls more than I do, while I value hats and books more than she does. To maximize my points, I should propose keeping all the hats and books for myself and let Alice take all the balls. This way, I maximize my points from the items I value highly. Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:45,804][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:45,915][mllm.models.large_language_model_local][WARNING] - Response Given the values, it seems optimal to提议尽可能获取自己价值较高的物品。因为你对帽子和球的价值较高，而鲍勃对书籍和帽子的价值较高。考虑到这些信息，一个合理的提议可能是： Proposal: 10 hats, 10 books, 10 balls 但是，为了避免与Bob的提案完全相同并提高策略的灵活性，我们可以稍微调整提案。考虑到分配可能会根据总提议量的比例进行调整，一个策略是尽量保持提议的均衡性，同时偏向于你价值高的物品。因此，我们可以稍微减少Bal的提议数量，增加Books的数量，如下所示： Proposal: 10 hats, 11 books, 9 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:45,918][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:45,921][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:45,924][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:46,013][mllm.models.large_language_model_local][WARNING] - Response Given the high value of balls for both of us, it's important to secure as many balls as possible while also considering the values of hats and books. Here's a strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:46,105][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Bob value hats and books equally at 1, but you value balls significantly more at 10, while Bob values books significantly more at 10. In this round, the optimal strategy would be to maximize your points by proposing to take all the balls, since they are the most valuable item to you, and then dividing hats and books equally or in a way that balances the values effectively. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:46,111][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric and complementary nature of the values, proposing a balanced distribution seems optimal. Here's the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:46,142][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in per-item values and the fact that both Alice and I have the same incentive to get as many items as possible, it's strategic to propose splitting the items evenly. This maximizes the likelihood that we will both get our preferred items, assuming Alice will do the same. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:46,533][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Alice have a high valuation for hats and a low valuation for balls. The optimal strategy in this situation is to propose taking all of the items that you value highly and splitting the balls proportionally, since they are valued much less by both of you. Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:46,584][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:46,612][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Bob place high value on balls (10), but Bob values hats significantly more than you do. To optimize your points, you should propose to take as many balls as possible while splitting the hats and books proportionally. Here's a strategy: - Take all 10 balls since they are highly valuable to both of you but you value them the most. - Split the hats and books equally to avoid overproposing and losing out on items. Proposal: 10 balls, 5 hats, 5 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:46,658][mllm.models.large_language_model_local][WARNING] - Response Given the values, it is clear that you and Bob have very different preferences for the items. You value hats and books highly, while Bob values hats and balls highly. To maximize your points, you should focus on the items you value more. Here's a strategy: - Offer to take all 10 hats since they are your highest valued item. - Offer to take 10 books since they are your second most valued item. - Offer to take 0 balls since they are the least valued for you. This proposal should allow you to claim the items you value highly without overproposing on items that Bob values highly, which could lead to a proportional allocation that might not maximize your points. Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:46,661][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:46,663][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:46,703][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the previous rounds, proposing the same distribution as before seems optimal. Here's the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:46,710][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:46,771][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:46,774][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:46,776][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:46,779][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:46,854][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:46,858][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:46,860][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:46,863][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:46,866][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:46,904][mllm.models.large_language_model_local][WARNING] - Response Given the values, you have a significant advantage in valuing hats and balls much more than Alice does. To maximize your points, you should propose a distribution that reflects these values while also considering Alice's likely proposal. Here’s a strategic breakdown: - **Hats**: You value hats at 10 points each, and Alice values them at only 1 point each. You should propose to take as many hats as possible. - **Books**: Neither you nor Alice value books highly, with you valuing them at 1 point each and Alice also valuing them at 1 point each. You should split these as you see fit. - **Balls**: You value balls at 10 points each, and Alice values them at only 1 point each. You should propose to take as many balls as possible. Given this, you could propose the following: - 10 hats (full value for you, only 1 for Alice) - 10 books (neutral value) - 10 balls (full value for you, only 1 for Alice) So, your proposal would be: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:46,911][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the equal expected per-item values for both hats and books, and the significantly different values for balls, a strategic proposal would be to maximize the items with higher personal value. Here, books and hats have the same personal value and are both higher than the value of balls. Therefore, a balanced approach seems fair. proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:46,913][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:46,916][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:46,988][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:46,991][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:46,994][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:46,996][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:46,999][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:47,002][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:47,005][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:47,074][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:47,157][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:47,164][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:47,241][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:47,244][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:47,327][mllm.models.large_language_model_local][WARNING] - Response Given that my per-item values for hats and books are significantly higher than Bob's, and my value for balls is slightly higher, it would be strategically wise to propose a distribution that maximizes my points. Here's the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:47,464][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:47,467][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:47,470][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:47,474][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:47,476][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:47,486][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:47,489][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:47,578][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both you and Bob place the highest value on different items. You value hats the most (10), while Bob values books the most (10). Balls are valued similarly by both of you (1). To maximize your points, you should propose allocating items that you value more highly while still considering Bob's likely proposals. Here’s a strategic proposal: Proposal: 10 hats, 10 books, 10 balls This proposal keeps the distribution similar to previous rounds, ensuring that both you and Bob get a fair share of the items. This should lead to a fair split where both parties receive items they value, thus maximizing your points based on your values. So, the proposal is: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:47,581][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:47,584][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:47,587][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:47,590][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:47,656][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:47,660][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:47,662][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:47,735][mllm.models.large_language_model_local][WARNING] - Response Given the high value of hats for you and the high value of books for Bob, it's beneficial to try to secure as many hats as possible. However, we also need to account for the fact that books have a high value for Bob, so we should probably allocate some books to him to avoid him getting them all. Balls, being low-value for both of us, can be split proportionally. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:47,816][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:47,819][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:47,822][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:47,874][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:47,881][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:47,954][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the high value of balls for both of us, it's strategic to claim all of the balls for maximum points. For the hats and books, since they have a lower value for one of us, it's safer to propose a split that reflects our valuations while securing the items we value highly. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:47,957][mllm.models.large_language_model_local][WARNING] - Response Given the new values, where you have a higher valuation for hats and Bob has a higher valuation for balls, it's important to propose a distribution that maximizes your points. Here's the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:48,095][mllm.models.large_language_model_local][WARNING] - Response Given the high value of hats for both of us, it's crucial to secure as many hats as possible. However, we should also consider splitting the other items to ensure a balanced distribution. A balanced approach might be to propose slightly more hats and divide the books and balls fairly. Proposal: 7 hats, 6 books, 7 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:48,295][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric nature of the per-item values and the equal opportunity to propose, it seems that proposing fractions of items might be more efficient to ensure that both agents receive a fair share without exceeding the item quantities. Here's a strategic proposal that balances the items: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:48,360][mllm.models.large_language_model_local][WARNING] - Response Given the new values where your per-item values for hats and books are higher and Bob's values are higher for balls, a strategic approach might involve proposing a distribution that aligns with your higher values for hats and books, while still considering Bob's valuation for balls. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:48,508][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric nature of the per-item values and the fact that both Alice and I have the same values for hats and books, and the only difference is in the balls, a good strategy would be to propose an allocation that maximizes the value for both of us. Since both of us value hats and books the same and we both value balls significantly more than the other values, we can propose an equal split of all items to ensure we both get the maximum possible value. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:48,803][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it appears that both you and Alice have a significant preference for balls. However, you also have a preference for hats, and Alice values books highly. To maximize your points, you should consider keeping as many hats as possible to leverage your high value for hats, and also keep some balls to capture their high value. Here is a strategic proposal that balances these preferences: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:48,858][mllm.models.large_language_model_local][WARNING] - Response Given the current per-item values, where you highly value balls (10) and Alice highly values hats (10), it makes strategic sense to propose a distribution that takes these valuations into account. Proposing to keep all the balls and distributing hats and books more evenly can help maximize points. Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:48,865][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:48,868][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:48,871][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:48,874][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:48,913][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:48,920][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:48,923][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:48,926][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:48,968][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:48,971][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:48,974][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:48,977][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:49,047][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:49,077][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the fact that both you and Alice value hats and balls the same way but value books differently, a strategic proposal would be to claim as many of the books as possible while sharing the hats and balls equally. This way, you can maximize the value of the books, which are much more valuable to you. Proposal: 5 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:49,080][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:49,084][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:49,086][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:49,089][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:49,092][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:49,095][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:49,122][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, Alice values books the most (10) and balls the least (1), while you value books the most (10) and balls the least (1). This suggests a shift in the relative value of books and balls between the two rounds. To maximize your points, you should propose to take all the books and a significant portion of the hats and balls, as you value books highly and both hats and balls less, but still, you should aim to get a fair share of the hats and balls to ensure the total sum does not exceed the item quantity. Proposal: 10 hats, 10 books, 10 balls This ensures you get all the books and a fair share of the hats and balls, maximizing your points considering your per-item values. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:49,129][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the fact that both you and Alice value hats and books very similarly but have different preferences for balls, a strategic proposal would be to consider the proportional distribution of the items based on your and Alice's values. Since both of you have a high value for hats (10 for you and 1 for Alice), propose to keep as many hats as possible. For books, you value them more than Alice, and for balls, Alice values them more than you. A balanced and strategic proposal would be: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:49,132][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the high values that both you and Bob have for hats and books, it seems rational to propose an allocation that maximizes the points for both parties, as we have seen a balanced outcome in previous rounds. Here, we can propose an equal distribution of items to ensure both of us receive a fair share of items we value highly. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:49,138][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:49,141][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:49,144][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:49,146][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:49,170][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:49,173][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:49,176][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:49,179][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:49,215][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:49,219][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:49,258][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:49,264][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:49,267][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:49,302][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Bob value balls the least, hats moderately, and books the most. Since our values are inversely aligned, it's important to coordinate to maximize the points. If we both propose to take all the balls, we can each get the full value of 10 balls, which is 100 points each. This is a more optimal strategy than splitting other items since we can't both get the full value of hats or books. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:49,348][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:49,373][mllm.models.large_language_model_local][WARNING] - Response Given the high value of hats and books for you and the high value of balls for Bob, it's important to secure the hats and books while allowing Bob to take the balls. Here's a strategic proposal: Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:49,376][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:49,379][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:49,382][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:49,398][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:49,401][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:49,404][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:49,407][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:49,410][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:49,413][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:49,416][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:49,419][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:49,435][mllm.models.large_language_model_local][WARNING] - Response Given the new values where you have a high valuation for hats and Bob has a high valuation for books and balls, proposing the maximum amount for each item might still be a good strategy to secure your high-value items. Here's the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:49,438][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:49,441][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:49,444][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:49,446][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:49,454][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:49,457][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:49,459][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:49,500][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:49,711][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Bob place high value on balls (10) and low value on hats (1) and books (1). Since the values are symmetrical and both of you value balls the most, it might be strategic to consider a cooperative approach that splits the items evenly, ensuring that both of you get a fair share of the high-value items. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:49,736][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in per-item values and the equal distribution of items, a competitive and strategic proposal would be to offer a fair split that maximizes the utility for both parties while ensuring we get a good share. Here, we can propose a slightly more aggressive split to ensure we get a substantial share of the items that we value highly (hats and balls). Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:49,893][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the high value placed on the same items by both Alice and Bob, a cooperative approach might maximize the points for both parties. Since both of us value hats and books highly and balls less, proposing to split all items evenly seems to be a logical choice. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:49,987][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:50,054][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:50,057][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:50,142][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the high value Alice places on hats and books while you value balls highly, a strategic proposal would be to maximize the split based on your values while ensuring Alice receives some value in the balls she highly values. Here's a balanced proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:50,185][mllm.models.large_language_model_local][WARNING] - Response Given that both you and Bob have similar high values for hats and books, and low values for balls, it is strategic to ensure both of you get a fair share of the items to avoid any proportional splits that could reduce your points. Here's the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:50,213][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:50,239][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:50,242][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:50,245][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:50,248][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:50,267][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the previous outcomes, it appears that both you and Alice have similar strategies and equal bargaining power. To maximize your points, you should aim to secure the items that are most valuable to you while also considering Alice's valuation. Here's a strategic approach: - Value of hats: 10 (to you) vs 1 (to Alice) — Keep as many as possible. - Value of books: 1 (to you) vs 1 (to Alice) — Share these equally. - Value of balls: 1 (to you) vs 10 (to Alice) — Keep as few as possible to avoid giving Alice more points. Based on this, a reasonable proposal would be to keep the highest-valued items for yourself and split the medium-valued items: Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:50,270][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:50,273][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:50,276][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:50,278][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:50,294][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:50,297][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:50,300][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:50,303][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:50,305][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:50,322][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:50,325][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:50,328][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:50,331][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:50,333][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:50,377][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:50,399][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:50,402][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:50,405][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:50,407][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:50,429][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:50,432][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:50,435][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:50,438][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:50,441][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:50,443][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:50,446][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:50,449][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:50,462][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Alice have complementary valuations for the items. You value hats and books highly, while Alice values balls highly. To maximize your points, you should try to get as many hats and books as possible, while giving Alice most of the balls. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:50,468][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the high mutual values, it's optimal to propose a split that matches Alice's proposal to ensure we maximize our shares equally. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:50,484][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:50,509][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:50,512][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:50,515][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:50,532][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:50,535][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:50,538][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:50,540][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:50,543][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:50,546][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:50,548][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:50,550][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:50,553][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:50,556][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:50,558][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:50,565][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:50,567][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:50,570][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:50,572][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:50,575][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:50,640][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:50,643][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:50,645][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:50,651][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:50,654][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:50,686][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:50,706][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:50,727][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and the symmetry in the game, it's strategic to propose an allocation that maximizes the total points for both you and Bob. Since both you and Bob have high values for hats and balls, and books are valued similarly by both, the best strategy might be to propose a fair split that takes full advantage of the item quantities. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:50,730][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:50,746][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:50,753][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:50,759][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:50,771][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both "hats" and "balls" are highly valued by one of us more than the other, while "books" has moderate value for both. To maximize your points, you should focus on claiming the items that you value more than Alice does. Here's the reasoning: - You value hats (10) and balls (10) very highly, while Alice values books (10) more than you do. - You value books (1) relatively less, while Alice values hats (1) and balls (10) less than you do. Therefore, proposing to take all of "hats" and "balls" and a fair share of "books" would be strategic. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:50,774][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:50,780][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:50,786][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:50,792][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:50,798][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:50,808][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:50,813][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:50,818][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:50,821][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:50,826][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:50,835][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:50,840][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:50,845][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:50,850][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:50,858][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:50,860][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:50,874][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:50,897][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:50,900][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:50,902][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:50,919][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:50,922][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:50,924][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:50,927][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:50,944][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the values and the fact that both I and Bob value hats and books highly, and balls less, a cooperative strategy might yield the best results. Let's propose an allocation that maximizes the total value while considering the proportional splitting rule. Proposal: 10 hats, 10 books, 5 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:50,947][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:50,950][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:50,953][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:50,971][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:50,996][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:50,999][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:51,001][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:51,016][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:51,019][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:51,022][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:51,025][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:51,027][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:51,030][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:51,044][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:51,047][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:51,050][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:51,052][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:51,055][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:51,058][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:51,074][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:51,094][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:51,097][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:51,100][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:51,112][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:51,134][mllm.models.large_language_model_local][WARNING] - Response Given the high value Alice places on books and balls, and your own high value on hats and books, while still considering the proportional allocation, a balanced approach would be beneficial. Let's try to maximize our points by slightly adjusting the proposal to account for Alice's higher valuation on books and balls. Proposal: 10 hats, 5 books, 5 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:51,137][mllm.models.large_language_model_local][WARNING] - Response Given the new per-item values where hats and books have higher value for Alice, and balls have the same value for both, a strategic approach would be to propose a distribution that maximizes the value of high-value items for both of us, while still considering the proportional allocation rule. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:51,150][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:51,171][mllm.models.large_language_model_local][WARNING] - Response Given that both you and Alice have statistically equal per-item values, and considering the current round dynamics, a cooperative strategy could be beneficial. Since both of you have a similar valuation of hats and books, and a complementary valuation of balls, it might be wise to propose a fair split that maximizes the points for both parties. Proposal: 10 hats, 10 books, 10 balls This proposal ensures that both you and Alice receive the full quantity of each item, which might lead to a fairer outcome and potentially higher individual points in the long run. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:51,177][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the equal per-item values for hats and balls between you and Bob, and the high value of books for Bob, it makes sense to proposal a fair split that maximizes the points considering the values. Here's a strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:51,195][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:51,269][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:51,326][mllm.models.large_language_model_local][WARNING] - Response Given the values, it seems optimal to propose the distribution of all items to maximize the points based on the item values. Here is the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:51,330][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:51,333][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:51,490][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's beneficial to maximize the high-value items (books) while ensuring a fair split that Alice values. Here's a strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:51,493][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:51,499][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:51,506][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:51,542][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:51,549][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:51,556][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:51,562][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:51,574][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and equal expected values in this setup, and considering the goal is to maximize points, it makes sense to propose a fair split that maximizes utility for both parties. Since both your and Alice's valuations for hats and books are similar, and balls are valuable to both but in different ways, a balanced approach is key. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:51,577][mllm.models.large_language_model_local][WARNING] - Response Given the values, you have a strong preference for books and balls, while Bob values hats more. To maximize your points, you should propose to take all of the items that you value higher. Here's the proposal: Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:51,579][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:51,585][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:51,591][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:51,598][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:51,600][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:51,618][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:51,624][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:51,630][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:51,647][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:51,649][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:51,655][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:51,661][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:51,667][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:51,672][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:51,681][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:51,686][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:51,691][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:51,696][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:51,701][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:51,709][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:51,732][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:51,737][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:51,790][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the high value of books and balls for both of us, it's beneficial to propose a split that maximizes the value of these items. Here is a strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:51,797][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:51,800][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:51,816][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:51,843][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:51,869][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:51,872][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:51,892][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:51,895][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:51,898][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:51,918][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:51,921][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:51,924][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:51,951][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:51,953][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:51,955][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:51,957][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:51,971][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both hats and balls are more valuable to you compared to Bob, while books are more valuable to Bob. To maximize your points, you should propose to take all the hats and balls, and leave the books for Bob. This way, you can get the full value from hats and balls, which are your most valuable items. Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:51,973][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:51,976][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:52,020][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:52,080][mllm.models.large_language_model_local][WARNING] - Response Given the values, it seems that both hats and books are less valuable to both of us compared to balls. To maximize your points, you should propose to keep as many balls as possible while dividing the less valuable items (hats and books) proportionally. Since hats and books are equally valuable to both of us and balls are highly valuable to both of us, it makes sense to keep as many balls as possible to increase your points. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:52,087][mllm.models.large_language_model_local][WARNING] - Response Given the high value that both you and Alice place on balls (10 each) and the relatively lower values for hats and books (10 for you and 1 for you, respectively, with 10 and 1 for Alice), the optimal strategy would be to coordinate to maximize the allocation of balls while ensuring a fair split for the other items to prevent both getting less than their fair share. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:52,090][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Bob place high value on hats and low value on books. Balls seem to be less valuable for Bob compared to you. To maximize points, you should try to secure as many hats and balls as possible since these items have higher individual values for you. Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:52,128][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:52,131][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:52,171][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:52,274][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:52,326][mllm.models.large_language_model_local][WARNING] - Response Given that both you and Bob have a higher value for hats compared to the other items, and knowing that proposing the entire quantity of an item will lead to a proportional split if the other party also proposes the full quantity, a strategic approach would be to ensure that we control the distribution of hats while dividing the books and balls evenly. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:52,374][mllm.models.large_language_model_local][WARNING] - Response Given Alice's higher value for books and balls, and your higher value for hats, it's important to secure as many hats as possible while also considering the value of books and balls. Here's a strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:52,450][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:52,457][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:52,535][mllm.models.large_language_model_local][WARNING] - Response Given that my value for hats and books is 1, while Alice values them at 10 and 1 respectively, and both balls are valued at 1 by both of us, a strategic proposal would be to ensure I get as many balls as possible since they have the highest value for me. Here's a balanced proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:52,538][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric and equal value distribution where both you and Bob have high values for the same items, proposing to take all items seems like the most balanced and optimal strategy. Here is the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:52,545][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:52,559][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:52,562][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:52,565][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:52,647][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:52,650][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:52,652][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:52,735][mllm.models.large_language_model_local][WARNING] - Response Given the pattern in Bob's strategy and the values of the items, it seems that Bob is trying to maximize his points by taking the items he values highly, which are hats and books. Since you value balls the most, and hats and books are what Bob values the most, you should try to secure as many balls as possible while also taking some hats and books to balance your total points. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:52,739][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Bob place high value on hats and books, and low value on balls. To maximize your earnings, you should propose a split that reflects this asymmetry while ensuring the total does not exceed the available items. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:52,741][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:52,744][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:52,748][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:52,750][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:52,753][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:52,756][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:52,758][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:52,828][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:52,831][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:52,834][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:52,836][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:52,919][mllm.models.large_language_model_local][WARNING] - Response Given the values, you have a significant value for hats and balls, while Bob values books the most. To maximize your points, it's crucial to ensure you get a good share of the hats and balls. Here’s a balanced proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:52,922][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:52,928][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:52,932][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:53,002][mllm.models.large_language_model_local][WARNING] - Response Given the values and Bob's likely strategy, proposing a balanced distribution that considers both your high value for balls and Bob's high value for books and balls would be wise. Here's a proposal that aims to maximize your points by securing the items you value highly while still making a reasonable offer: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:53,093][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both you and Alice have a strong preference for books and balls. However, to ensure fairness and to maximize points, we should propose equally dividing the items to avoid any potential allegations of not participating fairly. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:53,100][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:53,173][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:53,178][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:53,179][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:53,202][mllm.models.large_language_model_local][WARNING] - Response Given the new distribution of per-item values where hats are valued equally by both parties but books and balls have significantly different valuations, a reasonable strategy would be to propose an allocation that maximizes the value based on the perceived worth to Alice. Here’s a proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:53,205][mllm.models.large_language_model_local][WARNING] - Response Given that both sets of values are symmetrical for the items, proposing an equal share of each item seems to be a fair and strategic approach to maximize the points, considering the value distributions. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:53,207][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:53,209][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:53,214][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:53,218][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:53,221][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:53,243][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:53,287][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:53,331][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:53,334][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:53,337][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:53,355][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:53,377][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:53,416][mllm.models.large_language_model_local][WARNING] - Response Given the current situation where both you and Bob have high values for hats and books, and low values for balls, the optimal strategy would be to ensure you both get the items with high value to you. Let's propose to take all the items with high value to us and split the balls equally. Proposal: 10 hats, 10 books, 5 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:53,437][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:53,631][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the fact that your per-item values for hats and books are very low compared to Bob's, and your value for balls is much higher, a strategic proposal would be to maximize the items with high value to you. However, since both you and Bob value hats and books relatively similarly and we want to ensure we get the maximum points from balls without overestimating and risking proportional distribution if both propose maximum balls. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:53,672][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Alice place significant value on different items. You highly value hats and books, while Alice values hats and balls significantly more. Splitting the items evenly might not be optimal due to the differing valuations. A more strategic approach would be to propose a split that aligns with these valuations. Proposal: 10 hats, 10 books, 10 balls However, to be more strategic, consider proposing a split that acknowledges the differential values. Since you value books and hats more than Alice does, and she values balls more, proposing a split that gives you a slight edge in items with high personal value could be beneficial. Proposal: 10 hats, 10 books, 10 balls This strategy assumes that proposing to take all items might still be close to the limit, given the proportional distribution rule. Given the symmetrical nature of the proposal and the differential values, a more nuanced split might be needed to outsmart Alice's likely response. Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:53,676][mllm.models.large_language_model_local][WARNING] - Response Given the values and the symmetry in the situation, it's beneficial to propose a split that maximizes the value for both hats and books, which have higher individual values for both Alice and Bob. Here's a proposal that balances the items: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:53,757][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the values and knowing that Alice values hats and balls more than books while I value books and balls more than hats, a cooperative strategy might be optimal. However, to ensure we maximize our points, we should propose an allocation that takes into account both our and Alice's values. Here's a strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:53,760][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:53,763][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:53,765][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:53,772][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:53,778][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:53,780][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:53,783][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:53,785][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:53,790][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:53,796][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:53,802][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:53,807][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:53,810][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:53,816][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:53,822][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:53,828][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:53,834][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:53,836][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:53,951][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:53,953][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:54,047][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Bob value the books the most, and both of you have a similar valuation for hats and balls. To maximize your points, you should propose a distribution that reflects the relative values of the items. Here’s a strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:54,054][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:54,061][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:54,067][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:54,071][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:54,074][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:54,076][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:54,079][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:54,135][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:54,138][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:54,227][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the understanding that the items will be split proportionally, a strategic approach would be to mirror Alice's proposals to ensure fair outcomes. Here's the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:54,231][mllm.models.large_language_model_local][WARNING] - Response Given the values, it seems optimal to propose a split that maximizes the points based on our higher value for balls. Here's a strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:54,237][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:54,320][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:54,323][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:54,327][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:54,422][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:54,425][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:54,428][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:54,531][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:54,534][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:54,536][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:54,539][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:54,580][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:54,605][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:54,609][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:54,631][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:54,656][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:54,679][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:54,703][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:54,707][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:54,801][mllm.models.large_language_model_local][WARNING] - Response Given the values, it seems that both you and Bob have a high valuation for different items, specifically hats and books. To maximize your points, you should align your proposal to maximize your value. Since you value hats and books highly and Bob values them as well, a strategic proposal would be to take as much of what you value. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:54,966][mllm.models.large_language_model_local][WARNING] - Response Given Bob's values, he will prioritize hats and balls, while I prioritize books. To maximize my points, I should propose a distribution that aligns with my high value for books and the fact that my total value (21) is higher than Bob's in this round. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:54,975][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:54,978][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:54,981][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:54,984][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:55,035][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:55,038][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:55,044][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:55,050][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:55,054][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:55,060][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:55,065][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:55,071][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:55,074][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:55,076][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:55,079][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:55,081][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:55,087][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:55,090][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:55,096][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:55,102][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:55,107][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:55,112][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:55,139][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:55,141][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:55,145][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:55,238][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:55,241][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:55,414][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:55,418][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:55,424][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:55,427][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:55,562][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:55,589][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:55,592][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:55,614][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:55,655][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:55,695][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:55,698][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:55,701][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:55,748][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:55,750][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:55,791][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:55,793][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:55,796][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:55,800][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:55,846][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:55,849][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:55,852][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:55,870][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:55,873][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:55,943][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:55,946][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:55,949][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:55,952][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:55,994][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:55,998][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:56,037][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both you and Bob value hats the least, followed by books, and then balls the most. Here's a strategic proposal to maximize your points: - **Hats (10):** Since both of you value hats the least, it might be better to take just enough to avoid disputes but not too much. - **Books (1):** You value books significantly more than Bob does, so you should take as many as possible. - **Balls (10):** Both of you value balls equally, so splitting them equally will be fair. Proposal: 5 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:56,044][mllm.models.large_language_model_local][WARNING] - Response Given the significant differences in our per-item values and Alice's, it makes sense to focus on the items we value highly. Since I value hats and balls more than books, and Alice values books and balls more than hats, it's beneficial to maximize the items we both value. Here is a strategic proposal: Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:56,097][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:56,100][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:56,102][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:56,105][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:56,107][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:56,134][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:56,137][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:56,140][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:56,142][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:56,143][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:56,144][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:56,147][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:56,149][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:56,151][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:56,153][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:56,221][mllm.models.large_language_model_local][WARNING] - Response Given our per-item values and Bob's per-item values, it makes strategic sense to claim the items that we value more while also considering Bob's valuations to ensure an equitable split that maximizes our points. - You value hats at 1, books at 10, and balls at 10. - Bob values hats at 1, books at 10, and balls at 1. To maximize your points, you should propose to take the books and balls, as these are highly valued by both of you, and share the hats more evenly or slightly in your favor. Proposal: 5 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:56,597][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:56,599][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:56,651][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:56,654][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:56,657][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:56,660][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:56,702][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, the optimal strategy would involve recognizing that Alice values hats and balls much more than books, and you value books much more than hats and balls. Distributing the items equally might not be optimal due to these differing valuations. Here is a revised proposal: Proposal: 10 hats, 10 books, 10 balls However, to better exploit our valuations, we might want to propose slightly differently to maximize gains. Since you value books highly and Alice values hats and balls highly, it might be strategic to slightly reduce the number of balls you propose and increase the number of books. Proposal: 10 hats, 11 books, 9 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:56,707][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the fact that both Alice and I have the same valuation for hats and balls, and books, it's optimal to propose a fair split to maximize the total points earned from each item. The optimal strategy in this case is to propose the same split as Alice to ensure that neither of us gets a lesser share. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:56,711][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:56,714][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:56,733][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:56,737][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:56,760][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:56,820][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:56,823][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:56,827][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:56,830][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:56,852][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:56,860][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:56,864][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:56,867][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:56,871][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:56,932][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:56,940][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:56,947][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:56,954][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:56,957][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:56,960][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:56,962][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:56,964][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:56,965][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:56,966][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:56,969][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:56,971][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:56,973][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:56,977][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:56,979][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:56,982][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:57,024][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:57,026][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:57,027][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:57,029][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:57,031][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:57,034][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:57,110][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric and complementary nature of the per-item values, proposing a fair split is still the best strategy. Here, you value hats and books higher and balls lower, while Bob values balls and books higher and hats lower. A symmetric proposal will ensure that you both receive fair shares. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:57,192][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:57,196][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:57,273][mllm.models.large_language_model_local][WARNING] - Response Given the values, you have a strong preference for hats and slightly less for books and balls. Bob, on the other hand, values books and balls more than hats. To maximize your points, it's important to propose a distribution that leverages your higher value for hats. Proposal: 10 hats, 10 books, 10 balls This distribution assumes that Bob will either accept or follow suit, given the symmetry and similar values for books and balls between you two. However, to be more strategic, you might want to account for Bob's values and propose slightly less of the items he values more. Proposal: 10 hats, 10 books, 9 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:57,437][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:57,441][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:57,519][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both you and Alice place high value on balls. To maximize your points, you should propose keeping more balls to capture as much value as possible from them. Here is a strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:57,605][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:57,609][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:57,611][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:57,615][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:57,692][mllm.models.large_language_model_local][WARNING] - Response Given that both you and Alice value hats and books highly and balls less, but since your values for hats and books are the same and Alice's values are the same, a cooperative approach might lead to better outcomes in terms of total points. We can propose to split the items in a way that both of us receive a fair share, maximizing our individual values. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:57,700][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:57,703][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:57,706][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:57,775][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:57,776][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:57,780][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:57,783][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:57,874][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:57,882][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:57,890][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:57,897][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:57,908][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:57,915][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:57,918][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:57,925][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:57,935][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:57,938][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:57,965][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:57,972][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:58,303][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:58,335][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:58,339][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:58,341][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:58,343][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:58,346][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:58,348][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:58,351][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:58,353][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:58,356][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:58,359][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:58,362][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:58,365][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:58,367][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:58,370][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:58,372][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:58,375][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:58,377][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:58,401][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:58,404][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:58,548][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:58,553][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:58,692][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:58,696][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:58,711][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:58,714][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:58,738][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the high values for hats and books, it's strategically beneficial to maximize the items with higher individual values. Here, hats and books are valued significantly higher by both you and Bob, while balls are valued lower by both but slightly higher by you. To maximize points, it's wise to propose taking most of the hats and books, and a fair share of the balls. Given that you and Bob value the same items similarly, a competitive yet cooperative approach would be to propose an allocation that evenly distributes the items to prevent Bob from taking everything. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:58,742][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:58,745][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:58,783][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:58,804][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:58,808][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:58,826][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it makes sense to prioritize the items you value the most, which are hats and balls. Books are valued the same by both parties, so they can be divided equally. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:58,848][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:58,866][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:58,906][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:58,910][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:58,913][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:58,914][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:58,917][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:58,920][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:58,934][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:58,937][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:58,939][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:58,942][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:58,944][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:58,948][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:58,953][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:58,958][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:58,963][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:58,968][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:58,972][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:58,975][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:58,977][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:58,980][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:58,983][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:58,987][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:58,991][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:58,995][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:58,996][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:58,998][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:59,000][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:59,002][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:59,003][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:59,005][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:59,007][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:59,009][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:59,015][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:59,017][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:59,019][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:59,020][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:59,055][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the fact that both you and Bob value the items similarly, the best strategy is to propose a fair split that maximizes the points for both of you. Here, you both value balls the most and hats the least. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:59,057][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:59,090][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:59,092][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:59,179][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:59,199][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:59,202][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:59,226][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:59,229][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:59,263][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:59,279][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:59,283][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:59,298][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:59,316][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:59,367][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:59,370][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:59,373][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:59,375][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:59,388][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:59,391][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:59,394][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:02:59,412][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's beneficial to propose a distribution that takes advantage of the high values for books and balls while still accounting for the value of hats. Here's a more strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:02:59,419][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:59,422][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:59,424][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:59,427][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:59,454][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:59,455][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:59,456][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:59,457][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:59,458][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:59,460][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:59,462][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:02:59,463][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:00,217][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:00,219][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:00,221][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:03:00,224][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:03:00,320][mllm.models.large_language_model_local][WARNING] - Response Given the high value of books for both of us, it's important to secure as many books as possible. Let's assume that Bob will try to grab all the balls, given their high value for him. To maximize our points, we should propose to take all the books and split the hats and balls evenly. Proposal: 5 hats, 10 books, 5 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:03:00,324][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric and equal distribution of values and the fact that proposing the full allocation of items will likely result in a proportional split, I will propose the following distribution to maintain a fair and competitive stance: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:00,327][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:00,378][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:00,402][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:00,409][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:00,420][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:00,426][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:00,459][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:00,478][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:00,483][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:00,498][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:00,523][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:00,543][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the values and the equal distribution of items, proposing a proportional split seems合理. Let's propose: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:00,582][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in per-item values between you and Alice, and the fact that proposing equal shares leads to equal earnings, a reasonable strategy is to propose a fair split. This will ensure that we maximize our points without risking proportional allocation due to excess claims. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:00,590][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:00,596][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:00,603][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:00,609][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:00,618][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:00,623][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:00,639][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:03:00,641][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:00,644][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:00,648][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:00,655][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:00,659][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:00,663][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:03:00,665][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:03:00,667][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:03:00,669][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:03:00,672][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:03:00,673][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:03:00,675][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:03:00,676][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:03:00,678][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:03:00,681][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:03:00,683][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:03:00,685][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:03:00,686][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:03:00,688][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:00,690][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:00,692][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:00,694][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:00,697][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:00,699][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:00,702][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:00,704][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:00,730][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric values and the fact that both you and Alice have similar high values for different items, it's beneficial to propose an equal split to maximize the points for both items where you have high value and where Alice has high value. A reasonable proposal would be to split all items equally. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:00,732][mllm.models.large_language_model_local][WARNING] - Response Given that both your per-item values and Bob's per-item values are symmetric for hats and balls, and asymmetric for books, a fair and strategic proposal would be to split the items evenly to ensure you maximize your points based on your values. Here’s the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:00,733][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:00,737][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:00,740][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:00,741][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:00,770][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:00,818][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's important to consider the high value of books and balls for both parties. A strategic proposal would be to maximize the value from books and balls while ensuring the total quantity is not exceeded. Here's a proposal that balances the high-value items: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:03:00,854][mllm.models.large_language_model_local][WARNING] - Response Given the high value of books for both of us and the equal value for balls, we should focus on splitting the books and balls in a way that maximizes our points while ensuring we get a fair share. Since we both value books and balls equally, it might be beneficial to cooperate and split them evenly. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:01,013][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:01,097][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the equal allocation in previous rounds, a strategic proposal would be to mirror Bob's values to maximize the points. Therefore, the proposal should consider the high value Bob places on books and balls and the high value you place on hats and balls. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:03:01,101][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:03:01,120][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:01,123][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:01,131][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:01,134][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:01,137][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:01,140][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:01,143][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:01,146][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:01,149][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:01,151][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:01,153][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:01,155][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:01,156][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:01,157][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:01,158][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:01,173][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:01,175][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:01,177][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:01,180][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:01,182][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:01,332][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:03:01,350][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:03:01,353][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:03:01,403][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:03:01,405][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:03:01,421][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's beneficial to propose a distribution that takes into account the high value of balls for Alice and the high value of hats for you. A balanced proposal that considers both our preferences and Alice's might be: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:01,424][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:03:01,487][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:03:01,518][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:01,535][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:03:01,538][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:03:01,541][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:01,553][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:01,560][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:01,565][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:01,571][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:01,577][mllm.models.large_language_model_local][WARNING] - Response Proposal: 1 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:01,588][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:01,593][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:01,599][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:01,605][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:01,610][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:01,616][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:01,621][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:01,626][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:03:01,628][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:03:01,630][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:03:01,634][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:03:01,636][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:03:01,638][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:03:01,641][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:03:01,644][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:03:01,690][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:03:01,692][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:03:01,738][mllm.models.large_language_model_local][WARNING] - Response Given the values, it seems both you and Bob have a strong preference for books and balls, and slightly less for hats. A cooperative approach might yield higher points since splitting items proportionally could result in a loss of points if one agent overvalues an item. However, splitting everything equally might still be a good strategy since you both value books and balls highly and hats less. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:01,741][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:01,744][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:01,773][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:01,777][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:01,791][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Bob value the balls and hats relatively higher than the books. To maximize your points, you should propose to take the items that you value more. Here's the proposal: Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:01,798][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:01,805][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:01,813][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in per-item values and the previous rounds' outcomes, where proposing all items results in an equal split due to the random assignment, I will continue to propose an equal distribution to maximize the points based on my per-item values. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:03:01,894][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:01,914][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:01,917][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:01,995][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:01,997][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:01,999][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:02,001][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:02,003][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:02,004][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:02,005][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:02,013][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:03:02,015][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:03:02,016][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:03:02,018][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:03:02,038][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:02,055][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:02,058][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:02,084][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the situation where both you and Bob have high values for different items, a cooperative approach might be optimal to maximize points. Here, we could consider splitting the items evenly to cover all types. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:02,086][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:02,088][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:02,098][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the high value that both you and Alice place on balls, it's important to consider the potential for misallocation if we both propose to take all the balls. However, since the values for hats and books are relatively close, a balanced approach might be more strategic. Proposal: 10 hats, 10 books, 10 balls This proposal will ensure that we both receive shares of all items, which could be more stable and avoid any potential overvaluation of balls. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:02,114][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:02,116][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:02,201][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:02,207][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:02,249][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:03:02,261][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:03:02,265][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:03:02,278][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:02,284][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:02,290][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:02,295][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:02,300][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:02,302][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:02,309][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:02,313][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:03:02,315][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:02,318][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:02,320][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:02,322][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:02,325][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:02,366][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:02,403][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:02,409][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:03:02,412][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:03:02,478][mllm.models.large_language_model_local][WARNING] - Response Given our per-item values and Bob's per-item values, we should focus on the items where we have the higher values. Here, both of us value books and balls relatively highly. To maximize our points, we should try to secure as many books and balls as possible while also considering hats, which are valued more by Bob. Proposal: 10 hats, 10 books, 10 balls This proposal ensures that we are not leaving any high-value items (books and balls) for Bob, and we are also taking a significant portion of hats. However, let's consider a more strategic proposal that might optimize our points further: Proposal: 10 hats, 10 books, 10 balls Since this is a balanced proposal and both of us are likely to propose similarly, let's try to slightly outmaneuver Bob by proposing just one more item in our favor. Proposal: 10 hats, 11 books, 9 balls This way, we take the maximum number of books and balls while slightly more than Bob's proposal for hats. Let's submit this proposal. Proposal: 10 hats, 11 books, 9 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:03:02,494][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric nature of the per-item values and the predictability of Alice's strategy based on her high values for hats and balls and low value for books, it might be beneficial to try a different approach to maximize your points. Here, you might want to slightly adjust your proposal to try and grab more of the items that have higher values for you, while still ensuring the total sum of items proposed does not exceed the available items. Proposal: 10 hats, 5 books, 5 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:02,573][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the fact that both you and Bob have the same proposed strategy, it's rational to stick with a proposal that maximizes the points for your per-item values. Here, splitting the items equally would likely result in a fair outcome. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:02,628][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:02,643][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:02,646][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:02,689][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:02,692][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:02,699][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:02,706][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:02,713][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:02,737][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the fact that both Alice and Bob have similar strategies of proposing all items in every round, it's optimal to continue this approach. Here is the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:03:02,740][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:03:02,754][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:03:02,757][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:03:02,791][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:02,794][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:02,798][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:03:02,809][mllm.models.large_language_model_local][WARNING] - Response Given the high value of balls for both of us and the symmetric nature of the values, a cooperative approach would likely yield the highest points. Here's a proposal that maximizes the value of the balls while ensuring we both get a fair share of hats and books: Proposal: 5 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:02,910][mllm.models.large_language_model_local][WARNING] - Response Given that both Alice and Bob value hats and books highly, and balls minimally, a strategic proposal would be to try to maximize the value of items that are highly valued by both parties. Here's a proposal that accounts for this: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:02,918][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:03:02,922][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:03:02,936][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:02,950][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:02,957][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:02,995][mllm.models.large_language_model_local][WARNING] - Response Given the values, you have a strong preference for hats and balls, while Bob values books more. To maximize your points, you should propose to take more of the items you value higher. Here’s a proposal that takes advantage of your higher values for hats and balls: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:02,999][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:03,029][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the values of the items, it's strategic to maximize the items with the highest value for you (which are books and balls). Here's a proposal that balances the distribution: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:03:03,045][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:03:03,048][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:03,051][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:03,061][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:03,142][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's strategic to propose an allocation that maximizes the points based on the values. Since my value for hats and books is higher than for balls, and Bob values books and balls more, proposing the full allocation of 10 hats, 10 books, and 10 balls ensures that both my and Bob's full allocation is accepted without exceeding the total items available. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:03,146][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:03,150][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:03,154][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:03,206][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:03,285][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:03:03,316][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:03:03,317][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:03:03,422][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:03,425][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:03,503][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:03,516][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:03:03,519][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:03:03,531][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's beneficial to split the items in a way that maximizes the value for both hats and balls, which have higher values for me, and books, which have higher values for Alice. A strategic proposal would be to take the majority of items with high value for me and a portion that prevents Alice from getting too much of her high-value items. Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:03,534][mllm.models.large_language_model_local][WARNING] - Response Given the high value of books and balls for you, and the high value of hats for Bob, a strategic approach would be to propose an allocation that maximizes your value from books and balls while also ensuring you get some hats to avoid a situation where Bob might outbid you for all the hats. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:03:03,536][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:03,538][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:03:03,907][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:03:03,920][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:03:03,933][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and the symmetric nature of the problem, a cooperative strategy seems beneficial. Since we both value books highly, it makes sense to allocate books to both of us. We can keep the distribution of hats and balls similar to balance the overall value for both of us. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:03:03,937][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:03:03,957][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:03:03,960][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:03:04,065][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the fact that both you and Bob have the same total value for all items, the optimal strategy is to split the items evenly to maximize both your and Bob's earnings. Therefore, the proposal would be: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:04,068][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:03:04,077][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:03:04,080][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:03:04,084][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:03:04,105][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:03:04,108][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:03:04,117][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:04,129][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:04,140][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:04,144][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:04,164][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:04,167][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:04,176][mllm.models.large_language_model_local][WARNING] - Response Given the values, both you and Bob value books and balls highly, but hats are valued less by both. To maximize your points, you should try to get as many of the highly valued items (books and balls) as possible while still proposing a reasonable amount of hats. Here's a balanced proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:03:04,272][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:04,280][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:04,284][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:04,287][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:04,298][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:04,311][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:04,313][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:04,323][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:04,334][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:04,347][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:04,353][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:04,372][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:04,379][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:04,387][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:04,513][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:04,520][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:04,527][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:04,544][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:04,551][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:04,557][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:04,664][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:04,812][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:03:04,816][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:03:04,884][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:03:05,031][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:03:05,043][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:03:05,046][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:05,050][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:05,055][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:03:05,069][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:03:05,102][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:05,124][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:03:05,136][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:03:05,147][mllm.models.large_language_model_local][WARNING] - Response Given the high value of hats and balls for you and the high value of books for Bob, it's important to maximize your points by getting as much value from the items with high personal value. Here's the strategy: - You should take all the hats and balls since they have high value for you. - You should take as many books as possible since they have a high value for Bob but a low value for you. A proposed allocation would be: - 10 hats (full value for you) - 10 books (to minimize Bob's advantage) - 10 balls (full value for you) Thus, the proposal is: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:03:05,229][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:05,243][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:05,246][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:05,254][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:05,260][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:05,267][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:05,303][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:05,337][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:05,349][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:05,437][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:05,449][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:05,462][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:05,475][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:05,545][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:05,557][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:05,698][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the situation and the high value of books and balls for both of us, it makes sense to propose an equitable distribution. Here's the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:05,877][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:05,938][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:03:05,942][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:03:06,118][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:06,121][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:06,288][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:06,299][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:06,653][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:03:06,825][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:07,000][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:07,139][mllm.models.large_language_model_local][WARNING] - Response Given Alice's valuation for books and balls, and your own high valuation for hats and books, it might be beneficial to adjust the proposal to ensure that both of you can secure the items you value the most. Here's a more strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:03:07,324][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:03:07,499][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:03:08,616][__main__][INFO] - Number of regex retries in iteration 570: 2262 [2025-11-13 07:03:08,617][__main__][INFO] - agents played in iteration 570 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 07:03:09,542][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 07:03:09,563][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 07:03:09,584][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 07:03:09,605][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 07:03:09,606][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 07:03:09,606][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 07:03:10,430][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 07:03:10,886][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 07:03:11,392][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 07:03:11,891][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 07:03:12,388][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 07:03:12,894][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 07:03:13,393][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 07:03:13,897][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 07:03:14,396][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 07:03:14,897][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 07:03:15,400][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 07:03:15,898][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 07:03:16,394][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 07:03:16,894][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 07:03:17,390][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 07:03:17,891][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 07:03:18,387][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 07:03:18,883][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 07:03:19,377][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 07:03:19,875][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 07:03:20,371][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 07:03:20,868][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 07:03:21,368][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 07:03:21,882][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 07:03:22,382][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 07:03:22,877][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 07:03:23,375][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 07:03:23,873][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 07:03:24,379][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 07:03:24,876][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 07:03:25,370][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 07:03:25,868][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 07:03:26,367][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 07:03:26,863][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 07:03:27,358][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 07:03:27,854][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 07:03:28,375][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 07:03:28,868][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 07:03:29,358][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 07:03:29,854][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 07:03:30,346][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 07:03:30,840][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 07:03:31,332][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 07:03:31,828][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 07:03:32,322][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 07:03:32,812][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 07:03:33,305][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 07:03:33,804][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 07:03:34,302][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 07:03:34,801][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 07:03:35,300][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 07:03:35,798][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 07:03:36,299][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 07:03:36,797][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 07:03:37,294][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 07:03:37,797][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 07:03:38,294][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 07:03:38,792][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 07:03:39,300][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 07:03:39,797][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 07:03:40,294][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 07:03:40,787][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 07:03:41,282][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 07:03:41,788][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 07:03:42,284][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10889 tokens. [2025-11-13 07:03:43,146][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.18%, Current % of VRAM taken: 59.64%, Block Peak % of device VRAM: 62.24%, ΔTime: 00:00:32 [2025-11-13 07:03:43,895][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 07:03:43,896][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 07:03:43,898][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 07:03:45,804][__main__][INFO] - Iteration 571 took 1m 36s (61.65% Gen, 36.38% Train). Generation: 59s, Training: 35s. Estimated remaining time: 71h 54m 40s. Estimated total time: 80h 48m 44s. Time estimates for 10 more iterations: 16m 9s, 100 more iterations: 2h 41m 37s, 500 more iterations: 13h 28m 7s. [2025-11-13 07:03:45,807][__main__][INFO] - Starting iteration 571. [2025-11-13 07:03:46,291][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 57 and human policies 1. [2025-11-13 07:03:46,292][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 07:04:10,057][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,062][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,079][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,082][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,085][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,088][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,090][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,093][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,105][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,107][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,110][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,113][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,115][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,118][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,120][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,122][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,124][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,127][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,129][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,130][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,133][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,134][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,135][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,137][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,138][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,140][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,142][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,143][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,144][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,145][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,147][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,149][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,151][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,152][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,154][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,155][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,156][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,158][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,160][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,161][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,163][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,165][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,166][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,173][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,176][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,178][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,180][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,181][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,183][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,184][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,185][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,186][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,188][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,189][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,189][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,190][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,190][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,191][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,193][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,195][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,196][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,198][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,199][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,200][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,202][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,204][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,206][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,208][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,209][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,210][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,212][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,213][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,214][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,216][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,218][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,219][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,220][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,222][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,223][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,224][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,226][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,227][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,228][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,229][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,230][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,231][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,232][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,233][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,233][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,234][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,234][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,235][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,237][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,238][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,239][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,240][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,242][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,243][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,244][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,246][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,247][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,248][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,249][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:10,380][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,383][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,399][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,402][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,405][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,408][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,410][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,417][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,419][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,422][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,425][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,428][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,430][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,437][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,440][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,442][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,444][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,447][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,449][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,451][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,453][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,455][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,457][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,459][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,461][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,463][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,465][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,466][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,467][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,469][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,471][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,473][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,475][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,476][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,477][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,479][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,481][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,482][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,486][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,488][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,489][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,491][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,493][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,494][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,500][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,503][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,505][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,508][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,510][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,512][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,513][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,515][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,520][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,522][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,523][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,525][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,526][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,527][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,528][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,529][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,530][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,532][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,534][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,536][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,538][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,543][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,544][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,546][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,547][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,548][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,549][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,550][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,552][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,553][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,555][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,556][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,558][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,561][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,563][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,565][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,566][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,567][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,569][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,572][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,573][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,574][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,576][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,578][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,580][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,581][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,582][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,584][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,585][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,586][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,588][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,589][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,595][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,597][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,598][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,600][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,601][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,602][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,604][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:10,701][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,735][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,750][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,755][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,760][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,764][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,767][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,770][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,775][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,778][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,789][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,792][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,795][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,798][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,802][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,806][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,809][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,812][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,815][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,818][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,822][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,825][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,828][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,831][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,834][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,837][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,839][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,842][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,850][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,853][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,855][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,858][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,860][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,863][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,866][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,868][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,870][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,872][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,874][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,876][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,878][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,880][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,882][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,884][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,886][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,888][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,889][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,891][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,894][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,895][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,897][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,900][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,902][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,903][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,908][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,910][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,912][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,915][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,917][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,919][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,921][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,923][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,925][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,926][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,928][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,930][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,932][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,934][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,935][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,938][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,940][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,942][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,944][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,946][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,949][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,951][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,953][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,958][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,961][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,963][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,965][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,967][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,969][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,971][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,973][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,975][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,977][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,979][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,981][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,983][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,985][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,987][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,990][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,992][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,994][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:10,996][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:11,000][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:11,002][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:11,005][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:11,007][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:11,010][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:11,012][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:11,014][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:12,053][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,076][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,079][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,082][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,086][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,099][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,102][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,104][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,107][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,110][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,118][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,119][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,121][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,123][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,143][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,145][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,147][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,150][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,152][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,154][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,156][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,165][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,167][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,168][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,170][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,173][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,175][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,176][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,192][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,194][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,197][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,198][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,199][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,202][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,204][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,207][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,209][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,211][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,212][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,214][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,220][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,222][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,224][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,225][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,227][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,229][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,230][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,232][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,233][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,234][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,236][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,238][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,239][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,243][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,246][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,248][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,249][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,250][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,252][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,254][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,256][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,257][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,259][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,261][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,263][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,265][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,267][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,271][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,273][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,274][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,276][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,278][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,279][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,280][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,282][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,283][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,284][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,286][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,288][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,290][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,291][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,296][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,297][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,299][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,301][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,302][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,304][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,305][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,306][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,308][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,309][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,310][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,311][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,313][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,315][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,317][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,320][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,321][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,323][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,325][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,326][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,328][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,329][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:12,416][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,434][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,451][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,454][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,457][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,460][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,471][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,474][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,476][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,477][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,486][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,488][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,491][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,494][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,507][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,509][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,512][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,515][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,518][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,520][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,521][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,526][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,528][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,530][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,532][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,533][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,534][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,535][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,547][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,548][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,549][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,551][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,551][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,553][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,554][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,555][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,567][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,568][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,569][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,570][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,572][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,573][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,574][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,576][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,578][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,579][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,580][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,581][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,584][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,586][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,588][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,593][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,595][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,596][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,598][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,600][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,602][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,603][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,605][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,606][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,607][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,612][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,613][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,615][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,617][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,619][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,620][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,622][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,623][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,624][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,626][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,627][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,629][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,631][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,635][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,636][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,637][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,639][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,641][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,642][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,644][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,646][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,647][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,649][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,675][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,677][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,679][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,681][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,682][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,684][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,686][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,688][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,690][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,691][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,694][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,696][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,697][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,699][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,703][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,705][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,706][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,708][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,710][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:12,783][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:12,799][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:12,816][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:12,821][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:12,826][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:12,836][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:12,840][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:12,844][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:12,847][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:12,856][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:12,860][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:12,864][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:12,868][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:12,871][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:12,877][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:12,880][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:12,883][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:12,896][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:12,900][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:12,904][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:12,907][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:12,909][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:12,912][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:12,915][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:12,918][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:12,922][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:12,925][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:12,928][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:12,933][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:12,936][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:12,939][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:12,941][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:12,944][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:12,947][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:12,950][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:12,952][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:12,956][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:12,960][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:12,962][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:12,964][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:12,967][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:12,969][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:12,971][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:12,973][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:12,976][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:12,978][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:12,982][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:12,985][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:12,987][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:12,989][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:12,991][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:12,994][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:12,996][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:12,999][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:13,001][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:13,004][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:13,007][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:13,009][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:13,011][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:13,013][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:13,016][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:13,018][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:13,021][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:13,023][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:13,025][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:13,027][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:13,030][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:13,034][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:13,037][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:13,040][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:13,042][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:13,044][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:13,046][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:13,048][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:13,050][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:13,052][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:13,054][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:13,057][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:13,059][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:13,061][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:13,063][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:13,065][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:13,066][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:13,068][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:13,071][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:13,073][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:13,075][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:13,077][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:13,080][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:13,082][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:13,085][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:13,087][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:13,089][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:13,092][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:13,094][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:13,096][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:13,100][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:13,105][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:13,109][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:13,113][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:13,122][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:13,124][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:13,127][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:14,181][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,207][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,231][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,234][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,256][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,260][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,280][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,282][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,285][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,288][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,303][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,306][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,310][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,312][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,329][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,332][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,335][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,338][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,340][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,353][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,355][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,357][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,359][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,361][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,363][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,382][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,385][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,387][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,390][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,392][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,415][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,417][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,419][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,423][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,425][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,427][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,429][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,432][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,434][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,435][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,437][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,444][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,446][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,448][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,450][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,451][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,453][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,455][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,457][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,459][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,461][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,463][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,464][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,465][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,467][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,471][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,473][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,474][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,474][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,475][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,477][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,478][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,480][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,482][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,484][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,509][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,512][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,514][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,516][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,518][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,520][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,522][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,524][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,526][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,527][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,529][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,531][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,533][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,573][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,575][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,578][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,580][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,582][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,585][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,587][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,588][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,589][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,591][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,593][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,595][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,596][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,599][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,601][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,603][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,605][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,610][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,612][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,615][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,616][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:14,676][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:14,695][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:14,712][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:14,715][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:14,729][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:14,733][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:14,748][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:14,751][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:14,754][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:14,756][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:14,766][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:14,768][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:14,771][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:14,774][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:14,785][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:14,787][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:14,790][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:14,792][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:14,794][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:14,803][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:14,805][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:14,807][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:14,809][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:14,811][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:14,814][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:14,823][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:14,825][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:14,828][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:14,831][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:14,833][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:14,840][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:14,843][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:14,845][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:14,848][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:14,850][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:14,852][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:14,854][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:14,860][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:14,863][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:14,865][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:14,867][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:14,869][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:14,871][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:14,872][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:14,875][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:14,877][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:14,879][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:14,881][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:14,886][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:14,888][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:14,890][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:14,891][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:14,893][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:14,895][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:14,896][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:14,898][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:14,900][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:14,902][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:14,904][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:14,906][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:14,908][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:14,909][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:14,910][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:14,912][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:14,916][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:14,919][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:14,920][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:14,921][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:14,923][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:14,924][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:14,925][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:14,927][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:14,928][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:14,929][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:14,930][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:14,931][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:14,932][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:14,936][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:14,937][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:14,939][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:14,940][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:14,949][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:14,951][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:14,952][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:14,954][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:14,955][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:14,957][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:14,959][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:14,961][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:14,962][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:14,964][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:14,966][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:14,998][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:15,000][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:15,001][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:15,003][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:15,053][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:15,072][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:15,086][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:15,092][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:15,107][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:15,112][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:15,127][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:15,132][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:15,135][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:15,146][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:15,151][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:15,155][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:15,158][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:15,161][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:15,166][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:15,169][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:15,172][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:15,175][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:15,178][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:15,189][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:15,192][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:15,195][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:15,199][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:15,202][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:15,210][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:15,213][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:15,216][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:15,218][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:15,221][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:15,230][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:15,233][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:15,236][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:15,238][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:15,240][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:15,243][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:15,253][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:15,255][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:15,258][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:15,260][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:15,263][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:15,266][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:15,269][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:15,275][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:15,278][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:15,281][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:15,284][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:15,287][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:15,289][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:15,292][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:15,294][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:15,297][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:15,302][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:15,304][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:15,306][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:15,309][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:15,311][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:15,313][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:15,316][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:15,319][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:15,321][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:15,324][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:15,327][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:15,329][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:15,338][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:15,340][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:15,343][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:15,346][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:15,348][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:15,350][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:15,353][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:15,355][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:15,358][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:15,360][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:15,363][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:15,365][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:15,367][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:15,369][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:15,371][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:15,374][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:15,376][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:15,378][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:15,381][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:15,384][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:15,386][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:15,388][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:15,390][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:15,393][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:15,395][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:15,398][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:15,400][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:15,402][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:15,412][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:15,414][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:15,417][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:15,419][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:16,523][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:16,555][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:16,559][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:16,562][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:16,584][mllm.models.large_language_model_local][WARNING] - Response Given Bob's valuation of books and balls much lower than yours, and your valuation of books and balls much higher than theirs, it might be advantageous to focus on getting as many books and balls as possible while keeping a few hats. This strategy aims to maximize your points by leveraging your higher valuations for these items. Proposal: 3 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:16,588][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:16,591][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:16,609][mllm.models.large_language_model_local][WARNING] - Response Given Alice's values, she highly values books and balls, while I value hats and books more. To maximize my points, I should propose to take as many books and balls as possible since they have higher values for me. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:16,634][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:16,637][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:16,640][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:16,643][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:16,647][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:16,649][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:16,663][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:16,666][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:16,670][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:16,672][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:16,675][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:16,678][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:16,680][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:16,693][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:16,696][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:16,714][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Alice place high value on balls and low value on hats and books. Proposing to take all the balls while dividing the hats and books proportionally could maximize your earnings in this round. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:16,717][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:16,718][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:16,738][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:16,741][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:16,743][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:16,745][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:16,748][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:16,750][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:16,753][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:16,755][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:16,766][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:16,769][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:16,772][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:16,774][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:16,776][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:16,791][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:16,794][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:16,796][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:16,798][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:16,800][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:16,802][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:16,814][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:16,817][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:16,819][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:16,821][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:16,823][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:16,826][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:16,828][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:16,830][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:16,832][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:16,834][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:16,836][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:16,838][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:16,839][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:16,841][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:16,842][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:16,848][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:16,850][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:16,852][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:16,855][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:16,856][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:16,858][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:16,860][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:16,861][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:16,863][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:16,870][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:16,871][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:16,873][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:16,874][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:16,876][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:16,878][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:16,879][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:16,881][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:16,883][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:16,885][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:16,886][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:16,887][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:16,887][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:16,890][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:16,892][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:16,893][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:16,929][mllm.models.large_language_model_local][WARNING] - Response Given the updated per-item values where hats are valued at 1 by both of us, and books and balls are valued at 10 and 1 respectively for me, while Bob values books and balls at 1, the optimal strategy would be to try to maximize the items we value highly while ensuring we do not overpropose and cause an overallocation. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:16,931][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:16,934][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:16,936][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:16,938][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:16,973][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:17,031][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:17,046][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:17,049][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:17,052][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:17,064][mllm.models.large_language_model_local][WARNING] - Response Given the updated per-item values, where you have a high value for books (10) and Bob has a high value for hats (10), it's important to consider that we might not both get our highest-valued items. We should propose a split that maximizes our expected value based on these new preferences. Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:17,067][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:17,070][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:17,073][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:17,085][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:17,107][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:17,109][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:17,112][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:17,114][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:17,117][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:17,119][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:17,125][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:17,127][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:17,129][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:17,130][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:17,132][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:17,133][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:17,142][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:17,144][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:17,146][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:17,163][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:17,168][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:17,182][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:17,185][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:17,187][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:17,189][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:17,191][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:17,193][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:17,194][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:17,204][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:17,206][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:17,208][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:17,210][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:17,213][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:17,215][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:17,228][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:17,230][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:17,231][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:17,233][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:17,234][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:17,247][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:17,250][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:17,252][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:17,254][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:17,256][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:17,258][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:17,269][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:17,271][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:17,273][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:17,276][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:17,278][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:17,280][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:17,282][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:17,284][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:17,285][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:17,286][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:17,287][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:17,289][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:17,290][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:17,300][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:17,303][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:17,304][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:17,307][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:17,309][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:17,311][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:17,312][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:17,313][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:17,314][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:17,316][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:17,318][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:17,320][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:17,322][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:17,324][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:17,326][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:17,327][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:17,329][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:17,330][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:17,361][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:17,364][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:17,367][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:17,369][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:17,372][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:17,409][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:17,464][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:17,478][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:17,485][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:17,490][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:17,500][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:17,505][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:17,515][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:17,536][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:17,541][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:17,546][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:17,555][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:17,559][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:17,564][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:17,568][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:17,572][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:17,575][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:17,579][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:17,583][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:17,590][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:17,594][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:17,597][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:17,603][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:17,622][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:17,625][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:17,629][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:17,632][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:17,635][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:17,639][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:17,643][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:17,677][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:17,681][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:17,685][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:17,689][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:17,693][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:17,697][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:17,703][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:17,707][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:17,710][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:17,714][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:17,718][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:17,726][mllm.models.large_language_model_local][WARNING] - Response Given the new per-item values, where I value hats and books more highly than Alice, and Alice values balls more, a more strategic approach would be to propose a split that takes advantage of these differing valuations. Proposing an equal split would not maximize points, so let's propose taking the majority of the books and balls to maximize my earnings. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:17,730][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:17,733][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:17,736][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:17,738][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:17,741][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:17,743][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:17,755][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:17,758][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:17,761][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:17,763][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:17,766][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:17,769][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:17,771][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:17,774][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:17,776][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:17,779][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:17,783][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:17,786][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:17,789][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:17,793][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:17,797][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:17,799][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:17,802][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:17,804][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:17,807][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:17,809][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:17,811][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:17,813][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:17,816][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:17,818][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:17,821][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:17,823][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:17,825][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:17,827][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:17,830][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:17,832][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:17,835][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:17,838][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:17,840][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:17,842][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:17,845][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:17,887][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:18,070][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:18,131][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:18,290][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:18,293][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:18,521][mllm.models.large_language_model_local][WARNING] - Response Given the new per-item values, I should focus on maximizing the points from the items where I have a higher value. Here, hats and balls have higher values for me, while books have a higher value for Bob. A strategic proposal could be to take most of the hats and balls, and a smaller portion of the books. Proposal: 10 hats, 10 books, 10 balls However, since taking all 10 books might be aggressive and could result in a proportional split if Bob heavily values them, I should be moderate. Let's propose taking most of the hats and balls, and half of the books. Proposal: 10 hats, 5 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:18,793][mllm.models.large_language_model_local][WARNING] - Response Given the values, it seems that the best strategy is to propose taking all items, believing that Alice will likely do the same due to the symmetric nature of the problem. Therefore, the proposal would be: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:19,034][mllm.models.large_language_model_local][WARNING] - Response Given the new per-item values, where my values are lower for hats and books but higher for balls compared to Bob's, I should focus on claiming more of the items I value more. Here is my proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:19,037][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:19,067][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, you have a strong valuation for hats and books, while Alice has a strong valuation for balls. To maximize your points, you should propose to take as much of the items you value highly. Here’s a strategic proposal: Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:19,070][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:19,073][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:19,075][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:19,090][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:19,092][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:19,095][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:19,098][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:19,141][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:19,143][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:19,146][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:19,169][mllm.models.large_language_model_local][WARNING] - Response Given the new values, where you have a higher value for books and balls, and Bob has a higher value for hats and books, a strategic approach would be to allocate more of the items you value higher. Here’s a proposed allocation: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:19,172][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:19,175][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:19,194][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:19,196][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:19,198][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:19,218][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:19,220][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:19,222][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:19,225][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:19,239][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:19,241][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:19,244][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:19,262][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:19,264][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:19,266][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:19,267][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:19,269][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:19,272][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:19,287][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:19,290][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:19,291][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:19,292][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:19,293][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:19,294][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:19,297][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:19,298][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:19,310][mllm.models.large_language_model_local][WARNING] - Response Propposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:19,312][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:19,315][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:19,316][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:19,319][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:19,320][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:19,357][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:19,357][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:19,360][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:19,362][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:19,363][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:19,366][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:19,368][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:19,370][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:19,382][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:19,383][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:19,384][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:19,384][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:19,385][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:19,387][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:19,388][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:19,390][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:19,392][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:19,394][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:19,396][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:19,398][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:19,406][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:19,408][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:19,409][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:19,411][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:19,411][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:19,425][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:19,426][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:19,428][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:19,465][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric nature of the per-item values and the equal likelihood of proportional allocation, it seems that splitting the items equally would maximize the points for both parties due to the high values of hats and books for both agents. Here's the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:19,482][mllm.models.large_language_model_local][WARNING] - Response Given the revised per-item values, where hats and books are more valuable to you and balls are more valuable to Bob, a more strategic approach would be to propose keeping the items you value more while ensuring that the total quantity proposed does not exceed what is available. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:19,485][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:19,498][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:19,502][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:19,504][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:19,505][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:19,518][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:19,522][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:19,525][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:19,558][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:19,576][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:19,581][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:19,594][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:19,597][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:19,602][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:19,618][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:19,619][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:19,622][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:19,641][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:19,643][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:19,645][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:19,662][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:19,664][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:19,666][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:19,668][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:19,670][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:19,682][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:19,684][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:19,686][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:19,688][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:19,690][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:19,691][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:19,692][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:19,693][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:19,703][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:19,704][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:19,706][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:19,707][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:19,708][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:19,724][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:19,726][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:19,728][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:19,729][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:19,730][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:19,732][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:19,734][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:19,742][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:19,743][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:19,744][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:19,746][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:19,748][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:19,749][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:19,750][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:19,750][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:19,752][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:19,767][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:19,769][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:19,770][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:19,773][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:19,774][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:19,776][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:19,790][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:19,791][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:19,793][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:19,867][mllm.models.large_language_model_local][WARNING] - Response Given the new per-item values, it appears that both you and Bob value balls the most, while hats are valued least by both of you. Balls are highly valuable to you, and both you and Bob value them more than the other items. To maximize your points, you should propose to claim as many balls as possible while also taking some hats and books to ensure you do not receive an insufficient portion. Proposals from both parties are likely to be similar, so to ensure you get a fair share of the balls, it's best to propose a higher number of balls than Bob might propose. Here is a proposed allocation: Proposal: 10 hats, 10 books, 20 balls However, since you can't propose more balls than there are available, the maximum you can propose for balls is 10. Thus, the revised proposal is: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:19,870][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:19,887][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:19,892][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:19,910][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:19,968][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:19,988][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:20,011][mllm.models.large_language_model_local][WARNING] - Response Given the updated per-item values, where your values are hats=10, books=1, balls=1 and Alice's are hats=1, books=1, balls=10, a strategic proposal would be to maximize the items with higher value to you while still considering Alice's values. Here is the proposal: Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:20,016][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:20,030][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:20,034][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:20,051][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:20,055][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:20,059][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:20,095][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:20,100][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:20,104][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:20,108][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:20,111][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:20,118][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:20,122][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:20,124][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:20,126][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:20,129][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:20,132][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:20,136][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:20,144][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:20,147][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:20,149][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:20,152][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:20,154][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:20,168][mllm.models.large_language_model_local][WARNING] - Response Given Alice's per-item values are higher for hats and balls, but you have a significantly higher value for books, it might be beneficial to allocate more books to yourself to maximize your points. Here is the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:20,169][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:20,172][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:20,175][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:20,177][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:20,180][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:20,182][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:20,184][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:20,194][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:20,196][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:20,199][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:20,202][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:20,204][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:20,206][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:20,208][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:20,211][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:20,213][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:20,227][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:20,229][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:20,231][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:20,233][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:20,236][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:20,238][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:20,241][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:20,257][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that Alice values hats and balls more than books, and you value books more than hats and balls. To maximize your points, you should propose taking all the books since they have the highest value for you. Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:20,258][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:20,261][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:20,263][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:20,340][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:20,342][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:20,345][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:20,452][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it seems both you and Alice value hats and books highly, but Alice values balls more than you do. To maximize points, it's important to consider the proportion of items we each value highly. Here’s a strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:20,493][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Bob have high preferences for books and balls, and low preferences for hats. However, to ensure a fair distribution and avoid Bob taking all the books and balls, we need to propose a distribution that reflects our values while ensuring a balanced outcome. Proposal: 10 hats, 10 books, 10 balls This proposal ensures that both you and Bob get a fair share of hats and avoids one of you taking all the books and balls. Let's send this proposal to balance the distribution and maximize points effectively. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:20,547][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:20,598][mllm.models.large_language_model_local][WARNING] - Response Given the values, it seems Alice values books and balls highly, while you value hats and books highly. To maximize your points, you should propose to take all of the items you value highly, assuming Alice will propose to take all of the items she values highly. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:20,654][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:20,658][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:20,701][mllm.models.large_language_model_local][WARNING] - Response Given the values, both you and Bob have low desire for hats and high desire for books. To maximize your points, it's beneficial to propose a distribution that aligns with your values while also considering Bob's preferences. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:20,862][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both you and Bob value the balls the most, but you value the books much more than Bob does. To maximize your points, you should propose to take all the balls since they have the highest value for you. Here is a strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:20,921][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:21,026][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric and equal value structure of the items, where you and Alice have complementary valuations, proposing a fair split would likely maximize points for both agents. Here, proposing to take all items is optimal since the items are equally valuable to both of you and there's no risk of exceeding the total quantity. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:21,118][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:21,296][mllm.models.large_language_model_local][WARNING] - Response Given Alice's per-item values, she valuates hats and balls much higher than books. To maximize my points, I should propose a distribution that takes advantage of this while ensuring I get a reasonable share of the books to maximize my total points. Proposal: 10 hats, 10 books, 9 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:21,299][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the previous round results, proposing the same allocation is reasonable based on the observed outcomes. However, to explore a slightly different strategy, we could propose slightly fewer items to see if it influences Alice's response. Proposal: 9 hats, 9 books, 9 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:21,386][mllm.models.large_language_model_local][WARNING] - Response Given the updated per-item values, where you have higher values for books and balls, it makes sense to propose more of these items while still considering the items with lower per-item values. However, we should also ensure that our proposal is slightly more generous than Bob's to maximize our share. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:21,452][mllm.models.large_language_model_local][WARNING] - Response Given the new per-item values where hats are worth 10 to you and balls are worth 1, and Alice values hats at 1 and balls at 10, it makes sense to propose keeping more hats and balls while giving Alice the books. Here’s a proposal that maximizes your value: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:21,456][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Bob place high value on balls. However, you should consider the symmetry and the fact that you both have similar valuations for different items. A cooperative approach might yield better results. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:21,459][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:21,466][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:21,470][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:21,473][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:21,496][mllm.models.large_language_model_local][WARNING] - Response Given Alice's valuation of books much higher than hats or balls and your own valuation giving high value to hats and balls, a strategic approach would be to try to secure as many hats and balls as possible while offering just enough books to ensure Alice doesn't propose an allocation that benefits her more than it should. Proposal: 10 hats, 1 book, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:21,499][mllm.models.large_language_model_local][WARNING] - Response Given the new per-item values where both you and Bob value hats and books at 10 and balls at 1, the optimal strategy would be to cooperate and propose an even division of the items to maximize the points for both parties. Here's the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:21,502][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:22,098][mllm.models.large_language_model_local][WARNING] - Response Given the values, Alice places a high value on hats and books, while I place a high value on books and balls. To maximize my points, I should propose to take all the books and balls, and split the hats proportionally. Proposal: 5 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:22,122][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Bob value the hats the least, but you value books and balls much more than Bob does. To maximize your points, you should propose keeping all the items that you value highly, while keeping at least some hats to ensure the total proposal does not exceed the available quantity. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:22,152][mllm.models.large_language_model_local][WARNING] - Response Given the updated values, I should focus on maximizing the items where my value is higher. Since I value hats and balls more, and Bob values books and balls more, it makes sense to propose a distribution that maximizes my value. Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:22,222][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:22,225][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:22,246][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the values involved, it seems that proposing to take all the items will result in a fair allocation if Alice does the same, because the total quantity of items exactly matches the proposals. However, to ensure fairness and to maximize points while accounting for potential deviations in Alice's strategy, a strategic approach would be to propose a slightly less aggressive but still high claiming. Proposal: 9 hats, 9 books, 9 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:22,248][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:22,251][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:22,254][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:22,278][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:22,280][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:22,281][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:22,327][mllm.models.large_language_model_local][WARNING] - Response Given the values, I should focus on maximizing the points by taking advantage of the high value of books for me and the low value of hats for Alice. Here’s the proposal: Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:22,332][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:22,335][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:22,337][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:22,340][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:22,341][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:22,354][mllm.models.large_language_model_local][WARNING] - Response Given the values, it appears that both you and Bob have opposite preferences for hats, books, and balls. To maximize our points, we should attempt to get as many of the items we value highly as possible. Since the item quantities are the same for both of us, and we propose simultaneously, a good strategy is to propose a split that ensures we get the items we value highly. Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:22,356][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric and complementary nature of our per-item values, proposing the same split as before seems to be a reasonable strategy. Here's my proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:22,359][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:22,361][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:22,363][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:22,365][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:22,396][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it appears that both you and Alice place high value on different items. You value hats and balls at 10 and books at 1, while Alice values books at 1 and balls at 10. To maximize your points, you should propose to take all the items you value highly and let Alice take the items she values more. Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:22,398][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:22,400][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:22,425][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:22,427][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:22,428][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:22,428][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:22,429][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:22,473][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Bob place the highest value on balls and significantly lower value on hats and books. To maximize points, you should propose an allocation that prioritizes balls while still accounting for hats and books. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:22,478][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:22,480][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:22,482][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:22,485][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:22,486][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:22,495][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:22,498][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:22,500][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:22,502][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:22,504][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:22,519][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:22,521][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:22,523][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:22,524][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:22,526][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:22,529][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:22,562][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:22,564][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:22,566][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:22,584][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:22,586][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:22,591][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:22,595][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:22,599][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:22,607][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:22,610][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:22,633][mllm.models.large_language_model_local][WARNING] - Response Given the revised per-item values where hats are worth 10 points for me and 1 point for Bob, books are worth 1 point for me and 10 points for Bob, and balls are worth 1 point for both of us, it seems advantageous to maximize the value from hats and balls since they are worth the most to me. Proposal: 10 hats, 10 balls, 0 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:22,635][mllm.models.large_language_model_local][WARNING] - Response Given Bob's strategy of taking all books and balls and leaving no hats, a balanced approach would be to take all the hats and split the books and balls equally. This way, you maximize the items with your higher value and ensure you get some value from the items Bob values highly. Proposal: 10 hats, 5 books, 5 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:22,640][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:22,655][mllm.models.large_language_model_local][WARNING] - Response Given that both you and Alice have similar values for hats and books, and balls, proposing to take all items seems to be a fair and maximizing strategy in this symmetric scenario. Here is your proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:22,657][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:22,698][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:22,701][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:22,737][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:22,776][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:22,782][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:22,786][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:22,798][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:22,800][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:22,801][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:22,804][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:22,840][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:22,841][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:22,842][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:22,843][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:22,860][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:22,863][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:22,865][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:22,880][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:22,882][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:22,883][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:22,885][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:22,887][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:22,899][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:22,901][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:22,904][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:22,906][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:22,907][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:22,921][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:22,923][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:22,925][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:22,927][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:22,944][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:22,945][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:22,947][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:22,948][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:22,949][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:22,951][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:22,989][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:22,991][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:22,993][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:22,994][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:23,074][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric and equal per-item value scenarios, where both you and Alice have similar or identical values for the same items, splitting the items equally seems to be a reasonable strategy. This ensures that both you and Alice get the maximum points possible for each round. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:23,077][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:23,083][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:23,155][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:23,236][mllm.models.large_language_model_local][WARNING] - Response Given the values, you have a high value for hats and balls, while Alice values books the most. To optimize your points, you should propose to claim all the hats and balls, while Alice will likely propose to claim all the books. Here is the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:23,238][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:23,244][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:23,320][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Bob heavily value hats and balls, while books are valued lower by both. Given that, a balanced approach might not be optimal. Here’s a proposal that tries to maximize value based on the higher per-item values: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:23,324][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:23,329][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:23,333][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:23,401][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:23,487][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:23,490][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:23,569][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:23,573][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:23,654][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:23,739][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:23,784][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:23,791][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:23,798][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:23,801][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:23,848][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and the symmetry in the game where both Alice and Bob have similar values for different items, the optimal strategy is to propose an allocation that maximizes the benefit from the items with higher personal value. Here, Alice values hats at 1, books at 10, and balls at 10, while Bob values hats at 10, books at 10, and balls at 1. Proposing to take all of the books and balls, and splitting the hats, would align with maximizing the points based on personal value. Proposal: 5 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:23,850][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:23,856][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:23,863][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:23,873][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:23,876][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:23,878][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:23,896][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that books are most valuable to both of us, followed by balls and then hats. Since we both value books the most, it's crucial to secure as many books as possible. Here’s a strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:23,897][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:23,900][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:23,904][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:23,907][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:23,909][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:23,921][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:23,925][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:23,929][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:23,931][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:23,945][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:23,949][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:23,952][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:23,955][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:23,958][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:24,118][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric nature of the items and per-item values, and aiming to maximize points, it's beneficial to propose splitting the items equally to avoid any misallocation. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:24,250][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:24,253][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:24,255][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:24,257][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:24,259][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:24,262][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:24,299][mllm.models.large_language_model_local][WARNING] - Response Given the new per-item values where hats have the highest value for you and balls have the highest value for Alice, a strategic proposal would be to maximize your points by keeping the items that value you the most. Here's the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:24,361][mllm.models.large_language_model_local][WARNING] - Response Given the high values that Bob assigns to hats and books, and your values for balls, it makes sense to try to maximize the distribution of items where your values are highest, i.e., balls. However, since both you and Bob value balls, it's important to propose a distribution that avoids overproposing, which would lead to proportional allocation if the total exceeds the item quantity. Proposal: 10 hats, 10 books, 9 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:24,364][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the high values for hats and books for both of us, splitting the items equally seems like the optimal strategy to maximize points. Here’s the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:24,368][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:24,446][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:24,449][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:24,452][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:24,457][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:24,461][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:24,493][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:24,495][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:24,533][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:24,535][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:24,686][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both you and Bob place a high value on different items. You value hats and balls highly, while Bob values books highly. To maximize your points, it would be strategic to propose keeping more of the items you value more. Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:24,689][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric and complementary nature of the per-item values, it is beneficial to propose a distribution that maximizes the product of the amount we keep and our own per-item value, while also considering the potential for proportional allocation if the total proposed amounts exceed the available items. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:24,718][mllm.models.large_language_model_local][WARNING] - Response Given Alice's valuation of hats and balls, and your own valuation of books, it might be strategic to focus on items where your valuation is significantly different from Alice's. Here, books are valued at 1 by Alice and 10 by you, hats and balls are valued similarly at 10 and 1, respectively. A strategy could involve proposing to take all the books since that will maximize your points if Alice agrees to follow suit. Here is the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:24,724][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:24,730][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:24,838][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:24,919][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:24,922][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:25,011][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:25,014][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:25,099][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric and high value nature of the items for both players, a fair and competitive proposal would be to distribute the items evenly. Here is the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:25,102][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:25,188][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:25,193][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:25,196][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:25,198][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:25,273][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:25,276][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:25,359][mllm.models.large_language_model_local][WARNING] - Response Given the updated values, since I value hats and balls highly and Alice values books and balls highly, it makes sense to maximize the items where we both have low values and minimize the items where we both have high values. Here’s a strategic proposal: Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:25,363][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:25,447][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:25,451][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:25,454][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:25,456][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:25,457][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:25,464][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:25,467][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:25,470][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:25,473][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:25,474][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:25,533][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:25,617][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:25,620][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:25,623][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:25,631][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:25,705][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:25,709][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:25,713][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:25,720][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:25,787][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:25,790][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:25,793][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:25,876][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:26,004][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:26,008][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:26,028][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:26,032][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:26,034][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:26,035][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:26,037][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:26,039][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:26,061][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:26,064][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:26,066][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:26,094][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:26,100][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:26,105][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:26,108][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:26,111][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:26,337][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the high value that both you and Bob place on balls, it's important to ensure that we both get a fair share of the balls. Since balls are highly valuable for both of us, proposing an equal split for the balls might be a reasonable strategy. Here’s a proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:26,540][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, the optimal strategy would be to propose the distribution that maximizes your points. Since hats are valued at 10 by you and 10 by Bob, books are valued at 1 by you and 10 by Bob, and balls are valued at 10 by you and 1 by Bob, proposing to take all of the items would maximize your points. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:26,544][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both you and Alice value hats and books highly, but Alice values balls more than you do. To maximize your points, you should focus on taking more items that you value more. Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:26,720][mllm.models.large_language_model_local][WARNING] - Response Given the values for this round, it is beneficial to propose a distribution that maximizes the advantage of the high value items (hats and books) while still ensuring a fair allocation of the lower value item (balls). Here is the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:26,775][mllm.models.large_language_model_local][WARNING] - Response Given Alice's valuation for hats and balls, and your own valuation, it makes sense to propose an allocation that maximizes your points while considering Alice's likely response. Since Alice values hats and balls higher than just their own points, and you value books more than hats and balls, it's best to propose an allocation that takes these valuations into account. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:26,777][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:26,780][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:26,783][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:26,785][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:26,791][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:26,796][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:26,801][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:26,803][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:26,805][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:26,806][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:26,811][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:26,815][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:26,847][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:26,849][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:26,851][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:26,927][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Alice have high values for different items. Since you value hats and balls highly and Alice values books and balls highly, proposing an equal split might not be ideal. However, given the symmetry and the fact that both of you are proposing the same amounts, a strategic approach would be to slightly adjust your proposal to capture more of the items you value more. Proposal: 10 hats, 10 books, 9 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:27,013][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the high value of books for both of us, as well as the equal distribution of item quantities and values, a fair strategy is to propose a split that acknowledges the symmetry and attempts to maximize the total points. Since both of us have high values for the same items, splitting them evenly would be a good approach. Here is my proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:27,094][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:27,177][mllm.models.large_language_model_local][WARNING] - Response Given the values, you have a significant preference for balls and only a slight value for hats and books. Alice has a moderate value for hats and books and a slight value for balls. To maximize your points, it is strategically beneficial to claim the items that have the highest value for you, which are the balls. A good strategy here is to claim all the balls and split the hats and books proportionally or according to their values. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:27,180][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:27,181][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:27,184][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:27,187][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:27,190][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:27,192][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:27,260][mllm.models.large_language_model_local][WARNING] - Response Given the values, the optimal strategy would be to propose the items in a way that maximizes the points based on your per-item values. Since you value hats at 10, books at 1, and balls at 10, and Bob values hats at 1, books at 10, and balls at 1, proposing all the items equally would not be optimal. Instead, you should aim to get as many hats and balls as possible since they have higher values for you. Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:27,263][mllm.models.large_language_model_local][WARNING] - Response Given the values, the best strategy is to take more of the items that I value highly and fewer of the items that Bob values more. Here, I value books the most (10) and Bob values hats the most (10). Books and hats are our highest valued items, followed by balls which we both value less. ProposAl: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:27,265][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:27,269][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:27,272][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:27,342][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:27,345][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:27,425][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:27,513][mllm.models.large_language_model_local][WARNING] - Response Given Bob's per-item values, he will place a high value on balls and a low value on hats and books. Meanwhile, you place a high value on hats and books. Since you both value hats and books more than balls, a coordinated strategy might be beneficial to secure more of these items. However, proposing to take all items might lead to a proportional distribution if the sum of proposals exceeds the total items. A strategic proposal would be to slightly outbid each other in a way that ensures you both receive a significant portion of the hats and books while minimizing the risk of a proportional distribution over balls. Proposal: 9 hats, 9 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:27,517][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:27,520][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:27,603][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:27,607][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:27,610][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:27,688][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:27,691][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:27,695][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:27,777][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:27,781][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:27,862][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:27,865][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:27,869][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:27,873][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:27,877][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:27,930][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:27,934][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:27,937][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:28,064][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:28,067][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:28,069][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:28,070][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:28,072][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:28,074][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:28,076][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:28,193][mllm.models.large_language_model_local][WARNING] - Response Given the values, since you have a high value for hats (10) and balls (10), and Bob has a high value for books and hats, it's beneficial to negotiate or propose a split that maximizes your value. A reasonable strategy is to propose a split that keeps you competitive and reflects the values you and Bob have for the items. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:28,355][mllm.models.large_language_model_local][WARNING] - Response Given that both you and Bob have high values for balls and lower values for hats, and that the previous round's results suggest that splitting the items evenly results in both achieving high points, a strategic proposal would still be to offer an equal split. However, to account for the possibility of Bob wanting to secure as many balls as possible, you might want to slightly adjust the proposal to ensure you get a fair share of the high-value balls while still offering an attractive deal. Proposal: 10 hats, 10 books, 9 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:28,393][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:28,397][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:28,400][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:28,402][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:28,404][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:28,409][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:28,412][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:28,417][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:28,422][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:28,427][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:28,432][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:28,436][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:28,438][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:28,441][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:28,443][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:28,445][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:28,449][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:28,464][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:28,881][mllm.models.large_language_model_local][WARNING] - Response Given the values, it seems strategically advantageous to propose keeping all the balls since they have the highest individual value for both you and Bob. However, to ensure that we get the books and hats as well, we should propose a fair split while maximizing our high-value items. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:28,883][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:28,911][mllm.models.large_language_model_local][WARNING] - Response Given the high value of hats and books for both you and Alice, and the low value of balls, we should aim to maximize our shares of hats and books while also considering the proportional distribution of balls. Here's a strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:28,914][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:28,915][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:28,919][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:28,922][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:28,925][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:28,928][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:28,941][mllm.models.large_language_model_local][WARNING] - Response Given our per-item values and Bob's, it makes sense to propose a more strategic distribution to ensure we maximize our points. Since I value hats and books relatively low (1), and balls very high (10), while Bob values balls very low (1) and the other items relatively high, we should try to keep the items where we value them more. Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:28,943][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric nature of the situation and the values of the items, it makes sense to propose splitting the items evenly to maximize the utility for both parties. Here is my proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:28,970][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:28,996][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:29,022][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:29,025][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:29,049][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:29,052][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:29,055][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:29,100][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:29,104][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:29,131][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:29,135][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:29,155][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and the objective to maximize points, it's beneficial to consider the distribution of values. You value hats and balls significantly more than books, while Bob values books more highly. To ensure an optimal allocation, we should try to maximize the value of items you prefer while minimizing the value of books. Proposal: 10 hats, 10 balls, 0 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:29,158][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:29,160][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:29,162][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:29,166][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:29,205][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:29,233][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both you and Bob place high value on different items. You value hats at 10, books at 1, and balls at 1. Bob values hats at 1, books at 10, and balls at 10. To maximize your points, you should try to get as many hats as possible, while Bob is likely to try to get as many books and balls as possible. Here's a strategy where you propose to take all the hats and a fair share of books and balls. Proposal: 10 hats, 5 books, 5 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:29,237][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:29,242][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:29,247][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:29,252][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:29,270][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:29,353][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's important to maximize the points by aligning with the higher values of the items. Since you value books (10) and balls (10) more than hats (1), and Alice values hats (10) and balls (10) more than books (1), it makes sense to propose a distribution that maximizes your value for books and balls. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:29,359][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the high values for hats and books, it makes sense to propose a sharing strategy that maximizes the points based on the per-item values. Since both you and Alice have high values for hats and books, and similar low values for balls, proposing to take all hats and books and sharing the balls might be a good strategy. Proposal: 10 hats, 10 books, 5 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:29,363][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:29,366][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:29,369][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:29,371][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:29,374][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:29,377][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:29,440][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Alice have high values for different items. To maximize points, we should try to secure the items we value highly while ensuring the total allocation does not exceed the item quantity. Your per-item values: - Hats: 10 - Books: 1 - Balls: 10 Alice's per-item values: - Hats: 1 - Books: 10 - Balls: 10 A proportional allocation might be more favorable here. Given the high value you place on hats and balls, and Alice values books and balls highly, we should try to secure a balance that maximizes our points. Proposal: 10 hats, 10 books, 10 balls This proposal ensures that we take the full allocation of items, which can be split proportionally. Given the values, this should optimize the points for both of us. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:29,442][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:29,446][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:29,449][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:29,451][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:29,454][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:29,456][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:29,529][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:29,530][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:29,533][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:29,536][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:29,537][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:29,538][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:29,709][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:29,790][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:29,794][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:29,801][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:29,808][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:29,815][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:29,822][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:29,828][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:29,868][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:30,031][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:30,123][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's beneficial to propose an allocation that maximizes the higher valued items. Since both you and Alice value the same items highly, a Nash equilibrium might involve proposing an equal split to avoid the risk of a proportional distribution in case of conflict. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:30,130][mllm.models.large_language_model_local][WARNING] - Response Given the new per-item values, I should focus more on securing the balls since they have the highest value for you. Here's the proposal: ProposAl: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:30,137][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:30,139][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:30,206][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:30,209][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:30,213][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:30,385][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:30,460][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:30,463][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:30,467][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:30,484][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:30,487][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:30,490][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:30,510][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Bob value hats less, but you value books and balls significantly more than Bob does. To maximize your points, you should propose to take all the items that you value highly. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:30,513][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:30,535][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:30,537][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:30,540][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:30,578][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that hats are least valuable to both of us, and balls are the most valuable. Books have a moderate value for both. To maximize my points, I should focus on getting as many of the high-value items (balls) as possible. Since we have identical per-item values for hats and books, and complementary values for balls, an optimal strategy would involve splitting the balls between us and taking as many of the high-value items as possible. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:30,581][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's important to maximize the points based on the perceived value. Since you value books at 10 and balls at 1, and Bob values hats at 10, books at 1, and balls at 1, we should try to secure as many books and balls as possible while giving some value to hats to reach the total item quantity. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:30,662][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, you have a strong preference for balls and Bob has a similar preference for books and balls. To exploit this, you should propose a distribution that maximizes your points while also ensuring that Bob doesn't get too many items. Here’s a strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:30,669][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:30,673][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:30,710][mllm.models.large_language_model_local][WARNING] - Response Given the values, it seems optimal to propose the entire allocation of items since both you and Alice value books and balls significantly differently. You value hats (10) and books (1) highly, while Alice values hats (1) and books (10) highly. Therefore, proposing to keep all of the hats, all of the books, and all of the balls is a reasonable strategy. proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:30,714][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the results of previous rounds, it seems that splitting the items equally leads to a fair outcome where both parties earn the same amount of points. Given that both you and Bob value hats and balls equally and books proportionally, a reasonable strategy is to propose an even split: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:30,840][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the fact that Alice tends to value books and balls more, while you value hats and books more, it makes sense to maximize the distribution of items such that both you and Alice receive items valued according to their preferences. Here's a balanced proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:30,866][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that hats are most valuable to you, followed by books, and then balls. Alice values balls the most. To maximize your points, you should propose to keep as many hats and books as possible, while giving Alice some balls. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:30,870][mllm.models.large_language_model_local][WARNING] - Response Given the updated per-item values, where you have a higher value for books and Bob has a higher value for hats and books, it's beneficial to propose a distribution that aligns with these values. Here's a strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:30,923][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:31,193][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:31,196][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:31,200][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:31,205][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:31,211][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:31,216][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:31,220][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:31,289][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:31,352][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:31,359][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:31,366][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:31,382][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:31,415][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:31,418][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:31,466][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:31,468][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:31,566][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:31,570][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:31,637][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:31,640][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:31,669][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:31,702][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:31,723][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both you and Alice place the highest value on books and the lowest on balls. To maximize your points, you should aim to get as many books as possible while still proposing a fair share of hats and balls. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:31,727][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:31,757][mllm.models.large_language_model_local][WARNING] - Response Given the preferences, to maximize my points, I should focus on the items I value the most, which are balls (10 points each). I should also consider that Bob will likely keep the books (10 points each) since he values them as much as I do. Here is my optimal proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:31,763][mllm.models.large_language_model_local][WARNING] - Response Given the values, it seems optimal to propose a split that maximizes the value of the items you have a higher valuation for. Given your per-item values and Alice's per-item values, the strategy is to ensure you get the books and hats while Alice gets the balls. Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:31,766][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:31,807][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:31,894][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:31,970][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:31,973][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:31,978][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:32,040][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:32,043][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:32,046][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:32,071][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:32,074][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:32,077][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:32,079][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:32,082][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:32,084][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:32,118][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:32,121][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:32,124][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:32,127][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:32,128][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:32,129][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:32,202][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:32,206][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:32,209][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:32,213][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:32,216][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:32,218][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:32,221][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:32,225][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:32,361][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it makes sense to allocate the items in a way that maximizes the points for both you and Alice. Here, hats and books have high value for you, and balls have high value for Alice. A strategic proposal would be to split the items in a way that both parties get a good portion of their high-value items. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:32,368][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:32,423][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both hats and balls are highly valued by me and slightly valued by Alice, while books are slightly valued by me and highly valued by Alice. To maximize my points, I should try to get as many hats and balls as possible while giving Alice at least some books to avoid any proportional distribution that could reduce my earnings significantly. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:32,430][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:32,437][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:32,473][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:32,476][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:32,482][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:32,493][mllm.models.large_language_model_local][WARNING] - Response Given the strategic nature of the game, it's important to consider the potential responses from Alice. Since both our valuations for books and balls are very different, proposing to take all the books and balls could maximize our points by exploiting Alice's lower valuation for these items. Specifically, you value books at 10 and Alice values them at 1, and you value balls at 1 and Alice values them at 10. Thus, a strategic proposal would be to take all 10 books and 10 balls, and split the hats proportionally. Here is the proposal: Proposal: 5 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:32,494][mllm.models.large_language_model_local][WARNING] - Response Given the values, it makes sense to try to maximize the items with higher personal value. Since both you and Bob value hats the least and balls the most, we should attempt to take as many balls as possible while splitting the remaining items fairly. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:32,495][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:32,500][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:32,516][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:32,519][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:32,539][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:32,544][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:32,547][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:32,563][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:32,581][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Bob place a high value on different items. You value hats and balls highly, while you and Bob both value books. To maximize your points, you should propose to take more of the items you value higher, while ensuring the total quantities proposed do not exceed the available items. Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:32,582][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:32,584][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:32,586][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:32,604][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and equal distribution of values, the optimal strategy would be to propose an equal distribution of items. This ensures that both Alice and Bob receive the same amount, maximizing the points based on their respective values for each item. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:32,607][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:32,610][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:32,631][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both hats and books have higher value to you compared to Bob, while balls are valued more by Bob. However, since the values are quite similar, we should aim to maximize our higher-valued items while ensuring the proposal is within the item limits and the sum does not exceed the available quantity. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:32,634][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:32,636][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:32,639][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:32,654][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:32,657][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:32,677][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values where hats are most valuable for you (10) and balls are least valuable (10), while Bob values books and balls higher, I should propose to take as many high-value items as possible. Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:32,681][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:32,684][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:32,687][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:32,702][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:32,705][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:32,707][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:32,859][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:32,861][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:32,865][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:32,868][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:32,971][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:32,974][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:32,979][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:32,996][mllm.models.large_language_model_local][WARNING] - Response Given the nature of Alice's valuations and your own, it's clear that hats and balls have relatively higher value for you compared to books. Conversely, books are more valuable to Alice. To maximize your points, you should propose to take most of the hats and balls, while giving Alice a significant portion of the books. Proposal: 10 hats, 10 books, 10 balls Since this is a repeated pattern where we both propose the same allocation and earn the same points, let's try a new strategy to see if we can improve. We can propose to take all the hats and balls, and split the books proportionally. Proposal: 10 hats, 10 books, 10 balls / 2 = 5 books each did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:33,000][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, you have a higher value for books (10) and Alice has a higher value for hats (10) and books (1). To maximize your points, you should propose to take all of the books and a minimal number of hats and balls to avoid proposing more than the total quantity. Proposal: 1 hats, 10 books, 1 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:33,002][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:33,006][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:33,019][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:33,022][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:33,064][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:33,087][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:33,091][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:33,108][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:33,151][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:33,233][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:33,240][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:33,243][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:33,322][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:33,325][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:33,332][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:33,339][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:33,393][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:33,401][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:33,431][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the high value of balls for both of us, a strategic approach would be to both try to secure the balls while also taking into account the other items. A balanced proposal might be: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:33,435][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:33,442][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:33,449][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:33,456][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:33,467][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:33,473][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:33,480][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:33,526][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:33,529][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:33,610][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:33,614][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:33,694][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:33,698][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:33,701][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:33,704][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:33,781][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:33,785][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:33,864][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:33,946][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:34,028][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, you have a strong preference for hats and books, while Bob values balls highly. To maximize your points, you should propose to take all the hats and books since they have higher individual values for you. This will allow you to capture the majority of the value while still allowing Bob to get the balls. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:34,032][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:34,039][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:34,042][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:34,110][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:34,113][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:34,114][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:34,120][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:34,196][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:34,203][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:34,281][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both hats and balls are valued higher by Bob, and books are valued higher by you. Allocating items based on these values could be effective. Here's a strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:34,350][mllm.models.large_language_model_local][WARNING] - Response Given the values, books are much more valuable to me and balls are more valuable to Bob. To maximize my points, I should try to get as many books as possible since they have the highest personal value to me. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:34,354][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:34,361][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:34,368][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:34,504][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the situation and the equal earning potential when both Alice and Bob propose the same items, a rational approach would be to propose the same split as Bob to ensure a fair and maximal outcome for both parties. Therefore: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:34,509][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:34,585][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it makes sense to attempt to maximize the points by focusing on items where you have a higher value. Here, books and balls have higher values for you compared to Bob. Hats have middling values for both of you. Let's propose a split that maximizes your points. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:34,589][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Alice have high value for hats and books, and lower but similar value for balls. To maximize your points, you should propose to take most of the hats and books since your values for these items are higher. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:34,596][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:34,599][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:34,664][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's advantageous to propose a distribution that aligns with the high value items we both consider important. Since both Alice and I value hats and books highly, and balls less, a strategic proposal would be to take the majority of hats and books, and a fair share of balls. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:34,667][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:34,752][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:34,921][mllm.models.large_language_model_local][WARNING] - Response Given our values and Alice's values, we should aim to maximize our points by proposing a distribution that takes advantage of our higher value for hats and balls, while also considering Alice's higher value for books. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:34,987][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:35,052][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:35,092][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:35,095][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:35,224][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's beneficial to propose a distribution that maximizes the points based on our higher values for books and balls. Here's a strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:35,231][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:35,234][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:35,237][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:35,241][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:35,255][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:35,258][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:35,334][mllm.models.large_language_model_local][WARNING] - Response Given the values, it is clear that both hats and balls are more valuable to me (10 each) compared to books (1). Since Bob values books the most (10), he is likely to claim as many books as possible. To maximize my points, I should propose to take all the hats and balls, leaving Bob with all the books. Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:35,338][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:35,345][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:35,348][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:35,350][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:35,418][mllm.models.large_language_model_local][WARNING] - Response Given Alice's valuation, it's clear that she values books and balls significantly more than hats, while you value hats and balls more than books. To maximize your points, you should propose a distribution that aligns with your higher values while ensuring you don't concede too much to Alice, who would value those items more. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:35,422][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:35,429][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:35,436][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:35,503][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:35,507][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:35,760][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and Bob's strategy, it seems that both Alice and Bob have complementary valuations for the items. To maximize points, we should try to propose a distribution that both Alice and Bob can agree on, ensuring that the total does not exceed the item quantity and that we capture a fair share of the high-value items (balls). Proposal: 10 hats, 10 books, 10 balls This proposal distributes the items evenly, which aligns well with Bob's strategy of valuing hats highly and balls minimally. Here's the rationale: - Alice values hats at 1, books at 10, and balls at 10. - Bob values hats at 10, books at 10, and balls at 1. By proposing to take all the balls, Alice captures the high value for these items while Bob takes all the hats and books. This distribution ensures that both proposals sum to 30 (10+10+10), which is the total number of items, and the items are allocated proportionally based on value. **Proposal: 10 hats, 10 books, 10 balls** did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:35,937][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:35,978][mllm.models.large_language_model_local][WARNING] - Response Given Alice's high value for balls and my own high value for hats and books, it would be strategic to propose a distribution that maximizes my points while considering Alice's preferences. Since the allocation is proportional, I should propose to take most of the hats and books to maximize my points, and leave most of the balls to Alice. Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:36,006][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:36,010][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:36,012][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:36,037][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:36,039][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:36,042][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:36,044][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:36,066][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:36,069][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:36,072][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:36,075][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:36,142][mllm.models.large_language_model_local][WARNING] - Response Given the values where you place high value on hats and balls while Bob values books and hats highly, a strategic proposal would be to allocate items that maximize your total points. Here, you should propose to take all items that have high value for you and split the books since they have moderate value for you and low value for Bob. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:36,377][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both Alice and I value hats highly and balls much less, while books have moderate value for you and no value at all for Alice. To maximize points, we should try to ensure that we get as many hats and balls as possible since these items yield the highest individual values. Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:36,382][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:36,477][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 5 books, 5 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:36,523][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:36,526][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:36,528][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:36,530][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:36,532][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:36,533][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:36,581][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:36,584][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:36,664][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both balls are highly valued by you and hats are highly valued by Bob. To maximize your points, you should try to get as many balls as possible while also securing some hats since they are valued by Bob. Here is a strategic proposal: Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:36,672][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:36,727][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:36,730][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:36,752][mllm.models.large_language_model_local][WARNING] - Response Given Alice's high value for balls and your own high value for hats, a strategic allocation would be to maximize the value of the items you both value highly. A balanced approach might be to allocate more hats and balls to yourself, while leaving a small portion of books for Alice if it helps to secure a larger share of hats and balls. Proposal: 10 hats, 2 books, 8 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:36,755][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:36,758][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:36,761][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:36,768][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:36,776][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:36,779][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:36,781][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:36,782][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:36,785][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:36,803][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:36,805][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:36,807][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:36,831][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Bob place significant value on different items. You value hats and balls highly (10), while Bob values books highly. To maximize your points, you should propose to take all the items you value highly, as Bob will likely take the books. Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:36,837][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the equal likely chance of receiving items valued highly by both parties, a strategic proposal would be to split the items evenly, ensuring that no single agent monopolizes the items valued highly by the other. Thus, a balanced proposal would be: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:36,856][mllm.models.large_language_model_local][WARNING] - Response Given the values, it appears that both you and Alice have a strong preference for books and a much lower preference for balls and hats. In this round, proposing to take all items might lead to a proportional split, which could result in lower points since Alice will likely propose to take the books and hats, leaving you with the balls. To maximize your points, a strategic approach would be to propose a split that aligns with your lower values but still tries to secure at least some items you value. Here's a proposal that balances securing some high-value items while minimizing the risk of a proportional split: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:36,879][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's important to strategically propose to maximize my points. Since I value books at 10 and Alice values them at 1, I should propose to take all the books. Similarly, since Alice values hats at 1 and I value them at 1, I should propose to take all the hats. Balls are valued relatively equally, so I'll propose to take half, assuming Alice will take the other half. Proposal: 10 hats, 10 books, 5 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:36,881][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both you and Alice value books the most at 10, while hats and balls are valued less at 1. Since the values are symmetric for books but asymmetrical for hats and balls, a reasonable strategy is to split the items in a way that maximizes the value for books while ensuring a fair distribution for hats and balls. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:36,884][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Alice have significant value for hats and books but not for balls. Distributing the items equally seems to be the best strategy to maximize points since proposing all items will likely lead to a full allocation if Alice does the same. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:36,885][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and the symmetry in the other rounds, it seems that proposing the maximum number of items for each category maximizes the earnings proportionally to your per-item value. Therefore, I will continue to propose the maximum amount of each item: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:36,926][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:36,948][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:36,951][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:36,954][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:36,958][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:36,961][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:36,964][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:36,996][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:36,999][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:37,002][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:37,020][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, the best strategy would be to maximize the items with higher personal value. Here, hats and books are valued higher by you, and balls are valued higher by Alice. To maximize my points, I should propose the distribution that leverages these values. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:37,027][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:37,029][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:37,031][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:37,033][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:37,035][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:37,048][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:37,050][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:37,070][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Bob value balls the most and hats the least. However, since we are splitting the same number of items and both of us value balls highly, it's crucial to claim as many balls as possible to maximize our points. Here's a strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:37,074][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:37,076][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:37,114][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:37,117][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:37,119][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:37,160][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:37,211][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both you and Alice have a higher value for books and balls compared to hats. However, since the values are the same for hats, splitting them equally is reasonable. This maximizes the total value points for books and balls. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:37,213][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:37,217][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:37,251][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:37,254][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:37,261][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:37,273][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the random distribution of items, proposing an equal split is a reasonable strategy. However, considering your higher value for hats and books, it might be beneficial to slightly tilt the proposal towards those items. Here is a strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:37,279][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:37,282][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:37,284][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:37,287][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:37,293][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:37,310][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:37,315][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:37,320][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:37,329][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:37,379][mllm.models.large_language_model_local][WARNING] - Response Given the high value that you and Bob place on balls (1 and 10 respectively), and the equal value for hats and books (10 and 1 respectively), it's crucial to ensure that we both receive some balls to maximize our points. A cooperative approach might yield the best results, as splitting the balls between us will ensure that both of us get a significant portion of their value. Proposal: 10 hats, 10 books, 5 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:37,383][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:37,401][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:37,404][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:37,406][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:37,408][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:37,423][mllm.models.large_language_model_local][WARNING] - Response Given the values, both you and Alice place high value on hats and books, and lower value on balls. To maximize your points, you should try to secure as many of the items you value higher (hats and books) as possible. Here's a strategic proposal that aims to secure the items with higher individual value for you: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:37,449][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:37,468][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:37,470][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:37,473][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:37,476][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:37,478][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:37,480][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:37,517][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:37,520][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:37,523][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:37,539][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:37,545][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:37,548][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:37,551][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:37,554][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:37,574][mllm.models.large_language_model_local][WARNING] - Response Given the values, it seems that both you and Bob have a high valuation for balls (you value balls at 10, and Bob values them at 10 as well), while both of you value hats and books at the same low level (you at 1 and Bob at 10, but in reverse for books). Given this, a cooperative approach might be beneficial. Since both of us value balls the same and there are 10 of them, a good strategy could be to split the items in a way that we both get a fair share of the high-value items (balls). Here's a proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:37,579][mllm.models.large_language_model_local][WARNING] - Response Given the current per-item values, it appears that both you and Alice place high value on different items. To maximize your points, you should try to ensure that you get the items you value highly. Here, you value hats (1), books (10), and balls (10), while Alice values hats (10), books (1), and balls (10). The best strategy would be to propose an allocation that ensures you get the items you value the most, even if it means Alice gets the items she values the most. Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:37,585][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:37,587][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:37,596][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:37,601][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:37,605][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:37,617][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:37,619][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:37,643][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:37,645][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:37,648][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:37,652][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:37,654][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:37,657][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:37,659][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:37,671][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:37,672][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:37,702][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:37,704][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:37,726][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:37,768][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:37,773][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:37,801][mllm.models.large_language_model_local][WARNING] - Response Given the asymmetric values and to maximize points, a strategic approach would be to ensure that I get the items with higher personal value, while Alice gets the ones she values more. Here, I value hats and balls highly (10), while Alice values books highly (10). Books are valued at 1 by me, so I should avoid proposing any books to myself to prevent Alice from getting them. Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:37,806][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:37,827][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:37,830][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:37,914][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, I should focus on optimizing the proposal to maximize my points. Since I value hats and balls more than books, and Bob values books more, the optimal strategy is to take as many hats and balls as possible, while ensuring the total does not exceed 10. Proposal: 10 hats, 10 balls, 0 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:37,917][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Bob have high value for hats and balls, and both of you value books relatively lower. To maximize our points, we need to consider splitting the books more equitably since they have the second-highest value for both of us. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:37,921][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:37,998][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Alice place high value on different items, and both sets of values sum up to 21. The most strategic approach would be to propose an allocation that maximizes your utility given the values you have. Since Alice values hats the highest and books and balls the least, and you value books and balls the highest, proposing an even split could lead to a proportional allocation that benefits both of you. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:38,002][mllm.models.large_language_model_local][WARNING] - Response Given the values, it is clear that both you and Bob value hats and books very differently. You value hats and books higher than balls, while Bob values balls higher than hats and books. To maximize your points, you should try to ensure that you secure the items you value more highly. Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:38,006][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:38,013][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:38,020][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:38,081][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:38,190][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's important to maximize the points by focusing on items with higher personal value. Since both you and Alice have high values for hats and books, and low values for balls, a strategic proposal would be to maximize the distribution of hats and books while ensuring the allocation is within the item limits. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:38,194][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:38,202][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:38,209][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:38,216][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:38,223][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:38,229][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:38,247][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values between you and Alice, a cooperative strategy where we both propose to take all items seems rational. This ensures that we each get the maximum possible value from our higher valuation items and split lower valuation items equally. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:38,250][mllm.models.large_language_model_local][WARNING] - Response Given the values, both you and Bob have a significant value for balls and a lesser value for hats and books. To maximize points, we should try to secure as many balls as possible while also fetching some hats and books to add to our total. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:38,252][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in per-item values and the previous rounds' outcomes, proposing an even distribution seems optimal. Therefore, I will propose: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:38,256][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:38,261][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:38,266][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:38,273][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:38,278][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:38,283][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:38,288][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:38,299][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric nature of the item values and Bob's predictable behavior, we can propose a strategy that maximizes our points by considering both our and Bob's values. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:38,301][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:38,304][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:38,306][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:38,340][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both you and Bob have a high valuation for hats and books, while balls are less valuable. To maximize your points, it's important to secure as many high-value items as possible. Here's a strategy: propose to take all of the items that you value highly and see if Bob agrees to the proposal proportionally. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:38,342][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:38,347][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:38,397][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:38,400][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:38,486][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:38,493][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:38,500][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:38,503][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:38,506][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:38,571][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:38,574][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:38,659][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:38,661][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:38,917][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Alice value the same items differently. You value hats and balls more, while Alice values books more. To maximize your points, it makes sense to propose taking all the hats and balls for yourself and leaving the books for Alice. Proposal: 10 hats, 10 balls, 0 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:38,920][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:38,922][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:38,923][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:39,063][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:39,067][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:39,070][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:39,073][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:39,138][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both you and Bob value the balls the most, but you value the books and hats less than Bob does. To maximize your points, you should prioritize proposing a higher share of the balls while also considering the books and hats. Here's a strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:39,141][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:39,200][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:39,244][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:39,248][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:39,922][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:39,924][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:39,927][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:39,930][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:39,932][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:39,934][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:39,989][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:40,154][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:40,237][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:40,300][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both you and Bob value the items differently. You value balls the most at 10, followed by hats at 1, and books at 1. Bob values hats the most at 10, followed by balls at 10, and books at 1. Since both of you have different preferences, it's strategic to propose an allocation that aligns with your high value items while anticipating Bob's preferences. Proposal: 10 hats, 10 books, 10 balls This proposal assumes that you and Bob will each take the items you value the most, with the distribution being roughly as follows: - You take all the hats (hats = 10) because you value hats the most. - Bob takes all the balls (balls = 10) because he values balls the most. - You and Bob share the books (books = 10), split proportionally based on your and Bob's valuations. However, since books are valued the same (1) by both of you, it's a shared resource. An equal split is fair and strategic. Proposal: 10 hats, 5 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:40,305][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:40,362][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:40,369][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:40,376][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:40,379][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:40,382][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:40,388][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:40,393][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:40,395][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:40,398][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:40,400][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:40,408][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:40,413][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:40,416][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:40,418][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:40,421][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:40,422][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:40,424][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Alice place high value on hats and books, but the distribution of values is such that you value hats and books more than Alice does, while Alice values balls more than you do. To maximize your points, you should try to get as many hats and books as possible while accepting some balls, even though your value for balls is low. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:40,426][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:40,428][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:40,436][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:40,459][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:40,460][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:40,464][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:40,468][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:40,470][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:40,473][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:40,475][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:40,512][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:40,541][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the previous rounds where proposing to take all items resulted in a fair split, I will make a slightly different proposal to explore if we can achieve a better outcome for myself. Since I value hats and books more than Bob does, and he values balls more, I will propose to take most of the books and balls, while leaving some hats for Bob. Proposal: 5 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:40,562][mllm.models.large_language_model_local][WARNING] - Response Given that Alice values books much higher than you do, and she will likely propose to keep all the books, it makes sense to guard your books and balls carefully. Since you value books and balls the same, and they are worth more to Alice, you should propose to keep at least 10 books and 10 balls for yourself. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:40,605][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:40,627][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:40,631][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:40,634][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:40,636][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:40,640][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:40,692][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the fact that both you and Bob value the items similarly but in an inversely proportional manner, it's important to propose a fair split to maximize the points. Since you value hats and balls highly and books very little, and Bob values books highly and hats and balls less, the best strategy is to propose an even split to ensure neither agent can gain an unfair advantage. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:40,717][mllm.models.large_language_model_local][WARNING] - Response Given the asymmetry in our per-item values, where you value hats and books higher and Alice values balls higher, a strategic proposal might be to capture as much of the high-value items as possible. Here’s a proposal that aims to maximize your points: Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:40,724][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:40,726][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:40,727][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:40,729][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:40,731][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:40,787][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:40,790][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:40,836][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:40,915][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:40,919][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:40,922][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:40,951][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:40,956][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:40,958][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:40,960][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:40,965][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:40,967][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:40,988][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:40,990][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:40,992][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:40,994][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:40,996][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:41,043][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:41,048][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:41,050][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:41,052][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:41,067][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:41,089][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:41,127][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both hats and books are valued equally by both of us but we value balls and Bob values them at different levels. To optimize the distribution, we should try to maximize the value of balls while ensuring we also get a fair share of hats and books. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:41,134][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:41,196][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:41,199][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:41,203][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:41,231][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:41,234][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:41,253][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:41,256][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:41,274][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric preferences and the equal likelihood of item distribution, it's beneficial to propose a fair split that maximizes the total points by leveraging the high value items. Here's the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:41,281][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:41,284][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:41,287][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:41,297][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric per-item values and the fact that both Alice and I value books and balls highly, but hats less so, a cooperative approach might yield better results. Let's split the items evenly to ensure we both maximize our points based on our valuations. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:41,304][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:41,307][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:41,311][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:41,313][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:41,317][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:41,319][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:41,322][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:41,326][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Bob place high value on different items. To maximize points, we need to consider allocating items where one of us has a higher value for them. In this round, you value hats and books highly, while Bob values balls more. Here's a strategic proposal: Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:41,330][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:41,332][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:41,335][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:41,337][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:41,343][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric nature of the values and the previous outcomes, it seems that proposing to take all items is a reasonable strategy, as both you and Alice are likely to propose the same, leading to a proportional split. Therefore, I will continue with the proposal of taking all items. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:41,345][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:41,391][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:41,394][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:41,413][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:41,420][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:41,432][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:41,438][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:41,444][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:41,446][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:41,448][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:41,450][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:41,462][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:41,467][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:41,472][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:41,476][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:41,492][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:41,496][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:41,499][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:41,550][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:41,552][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:41,614][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:41,678][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:41,717][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and Bob's strategy, it seems that both you and Bob value hats and books quite high and balls low. To maximize your points, it's important to secure the high-value items. Proposing to take all the hats and books and splitting the balls could be a good strategy. Proposal: 10 hats, 10 books, 5 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:41,720][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's beneficial to maximize the items with higher personal value while also considering Bob's valuation to avoid having many items backfiring. Here’s a strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:41,724][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:41,731][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:41,738][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:41,748][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:41,754][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:41,762][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:41,764][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:41,791][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:41,796][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:41,798][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:41,819][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:41,846][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:41,852][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:41,858][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:41,864][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:41,869][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:41,872][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:41,902][mllm.models.large_language_model_local][WARNING] - Response Given the values, a strategic proposal would be to maximize the items with higher personal value. Since hats and balls are more valuable to me, I should propose to take a larger share of these items while ensuring the total is within the limit. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:41,905][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:41,912][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:41,918][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:41,921][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:42,084][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:42,088][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:42,331][mllm.models.large_language_model_local][WARNING] - Response Given the values and the history, it seems most strategic to propose an equal split for each item, as both you and Alice value different items, and equal splits have generally led to fair outcomes where both parties earn comparable points. Here is the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:42,339][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:42,342][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:42,346][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:42,349][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:42,352][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:42,355][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:42,380][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:42,384][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:42,386][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:42,398][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, the optimal strategy would be to propose a distribution that maximizes the benefit for the items you value highly. Since you value hats and books more than balls, and Alice values balls more than hats and books, it's strategically beneficial to try to get as many hats and books as possible while letting Alice take most of the balls. Proposal: 10 hats, 10 books, 10 balls However, the previous rounds have shown that proposing equal shares can lead to lower earnings. In this round, let's try to allocate more to the items you value more. Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:42,400][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:42,402][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:42,404][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:42,405][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:42,458][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both hats and balls have higher values for you compared to Bob, while books have a higher value for Bob. To maximize your points, you should prioritize keeping as many hats and balls as possible, while also considering Bob's value for books. Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:42,606][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:42,629][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:42,647][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both you and Bob have a high value for hats (10 each) and a low value for books and balls (1 each). Since the values are symmetric and the total items are equal to the total proposed items, we can propose the following: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:42,653][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and the symmetry in the game, proposing an equal distribution seems to be the most strategic approach to maximize points. Here is the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:42,656][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:42,663][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:42,672][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both you and Bob have a high valuation for hats and balls, and a moderate valuation for books. To maximize points, we should try to get as many items with high value for us as possible. Here's a strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:42,695][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:42,701][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:42,716][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:42,722][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:42,735][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:42,736][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:42,782][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:42,788][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:42,793][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:42,796][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:42,801][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:42,806][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:42,807][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:42,902][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:42,904][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:42,920][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:42,947][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:42,953][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:42,959][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:42,964][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:42,967][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:42,970][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:42,973][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:42,978][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:42,980][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:42,983][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:42,985][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:42,999][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and equal per-item values across different items, proposing an equal split is a reasonable strategy to maximize points. Therefore: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:43,003][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:43,005][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:43,006][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:43,007][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:43,008][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:43,010][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:43,013][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:43,014][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:43,015][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:43,016][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:43,089][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:43,105][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:43,262][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:43,264][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:43,306][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:43,310][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:43,350][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:43,358][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:43,361][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:43,363][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:43,367][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:43,435][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:43,437][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:43,450][mllm.models.large_language_model_local][WARNING] - Response Given the values, it seems that both you and Alice place high value on different items, but your per-item values for books and balls are very close. Here's a strategic proposal to maximize your points: Proposal: 10 hats, 10 books, 10 balls This proposal ensures that you receive a fair share of the items you value highly while also considering the items Alice values highly. However, given the symmetry and the close values, this might lead to a split where both gain equally. To ensure you get a bit more of the items you value highly, you might want to slightly adjust your proposal: Proposal: 10 hats, 11 books, 9 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:43,456][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:43,459][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:43,491][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:43,498][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:43,503][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:43,506][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:43,508][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:43,510][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:43,512][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:43,515][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:43,556][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both Alice and I place high value on hats and books, and low value on balls. To maximize points, we should ensure that we both get a significant share of the items with high value per item. Therefore, the optimal proposal would be to split the items equally between hats and books, while taking all the balls for ourselves. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:43,559][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:43,564][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:43,567][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:43,570][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:43,573][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:43,576][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:43,581][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:43,585][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:43,590][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:43,592][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:43,595][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:43,654][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:44,057][mllm.models.large_language_model_local][WARNING] - Response Given Alice's per-item values (hats=1, books=10, balls=1), it's clear that she will value books more than you do. To optimize our proposal, we should consider taking more of the items that we value higher to achieve a better outcome for us. Proposal: 10 hats, 10 balls, 0 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:44,061][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:44,063][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:44,065][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:44,066][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:44,068][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:44,069][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:44,070][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:44,071][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and Bob's strategy of valuing hats and books highly, proposing a balanced distribution will likely yield the best outcome. Here's the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:44,073][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:44,075][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:44,077][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:44,079][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:44,080][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:44,081][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:44,083][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:44,086][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:44,087][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:44,090][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:44,092][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:44,094][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:44,095][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:44,096][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:44,098][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:44,100][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:44,103][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:44,105][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:44,106][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:44,107][mllm.models.large_language_model_local][WARNING] - Response Given the value asymmetry (hats=10, books=1, balls=1 vs. hats=1, books=10, balls=1), it's beneficial to consider the higher values first. Here's a strategy: propose to take the items that have higher value for you, while ensuring the sum of proposed items does not exceed the total quantity. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:44,110][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:44,112][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:44,115][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:44,117][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:44,120][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:44,123][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:44,126][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:44,127][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:44,128][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:44,129][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:44,131][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the fact that both you and Bob value the balls the least, it might be advantageous to propose an allocation that accounts for mutual gains, especially since the values for hats and books are significant for both of you. Here is a proposed allocation: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:44,132][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's crucial to maximize points by considering the higher values for books and balls, while still offering a fair split for hats. Here's the optimized proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:44,152][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in per-item values and the results from previous rounds, it seems that both Alice and I have incentive to propose the exact distribution of items each time, as it leads to equal division and maximizes points based on our respective item valuations. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:44,412][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Alice have significant value for hats and books but different preferences for balls. Since the previous optimal strategy was to take all items, let's reassess the situation. Your per-item values are hats=10, books=10, balls=1. Alice’s per-item values are hats=10, books=1, balls=10. If you propose to keep all 10 hats and 10 books, Alice will likely propose to keep all 10 balls. Since the values are high for hats and books for both, and low for balls, splitting the items evenly might lead to a better proportionate allocation. Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:44,465][mllm.models.large_language_model_local][WARNING] - Response Given the values, it seems both you and Alice value hats and books highly, but value balls less. To maximize your points, you should propose keeping more of the items you value higher. Here's a strategy to consider: - Since you value hats and books significantly more than Alice, propose keeping more hats and books. - Since you value balls the least and Alice values them highly, propose keeping as many balls as possible. A balanced proposal might be: Proposal: 10 hats, 10 books, 10 balls However, to further leverage your advantage in hats and books, you might propose a slightly different distribution: Proposal: 8 hats, 8 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:44,470][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:44,473][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:44,476][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:44,480][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:44,483][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:44,485][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:44,488][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:44,504][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:44,506][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:44,510][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:44,515][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:44,521][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:44,526][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:44,532][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:44,535][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:44,538][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:44,541][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:44,565][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:44,569][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:44,572][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:44,573][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:44,575][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:44,577][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:44,580][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:44,582][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:44,583][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:44,585][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:44,789][mllm.models.large_language_model_local][WARNING] - Response Given the asymmetric per-item values, we should consider proposing a split that reflects their respective values. Since you value hats and balls higher than Bob, and Bob values books more, a fair proposal would be to allocate items based on their relative preferences. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:44,883][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:44,890][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:44,901][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:44,908][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:44,915][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:44,921][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:44,926][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:44,929][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:44,933][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:44,935][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:44,937][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:44,943][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:44,951][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:44,954][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:44,958][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:44,962][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:44,963][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:44,964][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:44,966][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:44,967][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:44,991][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:44,994][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:44,995][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:44,998][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:45,000][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:45,002][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:45,003][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:45,004][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:45,005][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:45,006][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:45,008][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:45,009][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:45,010][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:45,048][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both hats and books are highly valued by you and Alice, while balls are of low value to you but highly valued by Alice. To maximize your points, you should propose to take all the items that you value highly, while giving Alice the balls since she values them more. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:45,236][mllm.models.large_language_model_local][WARNING] - Response Given the current dynamics and the distribution of values, it might be more strategic to try to maximize the value of the items that I value highly (books and balls) while respecting the proportional allocation rule. Here's a proposed strategy: - I value books and balls more, so I should try to secure as many of those as possible. - Hats are not as valuable for me, and I should limit the number of hats I propose to avoid splitting too many items that are less valuable to me. Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:45,318][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:45,321][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:45,363][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:45,367][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:45,371][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:45,373][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:45,376][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:45,379][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:45,381][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:45,469][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:45,476][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:45,483][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:45,490][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:45,497][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:45,500][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:45,502][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:45,504][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:45,507][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:45,509][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:45,511][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:45,512][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:45,514][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:45,516][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:45,570][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:45,739][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both you and Alice have a high value for balls (10) and a low value for hats (1) and books (1). Proposing to take all items will likely result in a proportional split if Alice also proposes to take all items. However, to ensure we capture as much value as possible, let's propose to take all the items. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:45,791][mllm.models.large_language_model_local][WARNING] - Response Given our similar per-item values, we can anticipate that proposing an equal split will ensure we both receive fair allocations. Here is my proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:45,841][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and equal value distribution, it's strategic to propose an equal split again. This approach ensures that we maximize the points regardless of Bob's strategy. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:45,890][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:45,928][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:45,931][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:45,934][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:45,937][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:45,941][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:45,947][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:45,952][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:45,958][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:45,964][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:45,967][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:45,970][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:45,973][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:45,979][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:45,984][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:45,989][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:45,994][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:45,999][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:46,006][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:46,011][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:46,013][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:46,016][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:46,026][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's evident that both hats and balls have a higher value for Bob, while books have a higher value for you. To maximize your points, you should propose to take as many books as possible since they have the highest value for you. Here is a strategic proposal: Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:46,079][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric distribution of values and the fact that both we and Bob value balls the least at 1, and hats and books the most, it seems a zero-sum distribution might be optimal. However, to maximize our points, we should aim to obtain the items we value the most, which are hats and books. Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:46,127][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the values and the fact that both you and Alice have a strong preference for hats and books respectively, while balls are valued similarly but less by both, a strategic proposal would be to mirror each other's preferences while ensuring the total quantity is met. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:46,221][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:46,250][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the values and the previous outcomes, it seems equitable to propose an allocation that reflects the values of both items and ensures both Alice and Bob receive a fair share. A strategic approach would be to propose an allocation that both maximizes points and considers the proportional value of each item to each player. Proposal: 10 hats, 10 books, 10 balls This proposal maintains the symmetry and ensures that both players benefit equally. However, considering the significant value of "books" and "balls" for both players, we can slightly adjust the proposal to better reflect these values. Proposal: 5 hats, 10 books, 10 balls This proposal gives more emphasis to the items with higher cumulative values for both players, while still maintaining a fair distribution. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:46,253][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:46,257][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:46,268][mllm.models.large_language_model_local][WARNING] - Response Given the pattern of equal proposals and equal earnings, it appears that both Alice and I are benefitting from splitting all items equally. This strategy ensures that we each receive the maximum possible value based on our per-item values. Therefore, I will continue to propose splitting all items equally. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:46,272][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:46,277][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:46,290][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:46,293][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:46,296][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:46,334][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:46,338][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:46,344][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:46,348][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:46,351][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:46,367][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric nature of the situation where both you and Bob have similar valuation for the items, and aiming to maximize your points, I will propose to allocate the items in a way that reflects their relative values. Here's the proposal: Proposal: 10 hats, 10 books, 10 balls This proposal assumes that the allocation of 10 of each item maximizes the value given the similar but slightly different valuations of the items by both parties. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:46,403][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Alice have similar but not identical preferences. You value hats and books highly (10), while Alice values hats and balls highly (10), and books least (1). Proposing to split all items equally seems to be a balanced approach, as it maximizes the use of the items without overestimating the value of any particular item for either party. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:46,448][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:46,493][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric nature of the per-item values and the fact that both you and Bob value the items in a way that prioritizes balls and slightly heterogeneously for hats and books, a rational approach would be to propose an equal split that maximizes the value for both hats and books while securing the maximum value for balls. ProposAl: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:46,536][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:46,567][mllm.models.large_language_model_local][WARNING] - Response Given the values, it seems optimal to maximize the points from the items you value highly. Since you value hats and balls the most at 10 and 10 respectively, and books the least at 1, a proposal that captures the higher values would be: Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:46,574][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:46,584][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:46,601][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:46,609][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:46,613][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:46,617][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:46,619][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:46,621][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:46,623][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:46,625][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:46,627][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:46,660][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's in your best interest to maximize the items with higher personal value while also considering Alice's valuation. Here, you value hats and balls at 1 and 10 respectively, and Alice values books and balls highly. Given your values, proposing to take as many hats and balls as possible, and splitting the books could be a good strategy. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:46,680][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:46,683][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:46,686][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:46,689][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:46,882][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both hats and books are highly valued more by Bob, and balls are more valuable to you. To maximize your points, you should propose to take all the balls and split the hats and books in a way that minimizes Bob's gain from those items. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:46,910][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both hats and balls are more valuable to you and Bob, respectively. To maximize your points, you should aim to get as many of the balls as possible since they are worth the highest value to you. Here’s a strategic proposal: Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:46,918][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:46,964][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:46,966][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:46,969][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:46,971][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:46,974][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:46,977][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:46,989][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:47,010][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:47,015][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:47,020][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:47,023][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:47,026][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:47,110][mllm.models.large_language_model_local][WARNING] - Response Given the values, I should aim to get the items I value highly (books and balls) while ensuring the proposal does not exceed the available items. Here is the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:47,122][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:47,123][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:47,216][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:47,282][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that hats and books are highly valued by both Alice and Bob, while balls are valued less. However, since both Alice and Bob value hats and books more than the other does, proposing to take all the hats and books could be effective. Let's focus on taking the majority of the hats and books while also claiming some balls to maximize the points. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:47,286][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:47,293][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:47,300][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:47,307][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:47,313][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:47,318][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:47,331][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:47,333][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:47,350][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:47,355][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:47,403][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both you and Alice value books the most. However, since we are proposing simultaneously and aiming to maximize our points, splitting the items equally might not be the best strategy. Here, you value books the most (10), and Alice also values books the most (10), but you value balls the most (10), and Alice values hats the most (10). A strategic approach would be to propose to take all the items you value the most to maximize your points, while acknowledging that Alice will likely do the same. Given this, a reasonable proposal would be to take all the items you value the highest: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:47,406][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the equal distribution of points in previous rounds, I will propose the same allocation to ensure an equal split that maximizes my points while also challenging Bob to react. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:47,446][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric nature of the game and the values of the items, a balanced proposal is likely to yield the best result. Distributing the items equally should provide a fair split and maximize the points considering both of our valuation patterns. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:47,449][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:47,460][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:47,463][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:47,529][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, the optimal strategy would be to propose shares that reflect the high value of hats and books for yourself and the high value of books and balls for Alice. Since both of you value hats and books highly, it's crucial to secure as many of these items as possible. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:47,532][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both hats and books are more valuable to Alice, while balls are more valuable to you. To maximize your points, you should propose a distribution that takes advantage of this disparity. Here’s a strategic proposal: Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:47,559][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:47,574][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:47,577][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:47,588][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:47,591][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:47,620][mllm.models.large_language_model_local][WARNING] - Response Given the values, it seems that both hats and books are highly valued by both Alice and me, while balls are valued less. Since we both value hats and books significantly, it's in my best interest to secure as many of these items as possible. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:47,626][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:47,629][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:47,632][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:47,642][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:47,648][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:47,650][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:47,714][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:47,727][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:47,734][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:47,818][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:47,830][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it is clear that both you and Alice place high value on hats and books, with balls being the least valuable. To maximize your score, you should propose to take most of the hats and books, while sharing the balls. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:47,833][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:47,837][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:47,846][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:47,850][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:47,875][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:47,879][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:47,882][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:47,901][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:47,904][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:47,907][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:47,909][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:48,048][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, the optimal strategy is to propose a split that maximizes the points based on your own values. Since you value hats and books at 1 and balls at 10, and Bob values balls at 1, it makes sense to propose keeping all the balls for yourself to maximize your points. Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:48,101][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:48,106][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:48,113][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:48,124][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both hats and books have higher value for Bob, while balls have higher value for you. To maximize your points, you should propose keeping all the balls and splitting the hats and books in a way that maximizes your share of books while leaving some hats for Bob to avoid a proportional split that might not be favorable. Proposal: 5 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:48,128][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in per-item values and the fact that both you and Bob have the same total value for the items, a cooperative and competitive strategy would be to propose an equal split to ensure both receive a fair share, maximizing the points for both agents. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:48,131][mllm.models.large_language_model_local][WARNING] - Response Given our per-item values and Alice's, it makes sense to propose splitting the items equally to ensure we maximize our total points. Here is the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:48,134][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:48,140][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:48,148][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:48,151][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:48,162][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:48,164][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:48,177][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:48,182][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:48,188][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:48,192][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:48,256][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:48,269][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:48,296][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:48,347][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:48,372][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:48,375][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:48,394][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:48,400][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:48,410][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:48,413][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:48,532][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:48,546][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:48,640][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:48,654][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:48,658][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:48,675][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it seems that both hats and books are valued very highly by both Alice and I, while balls are valued much less. A prudent strategy would be to maximize the items that have higher individual values for both of us. Here, proposing to take all 10 hats and 10 books while dividing the balls could be a good approach. Proposal: 10 hats, 10 books, 5 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:48,679][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:48,685][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:48,697][mllm.models.large_language_model_local][WARNING] - Response Given the values and Bob's strategy, it's clear that proposing an equal split generally leads to a fair outcome where both Alice and Bob earn 60 points each. However, we can try to exploit Bob's valuation to maximize our earnings. Since Bob values hats and books equally and very high, and balls very low, we can propose a split that takes advantage of this. Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:48,727][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:48,730][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:48,765][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:48,778][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:48,884][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:48,897][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:48,900][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:48,922][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in both our per-item values and the fact that the total number of items is the same, proposing an equal share of each item seems to be a fair and strategic approach. Therefore: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:48,946][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:49,123][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:49,127][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:49,160][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:49,163][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:49,177][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:49,318][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Bob have significantly higher value for books (10 each) compared to hats (1 for you, 10 for Bob) and balls (1 for you, 10 for Bob). Since both of you have the same values for hats and balls, and the items are split equally when proposals exceed the available items, a cooperative strategy might be beneficial. To maximize points, we should allocate the items such that both Alice and Bob get a good share of the high-value books. Here is a proposal that considers both individual and cooperative strategies: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:49,330][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:49,337][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:49,356][mllm.models.large_language_model_local][WARNING] - Response Given the values, proposing an equal split seems like a fair and strategic move. Here’s the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:49,367][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:49,369][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:49,386][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:49,390][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:49,401][mllm.models.large_language_model_local][WARNING] - Response Given the high value I place on hats and balls (10 each), while Bob values books more, a cooperative approach might yield the best results. Let's propose a distribution that maximizes our combined value. Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:49,450][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and equal per-item values for all items between you and Bob, proposing an equal distribution will likely result in the highest earnings for both parties. Therefore, a fair and optimal proposal would be: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:49,486][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both hats and books have higher value for you compared to balls. Alice values balls the most. To maximize your points, it's beneficial to propose keeping most of the hats and books for yourself, while allowing Alice to take most of the balls. Proposal: 10 hats, 10 books, 10 balls However, since the allocation might still be split if the sum exceeds the total items, to fully exploit the high value of hats and books for you, you can propose slightly less than 10 hats and books to avoid any potential splits that would reduce your points. Proposal: 9 hats, 9 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:49,588][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:49,595][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:49,603][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:49,607][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:49,611][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both you and Alice value hats and books similarly but have different preferences for balls. To maximize your points, you should focus on claiming as many of the items you value highly (hats and books) while preventing Alice from getting all the balls. Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:49,618][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:49,664][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:49,809][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:49,822][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:50,057][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Bob place high value on books and balls, while both place a low value on hats. Distributing the items equally is a fair strategy based on the values. However, to maximize points, we should try to capture as many items with high value as possible. Here's a proposed strategy: - You value books and balls highly, so you should try to get as many of these items as possible. - Since Bob values balls less than you do, he might be more willing to give you balls in exchange for hats or books. A balanced but slightly biased proposal could be: Proposal: 10 hats, 10 books, 10 balls This proposal ensures that both of you get an equal share of the items, which is fair and maximizes the likelihood of both of you getting a reasonable amount of high-value items. However, if we assume Bob is strategic and might try to get more balls, we might need to slightly adjust the proposal to get more books and balls while ensuring hats are distributed as well. To play it safe and ensure a balanced outcome, let's propose: Proposal: 7 hats, 10 books, 10 balls This way, you get more of the high-value items (books and balls) and ensure both you and Bob get some hats. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:50,215][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:50,226][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:04:50,276][mllm.models.large_language_model_local][WARNING] - Response Given our per-item values and Alice's, it's clear that both of us value hats and books much higher than balls. Splitting hats and books evenly seems to be a fair and optimal strategy based on the values each of us has assigned. Here's the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:50,299][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric nature of the values and the previous outcomes where proposing to take all items led to an equal split, a strategic approach would be to consider the value ratio between the items and ensure a fair distribution that also accounts for the higher value of balls. Here, balls are the most valuable item for both of us, but distributing them equally might not be optimal if the total quantity is limited. Proposal: 10 hats, 10 books, 10 balls This proposal ensures that we take all items, which historically has led to a fair and positive outcome for both players. Let's stick with this proposal to ensure we maximize our points given the current values. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:50,404][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:50,416][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:04:50,584][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:50,596][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:50,934][mllm.models.large_language_model_local][WARNING] - Response Given that both you and Bob value the items differently and that there is potential for asymmetrical value, a strategic approach would be to propose an allocation that reflects the true value to you. Since hats and books have significant value to you and balls have high value to Bob, it would be wise to propose keeping more of the items that you value highly. Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:04:51,686][__main__][INFO] - Number of regex retries in iteration 571: 2782 [2025-11-13 07:04:51,687][__main__][INFO] - agents played in iteration 571 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 07:04:52,651][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 07:04:52,676][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 07:04:52,701][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 07:04:52,734][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.56%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 07:04:52,734][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 07:04:52,734][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 07:04:53,549][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 07:04:54,004][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 07:04:54,508][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 07:04:55,009][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 07:04:55,501][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 07:04:55,991][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 07:04:56,480][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 07:04:56,987][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 07:04:57,477][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 07:04:57,969][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 07:04:58,462][mllm.training.trainer_common][INFO] - 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Processing mini-batch 54 of 64 [2025-11-13 07:05:20,705][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 07:05:21,202][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 07:05:21,702][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 07:05:22,198][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 07:05:22,704][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 07:05:23,203][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 07:05:23,701][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 07:05:24,207][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 07:05:24,706][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 07:05:25,205][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10916 tokens. [2025-11-13 07:05:26,058][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.23%, Current % of VRAM taken: 59.68%, Block Peak % of device VRAM: 62.38%, ΔTime: 00:00:32 [2025-11-13 07:05:26,730][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 07:05:26,732][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 07:05:26,734][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 07:05:27,524][__main__][INFO] - Iteration 572 took 1m 41s (64.60% Gen, 34.62% Train). Generation: 1m 5s, Training: 35s. Estimated remaining time: 75h 25m 54s. Estimated total time: 84h 21m 39s. Time estimates for 10 more iterations: 16m 52s, 100 more iterations: 2h 48m 43s, 500 more iterations: 14h 3m 36s. [2025-11-13 07:05:27,526][__main__][INFO] - Starting iteration 572. [2025-11-13 07:05:28,024][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 57 and human policies 1. [2025-11-13 07:05:28,025][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 07:05:43,708][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:43,748][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:43,749][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:43,793][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:43,809][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:43,821][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:43,833][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:43,844][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:43,876][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:43,884][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:43,896][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:43,904][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:43,905][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:43,923][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:43,931][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:43,947][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:43,959][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:43,967][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:43,967][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:43,968][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:43,968][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:43,991][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:43,996][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:44,004][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:44,012][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:44,020][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:44,024][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:44,055][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:44,093][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:44,105][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:44,109][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:44,110][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:44,110][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:44,128][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:44,141][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:44,149][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:44,165][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:44,169][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:44,173][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:44,173][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:44,174][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:44,174][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:44,198][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:44,206][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:44,218][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:44,230][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:44,241][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:44,250][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:44,250][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:44,251][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:44,251][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:44,268][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:44,272][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:44,272][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:44,289][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:44,301][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:44,301][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:44,302][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:44,302][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:44,365][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:44,388][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:44,399][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:44,428][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:44,436][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:44,439][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:44,440][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:44,440][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:44,441][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:44,441][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:44,488][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:44,504][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:44,516][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:44,516][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:44,517][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:44,568][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:44,579][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:44,579][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:44,580][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:44,580][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:44,619][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:44,636][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:44,644][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:44,652][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:44,652][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:44,653][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:44,695][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:44,715][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:44,745][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:44,770][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:44,801][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:44,832][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:44,865][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:45,061][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:45,063][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:45,063][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:45,064][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:45,064][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:45,067][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:45,068][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:45,069][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:45,069][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:45,070][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 1 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:45,070][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:45,072][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:45,075][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:45,076][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:45,077][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:45,081][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:45,086][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:45,087][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:45,091][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:45,092][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:45,092][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:45,092][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:45,093][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:45,093][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:45,106][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:45,109][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:45,111][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:45,123][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:45,132][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:45,132][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:45,133][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:45,133][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:45,135][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:45,137][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:45,137][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:45,138][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:45,138][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:45,139][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:45,139][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:45,142][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:45,142][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:45,143][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:45,145][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:45,146][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:45,146][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:45,148][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:45,148][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:45,149][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:45,151][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:45,152][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:45,158][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:45,161][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:45,162][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:45,164][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:45,166][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:45,167][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:45,168][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:45,170][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:45,171][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:45,172][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:45,173][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:45,175][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:45,176][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:45,177][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:45,177][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:45,180][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:45,181][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:45,182][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:45,184][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:45,185][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:45,186][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:45,186][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:45,189][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:45,189][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:45,199][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:45,200][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:45,200][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:45,201][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:45,201][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:45,202][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:45,202][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:45,203][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:45,205][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:45,207][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:45,207][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:45,208][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:45,208][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:45,210][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:45,211][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:45,212][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:45,212][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:45,215][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:45,360][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:45,375][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:45,378][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:45,380][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:45,384][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:45,385][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:45,390][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:45,393][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:45,394][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:45,396][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:45,398][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:45,400][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:45,402][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:45,405][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:45,407][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:45,414][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:45,416][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:45,418][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:45,420][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:45,423][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:45,425][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:45,428][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:45,430][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:45,432][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:45,433][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:45,435][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:45,437][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:45,441][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:45,443][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:45,445][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:45,447][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:45,449][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:45,452][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:45,454][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:45,457][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:45,459][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:45,462][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:45,464][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:45,467][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:45,468][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:45,470][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:45,472][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:45,474][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:45,476][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:45,478][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:45,480][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:45,482][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:45,484][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:45,486][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:45,488][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:45,490][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:45,492][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:45,493][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:45,495][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:45,497][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:45,502][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:45,504][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:45,505][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:45,507][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:45,509][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:45,511][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:45,513][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:45,515][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:45,517][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:45,519][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:45,520][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:45,522][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:45,524][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:45,526][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:45,528][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:45,530][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:45,532][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:45,534][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:45,536][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:45,538][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:45,542][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:45,544][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:45,545][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:45,547][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:45,549][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:45,551][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:45,553][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:45,555][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:45,557][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:45,559][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:45,560][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:45,562][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:45,564][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:45,566][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:45,568][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:45,570][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:45,572][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:46,705][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:46,729][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:46,732][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:46,735][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:46,738][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:46,753][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:46,755][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:46,758][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:46,761][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:46,763][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:46,766][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:46,769][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:46,778][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:46,781][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:46,784][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:46,786][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:46,788][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:46,791][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:46,793][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:46,799][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:46,801][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:46,802][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:46,804][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:46,806][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:46,807][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:46,808][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:46,809][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:46,810][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:46,836][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:46,837][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:46,839][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:46,841][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:46,843][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:46,845][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:46,846][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:46,847][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:46,916][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:46,918][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:46,921][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:46,924][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:46,926][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:46,928][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:46,930][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:46,931][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:46,932][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:46,963][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:46,965][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:46,967][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:46,968][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:46,969][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:46,970][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:46,971][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:46,972][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:46,973][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:46,974][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:46,975][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:47,027][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:47,030][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:47,032][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:47,035][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:47,037][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:47,040][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:47,042][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:47,045][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:47,048][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:47,049][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:47,051][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:47,069][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:47,072][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:47,074][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:47,076][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:47,079][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:47,082][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:47,085][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:47,087][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:47,096][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:47,099][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:47,101][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:47,104][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:47,106][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:47,109][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:47,111][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:47,114][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:47,116][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:47,117][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:47,120][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:47,128][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:47,131][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:47,132][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:47,292][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:47,308][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:47,310][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:47,313][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:47,314][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:47,344][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:47,348][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:47,350][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:47,353][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:47,362][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:47,365][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:47,368][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:47,370][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:47,372][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:47,375][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:47,378][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:47,380][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:47,388][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:47,390][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:48,297][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:48,300][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:48,303][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:48,306][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:48,309][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:48,310][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:48,312][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:48,313][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:48,314][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:48,355][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:48,357][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:48,359][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:48,361][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:48,362][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:48,363][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:48,364][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:48,366][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:48,428][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:48,430][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:48,433][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:48,435][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:48,438][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:48,440][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:48,443][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:48,445][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:48,447][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:48,497][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:48,500][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:48,501][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:48,503][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:48,506][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:48,508][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:48,511][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:48,514][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:48,516][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:48,519][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:48,521][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:48,579][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:48,581][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:48,584][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:48,587][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:48,590][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:48,592][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:48,593][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:48,594][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:48,595][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:48,598][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:48,601][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:48,633][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:48,637][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:48,670][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:48,673][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:48,676][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:48,680][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:48,683][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:48,686][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:48,715][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:48,719][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:48,722][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:48,724][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:48,726][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:48,731][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:48,734][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:48,737][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:48,740][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:48,743][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:48,997][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:49,017][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:49,021][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:49,024][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:49,027][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:49,059][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:49,062][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:49,066][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:49,098][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:49,101][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:49,104][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:49,106][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:49,113][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:49,115][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:49,116][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:49,117][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:49,117][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:49,118][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:49,118][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:49,119][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:49,119][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:49,121][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:49,122][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:49,123][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:49,124][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:49,125][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:49,126][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:49,280][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:49,915][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both hats and books are highly valued by me, while balls are highly valued by Bob. A strategic proposal would be to maximize the items that have high value for me while also taking into account the proportional allocation rule. Propposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:50,044][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:50,046][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:50,049][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:50,051][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:50,138][mllm.models.large_language_model_local][WARNING] - Response Given Alice's revised per-item values, it is more beneficial to maximize the allocation of hats and balls while keeping a few books for balance. Here's the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:50,141][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:50,145][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:50,148][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:50,151][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:50,153][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:50,155][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:50,157][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:50,221][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:50,225][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:50,227][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:50,229][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:50,232][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:50,235][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:50,236][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:50,237][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:50,240][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:50,291][mllm.models.large_language_model_local][WARNING] - Response Given the new per-item values, where my value for books has dropped to 10 and Alice’s value for balls has increased to 10, I should adjust my strategy accordingly. Since both of us value books more than hats or balls, we might be better off focusing on getting as many books as possible. Here is my proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:50,294][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:50,297][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:50,300][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:50,303][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:50,306][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:50,308][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:50,337][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:50,339][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:50,362][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:50,363][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:50,366][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:50,368][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:50,371][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:50,374][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:50,389][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:50,392][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:50,395][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:50,397][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:50,398][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:50,408][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:50,410][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:50,550][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:50,575][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:50,577][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:50,580][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:50,583][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:50,603][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:50,607][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:50,610][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:50,623][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:50,648][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:50,651][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:50,654][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:50,657][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:50,660][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:50,663][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:50,666][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:50,669][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:50,671][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:50,687][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:50,689][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:50,691][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:50,693][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:50,695][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:50,697][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:50,698][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:50,714][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:50,716][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:50,775][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:50,777][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:50,778][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:50,780][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:50,781][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:50,783][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:50,863][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:50,947][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:50,951][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:50,954][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:50,958][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:50,961][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:50,963][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:50,971][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:50,974][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:50,977][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:50,980][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:50,983][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:50,990][mllm.models.large_language_model_local][WARNING] - Response Given the updated values, where your preference for balls is significantly higher than Bob's, it makes sense to offer more balls to maximize your points. Let's propose a strategy where we try to maximize the number of balls we receive while still considering the distribution of hats and books. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:50,993][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:50,994][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:51,104][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:51,125][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:51,128][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:51,130][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:51,132][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:51,142][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:51,143][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:51,144][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:51,160][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:51,186][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:51,188][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:51,208][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:51,210][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:51,214][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:51,216][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:51,219][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:51,222][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:51,258][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:51,260][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:51,262][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:51,306][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:51,308][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:51,310][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:51,329][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:51,336][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:51,341][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:51,391][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:51,436][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:51,443][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:51,463][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:51,470][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:51,475][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:51,482][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:51,513][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:51,519][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:51,522][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:51,529][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:51,673][mllm.models.large_language_model_local][WARNING] - Response Given the updated values, it seems Bob places a much higher value on hats and balls, and a much lower value on books, while you place a much higher value on books and a moderate value on hats and balls. In this round, proposing to take all the books and a moderate amount of hats and balls might maximize your points. Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:51,694][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:51,717][mllm.models.large_language_model_local][WARNING] - Response Given the updated per-item values, where you value books at 10 and Alice values them at 1, and vice versa, a strategic proposal would be to grab as many books as possible while also securing some hats and balls to avoid splitting the books. Proposal: 5 hats, 10 books, 5 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:51,720][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:51,727][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:51,734][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:51,739][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:51,763][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:51,769][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:51,776][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:51,805][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:51,826][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:51,832][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:51,867][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:51,872][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:51,878][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:51,884][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:51,912][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:51,933][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the high value Bob places on books and balls, while you place a high value on books and balls as well, it might be beneficial to cooperate and split the items equally to avoid any potential loss from proportional distribution. Here’s a balanced proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:51,936][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:51,940][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:51,943][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:51,945][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:51,948][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:51,954][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:51,959][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:51,961][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:51,963][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:51,966][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:52,281][mllm.models.large_language_model_local][WARNING] - Response Given the values where you have a higher valuation for hats and Bob has a higher valuation for books, we need to propose a distribution that maximizes our points. Since both of us are proposing the same amounts and the scores are the same, let's consider proposing a slightly different distribution to try to gain an advantage. Proposal: 10 hats, 10 books, 9 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:52,339][mllm.models.large_language_model_local][WARNING] - Response Given the new values, since you have a high value for balls (10) and Bob has a high value for hats (10), it might be more strategic to focus on maximizing the number of items you value highly while still considering Bob's valuation. Here’s a proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:52,649][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and Bob's strategy, it appears that both of us are valuing hats and books highly and balls less. To maximize points, we should propose to take the full quantity of items we value highly while ensuring the total proposals do not exceed the item quantities. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:52,698][mllm.models.large_language_model_local][WARNING] - Response Given the updated per-item values, where your value for balls is significantly higher (10) compared to both hats and books (1 each), and Bob's value for hats is significantly higher (10) compared to both books and balls (1 each), it would be rational to maximize the number of balls you receive while ensuring you get at least a minimum from hats and books. A reasonable proposal would be to take almost all of the balls, while splitting the hats and books fairly to avoid proposing more than the total quantity. Proposal: 5 hats, 5 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:52,741][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in our per-item values and Alice's, it is strategic to propose an allocation that maximizes our own value while being reasonable. Since we both have high values for hats and balls, and low values for books, it makes sense to propose an allocation that prioritizes these items. Proposal: 10 hats, 10 balls, 0 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:52,799][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:52,802][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:52,893][mllm.models.large_language_model_local][WARNING] - Response Given Alice's new per-item values, she now highly values books and slightly values balls and hats. Since my value for books is much higher than Alice's, I can try to maximize my points by proposing to take all the books and a portion of the hats and balls. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:52,982][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the fact that both you and Alice have similar valuations for the items, the optimal strategy is to propose an allocation that reflects these valuations and maximizes your points. Since both of your valuations for hats and books are aligned, and you both value balls highly, proposing to take all the items is a reasonable strategy. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:52,986][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:53,023][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:53,026][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:53,029][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:53,032][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:53,063][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:53,067][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:53,069][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:53,112][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:53,155][mllm.models.large_language_model_local][WARNING] - Response Given Bob's higher valuation of hats and books, and your higher valuation of balls, a strategic proposal might involve taking more of the items that Bob values less. However, since both of you are likely to propose the same amounts to avoid splitting items, a cautious approach might be to propose slightly less of the items that Bob values highly to ensure you get at least some of the items you value. Proposal: 10 hats, 10 books, 9 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:53,180][mllm.models.large_language_model_local][WARNING] - Response Given the new per-item values, where you value hats and books at 10 and balls at 1, and Bob values hats at 1, books at 10, and balls at 1, it would be strategically beneficial to maximize the high-value items (hats and books) while ensuring you don't overpropose and get allocated proportionally if Bob's proposal slightly exceeds yours. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:53,183][mllm.models.large_language_model_local][WARNING] - Response Given the updated per-item values where both you and Bob value hats and books at 10 and balls at 1, a more strategic approach might be necessary to maximize points. Since you both value balls the same, there's less incentive to compete for them directly. Here's a revised proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:53,234][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:53,253][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:53,276][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:53,280][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:53,283][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:53,287][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:53,290][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:53,318][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:53,320][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:53,322][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:53,361][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that hats and books are more valuable to me, while balls are more valuable to Bob. Proposing to take all the balls and split the hats and books might be a strategic move. Here is the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:53,364][mllm.models.large_language_model_local][WARNING] - Response Given the symmetrical nature of the per-item values and the similarity in Alice's and your proposals in previous rounds, it's clear that splitting the items equally works well for both parties. Therefore, I will continue to propose splitting the items evenly. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:53,371][mllm.models.large_language_model_local][WARNING] - Response Given Alice's different valuations, where she values hats and balls much higher than books, and knowing that I value books and balls more, it makes sense to adjust the proposal to maximize my points. Here's a revised proposal: Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:53,377][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:53,386][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:53,419][mllm.models.large_language_model_local][WARNING] - Response Given the new per-item values where you value hats and books at 1 and balls at 10, while Alice values hats at 10, books at 1, and balls at 1, it's clear that both of us place significant value on different items. To maximize my points, I should try to secure as many books and balls as possible, while understanding that Alice will likely want to take as many hats as she can. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:53,423][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:53,425][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:53,439][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:53,464][mllm.models.large_language_model_local][WARNING] - Response Given the updated values, where I value hats and books much higher than balls, and Alice values balls much higher than hats and books, it makes sense to propose a distribution that maximizes my value for the items I prefer. Here's the proposal: Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:53,471][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:53,474][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:53,490][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:53,494][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:53,496][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:53,499][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:53,501][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:53,504][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:53,506][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:53,509][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:53,553][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that books are highly valuable for Alice, while hats are highly valuable for you. To maximize your points, you should propose to take all the hats and books, while Alice will likely take all the balls. Here is the optimal proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:53,599][mllm.models.large_language_model_local][WARNING] - Response Given the values, I should prioritize hats and balls since they have higher value for me, and also consider that Bob values books higher. A strategic allocation might be to take more of the items I value highly while leaving some for Bob to ensure he doesn't completely dominate the books. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:53,602][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Bob value hats and books at 10 and balls at 1. A fair and optimal strategy would be to ensure that we both receive an equal share of the items while respecting the per-item values. Here's the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:53,606][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:53,788][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric nature of the game and the fact that Bob's value for hats (10) is the same as your value for books and balls (10), and your value for books is much higher than Bob's value for books, we can propose an allocation that maximizes our utility. Since we both have the same value for hats, and we both value books and balls highly, we can propose an equal split of all items to ensure fairness and maximize our points. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:53,853][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values where hats are worth 1 to you and books are worth 10, while Bob values hats and books equally at 1, and balls are highly valued by both of us at 10, it makes sense to maximize the allocation of books and balls since they have the highest individual values. Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:53,856][mllm.models.large_language_model_local][WARNING] - Response Given the values, Alice values books the most and hats the most, while you value books and hats relatively equally and balls the least. To maximize your points, you should propose to take all the books and a small number of hats, while leaving the balls for Alice, as you both value balls the least. Proposal: 1 hat, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:54,004][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both you and Bob place a much higher value on hats and books compared to balls. However, since balls are worth more to you, it would be strategically beneficial to claim as many balls as possible while still considering the total item quantity. Given that both of you likely will propose to keep all items (hats, books, and balls), you should aim to maximize your share of the balls, while keeping a reasonable number of hats and books to avoid over-proposing and having the items allocated proportionally. A strategic proposal would be to keep more balls and share the hats and books equally or slightly favoring your own higher valuation. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:54,284][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric and strategic nature of the game, where both Alice and Bob have the same values and Bob tends to mirror proposals, a balanced approach is likely to yield the best results. Since both you and Bob value hats and balls at a higher rate compared to books, and the allocations are proportional, a strategy of splitting the items evenly is reasonable. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:54,388][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:54,391][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:54,394][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:54,396][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:54,408][mllm.models.large_language_model_local][WARNING] - Response Given the new values, my per-item values are very different from Alice's. I value hats and books much less, and balls much more than Alice. Here's my proposal: Proposal: 10 balls, 10 books, 10 hats did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:54,409][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:54,411][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:54,417][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:54,423][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:54,425][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:54,427][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:54,429][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:54,497][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:54,596][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:54,600][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:54,603][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:54,607][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:54,610][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:54,626][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:54,646][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:54,650][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:54,684][mllm.models.large_language_model_local][WARNING] - Response Given the new per-item values where both you and Bob value hats and books at 10, but balls are valued at 10 by you and 1 by Bob, it makes sense to propose a distribution that maximizes your points. Since balls are highly valued by you and worth significantly more to you than to Bob, you should aim to take as many balls as possible. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:54,691][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both hats and books have higher value for you compared to Alice, while balls have a higher value for Alice. To maximize your points, you should propose to take all items with higher value to you, i.e., hats and books, and some balls to maximize your score. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:54,697][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:54,699][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:54,714][mllm.models.large_language_model_local][WARNING] - Response Given the current round setup and values, it seems advantageous to propose a split that takes into account the high value you place on hats and the high value Bob places on books. Here is a proposed strategy: - You value hats at 10 points each. - Bob values hats at 1 point each. - You value books at 1 point each. - Bob values books at 10 points each. - Both of you value balls at 1 point each. A fair strategy would be to propose splitting the hats and books based on their perceived value. Here is the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:54,716][mllm.models.large_language_model_local][WARNING] - Response Given the updated values, where your per-item values are hats=10, books=1, and balls=1, and Alice's values are hats=1, books=1, and balls=10, it's clear that you highly value the hats and Alice highly values the balls. Books have minimal value for both of you. To maximize your points, you should propose to keep all the hats and try to secure at least one book for yourself. Since Alice values the balls more than the books, you can propose to take all the balls from her. Proposal: 10 hats, 10 balls, 0 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:54,719][mllm.models.large_language_model_local][WARNING] - Response Given the current values, it seems that both you and Bob place significant value on different items. You value balls the most at 10, while Bob values hats the most at 10. Books are valued less by both of you. To maximize your points, you should focus on securing the items you value the most, which are the balls. Here's a strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:54,725][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:54,738][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:54,760][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:54,804][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:54,807][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:54,810][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:54,813][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:54,859][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:54,863][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:54,866][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:54,902][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:54,924][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:54,927][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:54,943][mllm.models.large_language_model_local][WARNING] - Response Given the asymmetric per-item values, a more strategic approach might be necessary. Since I value hats and books more than Alice values them, while Alice values balls more, I should try to secure more of items I value highly to maximize my points. Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:54,947][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:54,949][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:54,964][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the results from previous rounds, it seems that both Alice and Bob are trying to maximize their points by proposing equal shares of the items. To continue this strategy and maximize points, I will propose: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:54,967][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:55,011][mllm.models.large_language_model_local][WARNING] - Response Given the values, you have a significant advantage in books (10 points each) while Alice has an advantage in hats (10 points each). To maximize your points, you should propose to take all the books while splitting the hats and balls in a way that balances the risk and reward. Proposal: 10 hats, 10 books, 10 balls This proposal gives you all the books, maximizing your points from that category, while splitting the hats and balls evenly to ensure a fair and balanced proposal. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:55,046][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:55,050][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:55,053][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:55,060][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:55,067][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:55,070][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:55,089][mllm.models.large_language_model_local][WARNING] - Response Given the new values, Alice values hats and books more than I do, and I value books more than I previously thought. To maximize my points, I should focus on securing as many books as possible while also considering hats and balls. Here's my proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:55,089][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:55,178][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the situation and the values assigned, it seems that proposing to take all items is a fair and rational strategy, as it maximizes the points for both parties if Bob does the same. However, if we want to explore a bit more strategy, we could consider proposing a split that Bob might find attractive, such as splitting the items more equally. Proposal: 10 hats, 10 books, 10 balls This proposal ensures that both Alice and Bob have the best possible outcome given their valuation of the items. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:55,182][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:55,183][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:55,202][mllm.models.large_language_model_local][WARNING] - Response Given the values, it seems that both you and Bob place high value on different items. You value hats and books more, while Bob values hats and balls more. In this round, proposing to take all of the items might not be the best strategy since it could potentially lead to proportional allocation if the sum of our proposals exceeds the total quantity of items. A strategic approach would be to consider the potential for proportional allocation and try to make a proposal that maximizes your points while accounting for the possibility of such allocation. Given that we both value hats the most, it might be wise to propose taking a significant portion of the hats and balls, while leaving some books for Bob. Proposal: 10 hats, 10 balls, 5 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:55,205][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:55,208][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:55,212][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:55,225][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:55,248][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:55,251][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:55,274][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:55,375][mllm.models.large_language_model_local][WARNING] - Response Given Alice's high value for hats and books and your low value for hats and high value for books, a strategic proposal might involve trying to get more books while giving Alice a significant portion of hats and balls to ensure the total quantity is met. Proposal: 3 hats, 10 books, 7 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:55,379][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:55,382][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:55,434][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:55,437][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:55,440][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:55,536][mllm.models.large_language_model_local][WARNING] - Response Given the new values, where I value hats and books more and Bob values balls more, a smarter proposal might involve splitting the items in a way that maximizes my total value. Since I value hats and books more, it might be beneficial to keep more of those items and a smaller portion of the balls. Proposal: 10 hats, 10 books, 5 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:55,539][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:55,623][mllm.models.large_language_model_local][WARNING] - Response Given the updated per-item values where hats are worth 1 to you and 10 to Bob, and both books and balls are valued equally at 10 by you and 1 by Bob, we need to consider a strategy that maximizes your points. Since hats are worth much more to you than to Bob, and books and balls are the same, a more strategic approach might be to prioritize keeping more hats while splitting books and balls fairly to ensure you get a higher share of the higher-value items. A balanced proposal would be: Proposal: 10 hats, 5 books, 5 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:55,705][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:55,708][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:55,774][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:55,777][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:55,780][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:55,849][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and the previous round's outcomes, it seems that both Alice and Bob value the items very differently. Alice values hats and balls highly (10), while books are valued at 1. Bob values books highly (10) and hats and balls minimally (1). A strategic proposal would be to account for these differences to maximize points. Since both of us value hats and balls highly, it makes sense to propose a higher share of these items. Here's a proposal that might balance the points effectively: Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:55,853][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:55,933][mllm.models.large_language_model_local][WARNING] - Response Given the values, it seems Bob values books and balls more than hats, and you value hats and balls more than books. A strategic proposal should maximize the split of items that you value higher. Therefore, a balanced proposal that considers the values of both parties could be: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:55,971][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:55,974][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:56,015][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:56,022][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:56,026][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:56,030][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:56,033][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:56,036][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:56,038][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:56,175][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:56,182][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:56,256][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:56,263][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:56,266][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:56,336][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:56,408][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:56,411][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:56,414][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:56,426][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:56,470][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric nature of the per-item values and the fact that both you and Bob value hats and balls the same while valuing books differently, a strategic proposal would be to allocate the items in a way that maximizes your score, considering Bob's likely proposal. Since both of you value hats and balls the same, you might expect a similar split for these items. However, books are highly valued differently, so it's important to secure as many books as possible to maximize your points. A reasonable strategy would be to propose splitting hats and balls equally and taking as many books as possible. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:56,471][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:56,513][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:56,516][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:56,519][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:56,615][mllm.models.large_language_model_local][WARNING] - Response Given the updated values, I should focus on books and balls since they have higher values for both of us. The proposal should reflect an attempt to maximize the points based on the values: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:56,668][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that you and Alice have very asymmetrical preferences. You value books the most (10 points each), while Alice values hats the most (10 points each). In the previous round, we both split everything equally, which worked well for symmetric preferences but doesn't exploit the specific asymmetry in this round. To maximize your points, you should propose to take all the books since you value them the highest. Since Alice values hats the most, she is likely to propose to take all the hats. You can propose to take all 10 books, and Alice will take the hats. Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:56,671][mllm.models.large_language_model_local][WARNING] - Response Given the values, you should propose to take the items that align with your higher values while ensuring you do not exceed the total item quantity. Since your values for hats and balls are higher than Bob's for those items, and the values for books are similar, a strategic proposal would be to take most of the hats and balls, while splitting the books fairly. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:57,044][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:57,048][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:57,052][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:57,054][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:57,058][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:57,201][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:57,234][mllm.models.large_language_model_local][WARNING] - Response Given the values: - You value hats at 1, books at 10, and balls at 1. - Alice values hats at 10, books at 1, and balls at 1. Since you value books highly and Alice values hats highly, you want to negotiate your claim on the books while ensuring you keep some hats and balls for yourself. A strategic approach would be to propose an allocation that maximizes your points while considering Alice's values. Here's a proposal that balances both objectives: Proposal: 3 hats, 10 books, 10 balls This proposal gives you a significant share of the books which you value highly, while ensuring you also get some hats and balls to balance out your points. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:57,266][mllm.models.large_language_model_local][WARNING] - Response Given the values, you have a significant advantage with hats and books, while Alice has an advantage with hats and balls. Since the total quantity of items is the same and both are proposing to take all items, a strategic approach would be to propose slightly less to get a proportional share that still maximizes your value. Proposal: 9 hats, 10 books, 8 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:57,292][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:57,344][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the previous outcomes where proposing to take all items leads to a proportional split, it seems strategic to propose taking all items again. Here is my proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:57,347][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:57,370][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both hats and balls are more valuable to Bob than to you, while books are more valuable to you than to Bob. However, since both you and Bob value balls more in absolute terms and hats and books are equally valuable in your case, a balanced approach would be to propose an allocation that maximizes the value for both parties while ensuring that you get a fair share of the more valuable items. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:57,374][mllm.models.large_language_model_local][WARNING] - Response Given the updated per-item values, a strategic approach is necessary to maximize points. Since both Alice and I value hats the least and balls the most, we can try to ensure we get as many balls as possible. Here's a proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:57,381][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:57,384][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:57,386][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:57,390][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:57,401][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:57,427][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:57,450][mllm.models.large_language_model_local][WARNING] - Response Given the new values, my per-item value for hats is 1, for books is 10, and for balls is 1, while Alice's per-item values are 1 for hats, 10 for books, and 10 for balls. Since both of us value books the most and balls the least, and since the values are relatively similar, it makes sense to propose taking the majority of books to maximize points. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:57,453][mllm.models.large_language_model_local][WARNING] - Response Given the values, it seems that both you and Alice have high values for different items, making a fair proposal crucial. Let's propose an allocation that maximizes your utility given your values. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:57,455][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:57,458][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:57,493][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that we both have a high value for hats and books, and a low value for balls. To maximize points, we should propose to take all the hats and books since we value them more than Alice does. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:57,496][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:57,500][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:57,503][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:57,505][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:57,518][mllm.models.large_language_model_local][WARNING] - Response Given the new valuations, where your values for hats and books are lower but much higher for balls compared to Bob's, a more strategic approach might be to focus on maximizing your points from the balls, which you value highly. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:57,521][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:57,542][mllm.models.large_language_model_local][WARNING] - Response Given the asymmetric values where you have a higher value for hats and books, and a higher value for balls comes from Bob, a strategic approach would be to propose a split that maximizes your points based on your higher individual values. Here's the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:57,545][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:57,568][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:57,571][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:57,591][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:57,614][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:57,635][mllm.models.large_language_model_local][WARNING] - Response Given the new per-item values, where you highly value hats and Alice highly values balls, a strategic proposal would be to maximize your value for hats while still accounting for the possibility that the total proposed items might exceed the available items. Here’s the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:57,639][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:57,642][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:57,644][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:57,648][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:57,652][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:57,655][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:57,658][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:57,660][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:57,661][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:57,675][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:57,679][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:57,681][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:57,684][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:57,807][mllm.models.large_language_model_local][WARNING] - Response Given the values, both you and Alice place high value on hats and books, and low value on balls. A strategic proposal might involve splitting the items in a way that maximizes the value while considering the proportional allocation in case of overproposals. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:57,810][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:57,828][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:57,835][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:57,852][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:57,856][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:57,858][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:57,873][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:57,876][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:57,913][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:57,917][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:57,923][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:57,926][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:57,933][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:57,936][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:57,939][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:57,941][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:57,954][mllm.models.large_language_model_local][WARNING] - Response Given that Alice values hats and books more than balls, and you value hats and balls more than books, a strategy to maximize points would be to focus on items that you value highly. Here, distributing the items more skewed towards hats and balls could be beneficial. Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:57,958][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:57,971][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:57,975][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:57,990][mllm.models.large_language_model_local][WARNING] - Response Given the current per-item values and the fact that both Alice and Bob have similar but complementary valuations for the items, it makes sense to propose an allocation that maximizes the overall points while ensuring both parties are satisfied. Here’s a breakdown of the values: - **Hats**: You value hats at 1, while Bob values them at 10. - **Books**: You value books at 10, while Bob values them at 10. - **Balls**: You value balls at 10, while Bob values them at 1. A balanced proposal that considers both valuations could be: - Propose 10 hats to Bob (to exploit his high valuation). - Propose 10 balls to Bob (to exploit his high valuation). - Take all 10 books for yourself (to exploit your high valuation). This way, both parties get items that they value more highly, leading to a fair distribution that maximizes points for both. ** Proposal: 0 hats, 10 books, 10 balls** did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:57,992][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:57,995][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:58,012][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:58,033][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:58,055][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric and complementary nature of the values, proposing to take the entire items would likely result in a proportional split. To ensure a fair and potentially beneficial outcome, I will propose an equal distribution that accounts for the value each agent places on each item. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:58,058][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:58,065][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:58,071][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:58,082][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Bob highly value hats and books, while valuing balls less. To maximize your points, you should aim to get as many hats and books as possible while respecting the total item quantity. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:58,085][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:58,091][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:58,093][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:58,096][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:58,098][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:58,104][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:58,107][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:58,110][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:58,183][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it is clear that both hats and balls are relatively more valuable to you compared to Bob, while books are more valuable to Bob. To maximize your points, you should propose to take all items that are more valuable to you, while ensuring the total sum of your proposals does not exceed the item quantity if Bob also claims some of those items. Proposal: 10 hats, 10 balls, 0 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:58,187][mllm.models.large_language_model_local][WARNING] - Response Given the current per-item values, you value hats and books more than Bob, and Bob values balls more than you. To maximize your points, you should propose to take most of the balls since they have the highest value for you. Here's a proposal that takes into account the value differences: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:58,190][mllm.models.large_language_model_local][WARNING] - Response Given Alice's new values, it seems she places a much higher value on hats and books, and a lower value on balls. To maximize my points, I should try to get as many books as possible since they have a high value for Alice. Here's a proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:58,284][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the previous rounds and Bob's per-item values, it seems that a clear strategy emerges where both Alice and Bob are proposing to take all items each round, likely because the proportional sharing mechanism does not penalize them for doing so when their proposals sum up to or below the item quantity. However, to ensure we maximize points and consider the potential for Bob to deviate from this pattern, we should still propose to take all items to secure our maximum possible points. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:58,343][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:58,380][mllm.models.large_language_model_local][WARNING] - Response Given the current situation, where both you and Bob have similar but complementary values for the items, it's important to consider the allocation that maximizes your total value. Here's the reasoning: - You value hats (10 points each) and balls (1 point each) highly, but Bob values books (10 points each) highly. - To maximize your points, you should try to get at least some of the hats and balls since those have higher individual values for you, even if Bob values books more. A good strategy here would be to split the items in a way that balances your needs while also considering Bob's potential proposals. Here’s a proposal that allocates items in a way that maximizes your value: Proposal: 10 hats, 10 balls, 10 books This proposal ensures that you get all the items, which can maximize your points based on your values. Let's see how this might play out. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:58,382][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:58,384][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:58,385][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:58,391][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:58,398][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:58,439][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:58,446][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:58,521][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:58,605][mllm.models.large_language_model_local][WARNING] - Response Given the new values where hats are worth 1 point to you and 1 point to Alice, books are worth 10 points to you and 10 points to Alice, and balls are worth 1 point to you and 10 points to Alice, the allocation should be slightly adjusted to maximize your points. Since hats and books have equal value to both of us, it's a strategic tie-breaker between the two items. Balls, however, are more valuable to Alice. A balanced proposal would be to take a small number of books and a larger number of balls since they're highly valued by Alice. Let's propose to take 5 hats, 5 books, and 10 balls. Proposal: 5 hats, 5 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:58,608][mllm.models.large_language_model_local][WARNING] - Response Given the values, both you and Alice have high value for hats (10) and books (10), while balls are valued less (1). To maximize points, we should try to capture both hats and books. Here's a proposal strategy: - You value hats and books highly, so propose to keep all of them. - If Alice also values hats and books, she might propose to keep all of them as well. A balanced approach is to propose to keep a significant portion of the items: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:58,611][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:58,619][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:58,625][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:58,632][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:58,638][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:58,644][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:58,669][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric nature of the problem and the fact that both you and Bob value different items equally, it's strategic to propose an equal split to ensure fairness and maximize individual earnings. Here's the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:58,672][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:58,675][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:58,691][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and the strategic nature of the game, it's important to consider the relative values and how they might influence Bob's proposals. Since both of us have a high value for balls (10), and a low value for hats (1 in your case, 10 in his), books being relatively valued the same by both, we might want to propose splitting the items in a way that maximizes both our points. A balanced strategy would be to split the items fairly, ensuring that both parties get a share of the high-value items (balls and hats) and the middle-value item (books). Here’s the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:58,694][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:58,715][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the game and Bob's consistent strategy of proposing equal splits, it appears that proposing equal splits also maximizes your points. Here, since both you and Bob value hats and balls similarly (10 and 1 respectively), and books differently (1 for you vs 10 for Bob), proposing an equal split might not be the best strategy. However, since Bob values books more, he might allocate more balls and hats to himself, while keeping fewer books. A strategic approach would be to slightly adjust the proposal to account for Bob's valuation, but maintaining a notable share of the items to ensure fair distribution and maximize personal points. Proposal: 9 hats, 10 books, 9 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:58,718][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:58,721][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:58,727][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:58,737][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:58,765][mllm.models.large_language_model_local][WARNING] - Response Given the values, I should focus on books since they have the highest value for both of us. However, since Alice also values books, I need to ensure I get a fair share. A good strategy might be to propose keeping all the hats and balls for myself, while proposing a low number of books to avoid the risk of getting just a fraction of the books if Alice also claims a significant portion. Proposal: 10 hats, 5 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:58,767][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:58,792][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:58,815][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:58,847][mllm.models.large_language_model_local][WARNING] - Response Given the distribution of values, it's clear that both you and Alice have significant value for books and balls, but different preferences for hats. To maximize points, we should try to secure as many of the items with high personal value as possible. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:58,851][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:58,856][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:58,861][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:58,865][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:58,867][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:58,871][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:58,876][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:59,122][mllm.models.large_language_model_local][WARNING] - Response Given the scenario, it's important to balance the allocation to maximize the total points. Since I value books and balls significantly more than hats, and Bob values hats and books more than balls, it's strategic to propose a split that matches our respective valuations as closely as possible. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:59,126][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:59,130][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:59,186][mllm.models.large_language_model_local][WARNING] - Response Given the values where I have a high value for hats and books, and Bob values hats and books more than I do, a strategic proposal should focus on splitting the items in a way that maximizes my points while ensuring our combined proposal does not exceed the total items available. Here is a revised proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:59,190][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:59,192][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:59,267][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:59,351][mllm.models.large_language_model_local][WARNING] - Response Given the values, it seems optimal to propose a distribution that maximizes the value based on our per-item values. Since hats and balls are highly valued by me and moderately valued by Bob, while books are valued similarly by both, a strategic split could involve keeping more of the highly valued items and dividing the books proportionally. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:59,441][mllm.models.large_language_model_local][WARNING] - Response Given the updated per-item values, where you value hats and books more and Bob values hats and books equally but balls less, a strategic proposal might involve maximizing your own value by taking more of the items you value higher. However, since both you and Bob value hats the most and both propose taking all hats, it makes sense to mirror Bob's proposal to ensure fair allocation and maximize points. Here is the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:59,445][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:59,535][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and equal per-item values for the items, it seems that both you and Bob are getting the same value for each item. Therefore, to maximize your points, it makes sense to propose splitting the items equally. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:59,622][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric nature of Alice's and your per-item values, proposing a split that maximizes the value based on your higher preferences for hats and books, while still considering the proportional allocation, seems strategic. Here's a balanced proposal that aims to maximize your points: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:59,698][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:59,724][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's beneficial to focus on items where I have a significantly higher value than Alice. Here, books and hats are the key items. Let's maximize the points from books and hats while ensuring the proposal does not exceed the available items. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:59,732][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:59,739][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:59,744][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:59,761][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:59,768][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:05:59,774][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:59,778][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:59,848][mllm.models.large_language_model_local][WARNING] - Response Given the values: - You value hats at 1, books at 1, and balls at 10. - Alice values hats at 10, books at 1, and balls at 1. To maximize your points, you should try to get as many of the items you value highly (balls) as possible while still proposing a fair share to avoid splitting items proportionally. Here's a strategic proposal: Proposal: 10 books, 10 balls, 10 hats This proposal focuses on taking most of the balls and an equal amount of hats and books to match Alice's proposal, ensuring that you don't trigger proportional splitting while maximizing your valued items. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:59,921][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:59,951][mllm.models.large_language_model_local][WARNING] - Response Given the values, it is clear that both you and Alice place high importance on different items. You value books highly, while Alice values books and balls more. A strategic proposal would be to recognize the disparity and aim for an allocation that maximizes your points based on your higher valuation of books. Proposal: 10 hats, 10 books, 0 balls This proposal gives you the maximum number of books, which has a high value for you, and some hats, while giving up the balls, which have a low value for you but are highly valued by Alice. This should help maximize your points given the item valuations. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:05:59,954][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:59,956][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:59,974][mllm.models.large_language_model_local][WARNING] - Response Given the values, you have a strong preference for balls (10) and a significantly lower value for hats (1) and books (1) compared to Alice. Alice has a stronger preference for hats and books. To maximize your points, you should propose to keep all the balls and only a few of the hats and books. Here's a strategic proposal: Proposal: 1 hat, 1 book, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:59,975][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:05:59,977][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:00,003][mllm.models.large_language_model_local][WARNING] - Response Given the new values, Alice places high value on books and balls, while you value hats and books highly. To maximize your points, you should propose to take the majority of the hats since you value them highly and Alice values books and balls more. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:00,030][mllm.models.large_language_model_local][WARNING] - Response Given the updated values, where you have a high value for hats and balls, and Alice has a high value for books, it makes sense to propose a distribution that reflects these valuations while considering the proportional allocation rule. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:00,057][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:00,085][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:00,088][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:00,092][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:00,112][mllm.models.large_language_model_local][WARNING] - Response Given the values, you have a significant preference for books, and Alice has a similar preference but for hats. Meanwhile, both of you have a lower value for balls. Given that the items will be split proportionally if the sum of proposed amounts exceeds the item quantity, a strategic approach would be to maximize your points by focusing on the items where you have higher values. A reasonable proposal would be to take all the books and split the hats and balls equally (or almost equally) with Alice, to prevent the proportional split from costing you points. Proposal: 5 hats, 10 books, 5 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:00,747][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:00,752][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:00,756][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:00,759][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:00,762][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:00,765][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:00,771][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:00,777][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:00,780][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:00,782][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:00,818][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:00,822][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:00,824][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:00,899][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:00,902][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:00,906][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:00,909][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:00,981][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:01,066][mllm.models.large_language_model_local][WARNING] - Response Given the new values, where my per-item values for hats and books are lower but the value for balls is the same, while Bob values the hats and books more and balls less, a strategic proposal would be to maximize the distribution of items that I value highly while still considering Bob's valuation. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:01,134][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:01,138][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:01,158][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:01,165][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:01,168][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:01,171][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:01,175][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:01,179][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:01,182][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:01,277][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:01,365][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:01,372][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:01,375][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:01,448][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:01,451][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:01,454][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:01,597][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:01,618][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:01,621][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:01,625][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:01,639][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:01,663][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:01,716][mllm.models.large_language_model_local][WARNING] - Response Given the values, the optimal strategy would be to maximize the items with the highest value for both yourself and Bob, as well as spread your claims in a way that prevents total proposals from exceeding the item quantity. Here, hats are valued the highest for you, books are valued the highest for Bob, and balls have moderate value for both. A balanced proposal that maximizes your total points while being reasonable is: Proposal: 10 hats, 10 books, 10 balls However, to be more strategic and avoid the total sum exceeding the item quantity, we can slightly adjust it. Proposal: 10 hats, 8 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:01,723][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's important to maximize the points considering both my and Alice's valuations. Since I value hats and balls highly and Alice values books and balls highly, we are likely to have similar valuations for books. However, to maximize my points, I should propose taking the majority of items where I have a higher valuation. Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:01,850][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:01,852][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:01,855][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:01,858][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:01,862][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:01,864][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:01,867][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:01,870][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:01,874][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:01,877][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:01,917][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both hats and books are highly valued by both parties, while balls are valued less. To ensure a fair and strategic split, we can allocate the items proportionally to their value. Since both hats and books are valued equally by both agents, it makes sense to split them evenly to avoid conflict. Balls, being valued less, can be distributed based on whoever values them more slightly more. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:02,080][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:02,088][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:02,164][mllm.models.large_language_model_local][WARNING] - Response Given the asymmetry in values, it's important to propose in a way that maximizes the points for the items where you have a higher valuation. Here, books are highly valuable for you, followed by hats and balls. Proposal: 10 hats, 10 books, 10 balls However, since you have a much higher valuation for books, proposing fewer hats and more books might be more beneficial. Let's adjust the proposal to focus more on books where you value them highly. Proposal: 5 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:02,167][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:02,398][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:02,402][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:02,454][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:02,458][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:02,465][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:02,468][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:02,471][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:02,508][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:02,510][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:02,564][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:02,589][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:02,596][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:02,603][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:02,644][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:02,670][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:02,701][mllm.models.large_language_model_local][WARNING] - Response Given the new values, Alice highly values hats and books while I value books and balls more. To maximize my points, I should try to get as many books as possible, since they provide the highest value for both of us. Here's a strategic proposal: Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:02,709][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:02,712][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:02,715][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:02,722][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:02,725][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:02,727][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:02,730][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:02,746][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:02,749][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:02,751][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:02,758][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric and strategic nature of this game, where both you and Bob have similar valuations for the items, the optimal strategy would be to propose an allocation that ensures both parties receive equivalent value, thereby avoiding any potential proportionality issues if the sum of proposed allocations exceeds the item quantity. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:02,761][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both hats and balls are more valuable to me than to Bob, while books are valued equally. To maximize my points, I should propose to take all of the hats and balls, and split the books equally. Proposal: 10 hats, 10 balls, 10 books / 2 = 5 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:03,022][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and Bob's proposals in the last round, it seems that possessing items with higher values, such as the books and balls, is crucial for maximizing points. Analyzing the per-item values: - Hats: You value at 10 and Bob at 1 - Books: You value at 1 and Bob at 10 - Balls: You value at 10 and Bob at 1 In this round, we should aim to keep more of the items we value higher, which are the books and balls. Here is a strategic proposal: Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:03,025][mllm.models.large_language_model_local][WARNING] - Response Given the values, it appears that both you and Bob place high value on different items. You value hats and balls highly, while Bob values books and hats. In this round, we need to come up with a proposal that maximizes our points, considering our per-item values and Bob's. Since you value hats and balls highly, and these are being split equally, it might be beneficial to propose splitting the books as well, as they are also valuable for you. Here's a proposal that balances the distribution: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:03,029][mllm.models.large_language_model_local][WARNING] - Response Given the updated per-item values, it appears that both you and Bob highly value different items. You value hats and books highly (10 each), while you both value balls the least (1 each). Proposing to take all items you value highly will maximize your points. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:03,108][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:03,112][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:03,118][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:03,121][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:03,124][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:03,194][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric nature of the per-item values and the previous outcomes, it seems optimal to propose an equal split of all items. This strategy maximizes the likelihood of both parties receiving items that align with their individual values. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:03,197][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:03,199][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:03,267][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:03,293][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:03,297][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:03,300][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:03,354][mllm.models.large_language_model_local][WARNING] - Response Given the values, Alice places a high value on hats and books, while you and Alice value balls similarly but you value books much higher than Alice values them. To maximize your points, it's strategic to propose a distribution that aligns with your high value on books while still considering the total item quantity. Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:03,357][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:03,360][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:03,492][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Bob place high value on different items, and the optimal strategy would be to propose a split that maximizes the value for the items you both value. Here, you value hats and balls highly, while Bob values books and balls highly. To ensure you get the maximum benefit from the items you value, a balanced proposal that considers both your and Bob's interests might be: Proposal: 10 hats, 10 books, 10 balls However, if we consider a more strategic move to ensure you get the items you value more, we could propose: Proposal: 10 hats, 10 balls, 10 books This proposal ensures that you get all the hats and balls, which you value at 10 each, and a portion of the books, which you value at 1 each. So the proposal is: Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:03,495][mllm.models.large_language_model_local][WARNING] - Response Given the values, it seems optimal to propose splitting the items equally to ensure both agents receive a fair share based on their valuations. Here's the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:03,498][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:03,535][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:03,619][mllm.models.large_language_model_local][WARNING] - Response Given the current values, it seems that both agents have a higher value for hats and books compared to balls. Since we are equally likely to receive the items we propose, a strategic proposal might involve splitting the items more evenly to ensure a balanced distribution and maximize the overall value. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:03,668][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:03,671][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:03,674][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:03,677][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:03,701][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:03,988][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:03,991][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:03,994][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:03,997][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:04,033][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:04,036][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:04,039][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:04,045][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:04,049][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:04,052][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:04,056][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:04,058][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:04,065][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:04,068][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:04,072][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:04,075][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:04,079][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:04,113][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:04,116][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:04,119][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:04,202][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:04,205][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:04,290][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:04,297][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:04,300][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:04,302][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:04,305][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:04,308][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:04,310][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:04,351][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:04,421][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:04,424][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:04,445][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:04,449][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:04,545][mllm.models.large_language_model_local][WARNING] - Response Given the values and the symmetry in the distribution, it's beneficial to propose a split that reflects the high value of books and balls for both of us. Here's a proposal that tries to maximize the points for both hats and books, which have higher values: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:04,690][mllm.models.large_language_model_local][WARNING] - Response Given the values, both you and Alice place high value on books and low value on balls, but you both value hats relatively equally. To maximize points, it's important to consider the allocation that gives you the highest value. Proposing: - 10 hats - 10 books - 10 balls This distribution ensures you receive the maximum quantity of items you value highly (books) and reasonably value (hats). Since Alice values books and balls similarly to you, this might lead to a similar proposal. However, this strategy maximizes your potential points. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:04,729][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:04,732][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:04,756][mllm.models.large_language_model_local][WARNING] - Response Given Alice's valuation of books and balls, it seems strategic to propose a different distribution to see if we can get a more favorable split. Since I value hats and books more highly, I might try to negotiate a different split that balances my perceived value with Alice's different valuations. Proposal: 10 hats, 1 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:04,759][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:04,762][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:04,781][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:04,784][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:04,804][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, I should focus on maximizing the value of the items I have the highest valuation for. Since I value hats and books at 1 while Alice values hats at 10 and books at 1, and balls at 1 for both of us, it makes sense to propose keeping all the books and splitting the hats and balls. Proposal: 5 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:04,855][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:04,905][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both our values for hats and books are low compared to Alice's values, while Alice values balls the least. To maximize my points, I should keep all the items that I value higher and let Alice have the balls. Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:04,913][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:04,915][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:04,918][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:04,921][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:04,928][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:04,935][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:04,938][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:04,940][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:04,944][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:04,946][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:04,953][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:04,959][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:04,964][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:04,984][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that Alice values books the most, while you value hats the most and books to some extent. In this round, since Alice values books the most and you value them second, you might want to allocate more books to match Alice's valuation and secure more of the items you value highly (hats and balls). Proposal: 10 hats, 10 books, 10 balls This proposal ensures you get all hats and balls, which you value highly, and some books, which you also value but not as much as Alice. Given the symmetry and the high value of hats, this proposal aims to maximize your total points by securing all hats and a significant portion of books. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:04,989][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:04,993][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:04,998][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:05,000][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:05,194][mllm.models.large_language_model_local][WARNING] - Response Given the values, it seems that both you and Alice have complementary preferences for the items. You value hats and books highly, while Alice values balls highly. Proposing to take all items might lead to a proportional split if the total proposed exceeds the available items. A more strategic approach would be to propose taking as many of the items as possible to maximize your earnings. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:05,221][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:05,224][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:05,246][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:05,249][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:05,272][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:05,296][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:05,300][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:05,303][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:05,320][mllm.models.large_language_model_local][WARNING] - Response Given Alice's values, she values books and balls highly while I value hats and balls highly. To maximize my points, I should focus on claiming the items I value more. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:05,344][mllm.models.large_language_model_local][WARNING] - Response Given that both Alice and I have the same per-item values for hats and books, and identical values for balls, proposing equally should maintain the balanced distribution that has been effective so far. Therefore, I will continue to propose as follows: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:05,347][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and equal expected per-item values, proposing a fair split seems optimal. This ensures that neither agent gains an unfair advantage while maximizing the utility based on the given values. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:05,350][mllm.models.large_language_model_local][WARNING] - Response Given the values, you should focus on maximizing the items with higher personal value while also considering Bob's reduced value for balls. Here's the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:05,356][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:05,392][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the high value of "balls" for both of us, and the medium value for "books," it's strategic to propose an allocation that ensures we each get a significant portion of the "balls," while also considering the value of "books." Here's a refined proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:05,417][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:05,420][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:05,423][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:05,425][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:05,440][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:05,443][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:05,465][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:05,468][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:05,485][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Bob place significant value on hats and books, and less value on balls. Since the values are symmetrical and both of you propose equally dividing all items, let's consider a more strategic approach to maximize points. You value hats and books highly, while Bob values books and balls highly. To exploit Bob's valuation, we can propose taking almost all books and half the balls, while giving him most of the hats. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:05,492][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:05,509][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:05,531][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:05,534][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:05,537][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:05,554][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:05,561][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:05,568][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:05,574][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:05,581][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:05,587][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:05,626][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:05,630][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:05,632][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:05,654][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:05,798][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:05,802][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:05,883][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:05,890][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:05,908][mllm.models.large_language_model_local][WARNING] - Response Given the values, you have a significant preference for books (10) and Alice has a significant preference for hats (10). To maximize your points, you should try to claim as many books as possible while still considering the allocation of hats and balls. Here's a proposal that prioritizes books while ensuring you get some value from hats and balls: Proposal: 1 hat, 10 books, 10 balls This proposal ensures you get the maximum value from books while still obtaining some value from hats and balls. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:05,914][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:05,935][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it seems both you and Alice have a strong valuation for hats and books, and a low valuation for balls. Since both of you are likely to value the items similarly, and given the symmetry in the problem, a cooperative strategy where we both propose to split the items evenly would be optimal. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:05,938][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:05,941][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:05,944][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:05,962][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:05,969][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:05,985][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:05,988][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:05,995][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:06,011][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:06,017][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:06,019][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:06,036][mllm.models.large_language_model_local][WARNING] - Response Given the new values, it makes sense to prioritize books and balls since they have higher values for both Alice and Bob. Here's the optimal proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:06,039][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:06,045][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:06,126][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric and high value of books for Bob, it's crucial to address his valuation while ensuring we maximize our own value. Here's the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:06,129][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:06,136][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:06,198][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:06,204][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:06,367][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:06,441][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:06,444][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:06,447][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:06,512][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, the strategic proposal would be to take the items that are most valuable to you and allocate the rest proportionally. Since you value balls the most at 10, hats second at 1, and books least at 1, it would be wise to propose keeping all the balls, a few hats, and none of the books. Proposal: 10 hats, 10 balls, 0 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:06,538][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:06,562][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:06,565][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:06,568][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:06,571][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:06,629][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:06,673][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Bob place a high value on different items, and the optimal strategy involves proposing to take the items you value highly while hoping that Bob will take the items he values highly. Here, you value hats and balls highly (10) while Bob values books highly (10) and hats and balls less so (10 and 1, respectively). Proposing to take all 10 hats, 10 balls, and none of the books would maximize your potential points since you value those items highly and Bob does not value them highly at all. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:06,680][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:06,683][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:06,685][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:06,688][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:06,725][mllm.models.large_language_model_local][WARNING] - Response Given Alice's values, it makes sense to propose a distribution that takes into account the higher value she places on hats and books, while still securing a fair share of the balls. Here's the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:06,814][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:06,892][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:06,899][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:06,977][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:07,023][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, the optimal strategy would be to maximize the items that you value highly while also considering Alice's valuation. Since hats and books are highly valued by you and Alice values balls highly, proposing to take all items might lead to a proportional split when the total amount proposed exceeds the available items. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:07,031][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:07,108][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:07,156][mllm.models.large_language_model_local][WARNING] - Response Given the updated per-item values and the structure of the game, it is crucial to strategically allocate the items to maximize points. Here, you value hats at 1, books at 10, and balls at 10, while Bob values hats at 10, books at 10, and balls at 1. Proposing an equitable split that considers these differences would be more beneficial. Proposal: 10 hats, 10 books, 10 balls However, given your higher valuation of books and balls, and your understanding of Bob's valuations, proposing to take most of the items that you value more can be a strategy to maximize your points. Proposal: 10 hats, 10 books, 10 balls This approach aims to capture the majority of items you value the most, thereby maximizing your potential earnings. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:07,202][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric nature of the per-item values and the fact that both Alice and I value books highly, we can consider proposing a split that maximizes the value for books while ensuring a fair allocation of hats and balls. Since Alice values books the least, we might want to propose keeping all the books for ourselves to maximize our points. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:07,280][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the equal splitting of the items in previous rounds, it seems both Alice and Bob are proposing to take all of each item. To explore a different strategy and potentially gain an advantage, we could propose a more aggressive or defensive split. However, considering the points allocation system and the equal split of items, the expected return from this strategy is likely to be similar. To diversify and test the waters, we can propose an unequal split that benefits us more given our values. Here's a revised proposal: Proposal: 10 hats, 10 balls, 0 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:07,287][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in our per-item values and Alice's, proposing to take the entire allocation of items seems rational to maximize our points. Here’s the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:07,307][mllm.models.large_language_model_local][WARNING] - Response Given the significant disparity in the values each of us places on the items, it's important to propose a distribution that reflects these values to maximize our points. Since you value books and balls more and I value hats more, we should propose to take the items that we value the most. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:07,310][mllm.models.large_language_model_local][WARNING] - Response Given the new per-item values, your best strategy might involve recognizing that both you and Bob value hats and books highly, but Bob values balls significantly more than you do. To maximize your points, you should propose the majority of the items that you value highly. Proposal: 10 hats, 10 books, 1 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:07,380][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:07,465][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:07,471][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:07,478][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:07,551][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:07,601][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it is clear that both you and Bob have a strong preference for different items. You value hats (10) and books (1) highly, while Bob values hats and books at 1 and favors balls (1). Given the symmetry in the rounds so far and the random assignment of items, a strategy that focuses on maximizing the items you value highly while ensuring Bob also receives some value might be effective. Proposal: 10 hats, 10 books, 10 balls This proposal ensures you get all the hats and books, which you value highly, while also providing Bob with some balls, which he values. This should help maintain a fair and cooperative relationship while maximizing your points. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:07,604][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that the books and balls are highly valued differently by you and Alice. To maximize your points, you should try to get more of the items you value more. Here, you value books and balls highly, while Alice values hats more. However, since both of you have the same values for hats, a cooperative approach might be beneficial. A good strategy would be to propose a split that maximizes your score while considering Alice's probable response. Here’s a proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:07,685][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:07,688][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:07,691][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:07,694][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:07,697][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:07,771][mllm.models.large_language_model_local][WARNING] - Response Given Alice's valuation of books and your own valuation, proposing a more strategic distribution might be beneficial. You value books highly, while Alice also values books but less than you do. Therefore, proposing to keep more books and a moderate number of hats and balls might be a good strategy. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:07,779][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:07,857][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:07,860][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:07,864][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:08,018][mllm.models.large_language_model_local][WARNING] - Response Given the new values where you have a high value for hats (10) and Alice has a high value for hats (10), it makes sense to propose keeping all the hats. For books and balls, since there are low values for both of you, it might be safer to share these to avoid the risk of getting nothing if your proposal exceeds the available items. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:08,022][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:08,025][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:08,028][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:08,034][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:08,086][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:08,191][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:08,194][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:08,222][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:08,225][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:08,298][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:08,301][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:08,304][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:08,359][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:08,366][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:08,425][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:08,455][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:08,458][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:08,462][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:08,602][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the equal per-item values that are likely to be assigned randomly, a strategic proposal would be to propose the same quantity of items as what you would receive if Bob proposes optimally. This would ensure that you maximize your points based on your per-item values. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:08,796][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:08,799][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:08,838][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:08,844][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:08,847][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:08,850][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:08,853][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:08,856][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:08,859][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:08,913][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:08,916][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:09,252][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:09,256][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:09,258][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:09,265][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:09,337][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:09,340][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:09,343][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:09,345][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:09,407][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:09,430][mllm.models.large_language_model_local][WARNING] - Response Given the updated per-item values where you value hats at 1, books at 10, and balls at 1, and Alice values them as hats at 10, books at 1, and balls at 1, it's clear that we should try to maximize the distribution of books since Alice values them the least and you value them highly. Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:09,437][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Alice value the balls the most, but you value hats and books significantly less compared to Alice. To maximize your points, you should propose to take as many balls as possible while leaving the other items for Alice, who values them more. Proposal: 0 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:09,443][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:09,495][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:09,499][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:09,502][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:09,505][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:09,538][mllm.models.large_language_model_local][WARNING] - Response Given Alice's per-item values for this round, she values hats and books more than balls, which is similar to your values. To maximize points, we should both propose a distribution that reflects these high values. A fair and maximizing proposal would be to split the items evenly in terms of their values. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:09,541][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:09,556][mllm.models.large_language_model_local][WARNING] - Response Given the values, it seems that both you and Bob place a high value on hats and books, but Bob highly values books while you do not. This symmetry in valuations might lead to a more strategic approach where we try to maximize our own value while ensuring we don't overpropose relative to Bob's values. Proposal: 10 hats, 10 books, 10 balls However, let's consider a more strategic proposal: Proposal: 10 hats, 10 books, 10 balls This proposal evenly distributes all items, which might not fully exploit the disparity in Bob's values. Given that we both value hats and books similarly but Bob values books much more, we could consider proposing to take more books and balls to maximize our points. Proposal: 10 hats, 10 books, 10 balls Given the symmetry and the need to balance our proposals, let's propose: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:09,576][mllm.models.large_language_model_local][WARNING] - Response Given the values and the strategy of evenly splitting the items, I will continue to propose an equal distribution to maximize the points considering both the value of the items and the risk of over-proposing. Propposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:09,579][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:09,582][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:09,596][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:09,599][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:09,603][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:09,615][mllm.models.large_language_model_local][WARNING] - Response Given the values, it makes sense to propose a strategy that maximizes your points based on the per-item values. Since you value books the most at 10 and Alice values hats the most at 10, it's beneficial to attempt to get all the books while allowing some flexibility for hats and balls to ensure the total items do not exceed the quantity available. Proposal: 10 hats, 10 books, 10 balls However, given the structure and the previous rounds where splitting equally balanced out the points, a slight modification might be more strategic. Let's ensure we get all the books and a fair share of hats and balls. Proposal: 5 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:09,622][mllm.models.large_language_model_local][WARNING] - Response Given the current per-item values, it's clear that Alice values books significantly more than hats and balls, while you value hats more than books and balls. Based on this, a strategic proposal would be to attempt to maximize the value of items you both value highly, while also considering the proportional allocation in case of excess proposals. Proposal: 1 hat, 10 books, 10 balls This proposal ensures you get the maximum value from hats and books, while also giving Alice a significant portion of the books, which she values highly. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:09,625][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:09,694][mllm.models.large_language_model_local][WARNING] - Response Given the current item values, your best strategy would be to claim all the items that you value highly while leaving the items Alice values highly for her. Here's the proposal: Proposal: 10 hats, 10 books, 10 balls However, since you value books and balls the most and Alice values hats and books, you should propose to take all the balls and a portion of the books, leaving the hats for Alice. Here's a more strategic proposal: Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:09,763][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that hats and books have higher value for you, and balls have higher value for Alice. To maximize your points, you should propose to take the majority of the high-value items for you, while acknowledging that Alice values balls highly and will likely claim most of them. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:09,784][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:09,787][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:09,793][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:09,796][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:09,798][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:09,801][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:09,804][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:09,823][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:09,830][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:09,833][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:09,840][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:09,846][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:09,848][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:09,900][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:10,046][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:10,049][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:10,056][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:10,062][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:10,082][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:10,101][mllm.models.large_language_model_local][WARNING] - Response Given the values, it appears that both you and Alice place the highest value on books (10). This suggests a mutual interest in the books, while both of you value hats and balls less. Given this, a strategic proposal would be to allocate a significant portion of books to yourselves while splitting the rest of the items. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:10,121][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric nature of the per-item values and the previous outcomes, a cooperative strategy seems to be beneficial. Since we both receive similar points when we propose the same allocation, it might be optimal to continue proposing a fair split. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:10,124][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:10,127][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:10,143][mllm.models.large_language_model_local][WARNING] - Response Given that both you and Alice value hats and books highly and balls less, a cooperative strategy might be beneficial. Since both of you have the same values for hats and books, proposing to split these items equally could lead to the highest points for both. Balls, being valued the same by both, can also be split equally. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:10,149][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:10,152][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:10,155][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:10,170][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:10,176][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:10,190][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both you and Alice have complementary preferences. You value books and balls highly, while Alice values hats and books. A strategic approach would be to cooperate and propose a分配方案，使得双方都能最大化自己的收益。一个合理的提议方案是： Proposal: 10 hats, 10 books, 10 balls 然而，考虑到对方的估值，我们可以稍微调整分配，以确保你能获得更多的收益。一种可能的策略是尽量满足对方的高价值物品需求，同时确保自己也能获得足够的价值。基于这个思路，我们可以提议： Proposal: 10 hats, 10 books, 10 balls 但实际上，考虑到双方的高额估值差异，我们可以通过轮流分配高值物品来最大化收益。一种合理的方式是让对方先拿自己看重的物品，再补充你自己的高值物品。这里是一个具体的提议： Proposal: 10 hats, 10 books, 10 balls 不过，考虑到最大程度利用每件物品的价值，合理的分配可能会更倾向于满足高价值物品的需求。具体来说： Proposal: 10 hats, 10 books, 10 balls 考虑到双方的偏好，一种更具体的分配可以是： Proposal: 10 hats, 10 books, 10 balls 为了确保更具体的分配，我们可以更细致地考虑每种物品的分配比例。基于上述分析，一个合理的分配方案可以是： Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:10,193][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:10,196][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:10,209][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:10,212][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:10,230][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:10,268][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the high value both you and Bob place on different items, a strategic approach would be to try to maximize your points while anticipating Bob's likely strategy. Since both of you have the same sets of items, it's reasonable to assume that Bob will mirror your proposal to achieve the same outcome. Therefore, a balanced approach should yield the best result. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:10,274][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the values and the proposed strategy in the previous rounds, it seems that proposing an equal split might be effective. Since both of us value books and balls similarly, and hats are valued more by Alice, proposing an equal split might maximize the points. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:10,285][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:10,305][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:10,308][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:10,311][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:10,318][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:10,326][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both you and Alice value hats and books equally, but both place higher value on balls. However, since both of you propose the same amounts and the total always equals the number of items, we need to consider the proportional distribution if the total proposed exceeds the number of items. In this case, since the proposed amounts equal the number of items, the items will be distributed as proposed. Given this, and considering the high value of balls for both of us, a strategic proposal might be to slightly outbid each other for the balls to capture more of them, while keeping the hats and books split evenly since their value is relatively lower and more evenly distributed. Proposal: 10 hats, 10 books, 9 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:10,329][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:10,336][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:10,342][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:10,382][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:10,384][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:10,491][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:10,537][mllm.models.large_language_model_local][WARNING] - Response Given that both you and Alice have strong values for hats and books, and weak values for balls, proposing to split the items equally might not be the best strategy as it could lead to a proportional allocation that doesn't fully utilize the higher values. Instead, we should try to maximize the high-value items while still considering the proportional allocation rule for balls. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:10,609][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:10,615][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:10,690][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in per-item values and the previous outcomes where proposing to take all items leads to a proportional split and results in the maximum points, I will continue to propose the allocation of all items. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:10,693][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:10,696][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:10,699][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:10,705][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:10,736][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:10,742][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:10,747][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:10,757][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:10,781][mllm.models.large_language_model_local][WARNING] - Response Given that both you and Alice value hats and books highly and balls to a lesser extent, a cooperative strategy might yield better results. Let's propose a split that balances our high values and ensures neither of us feels the allocation is unfair. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:10,784][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:10,786][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:10,789][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:10,811][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:10,813][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:10,914][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:10,936][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:10,939][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:10,957][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:10,964][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:11,007][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Bob value books highly at 10. To maximize your points, you should try to secure as many books as possible, since they give you the highest per-item value of 10. Here's a strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:11,011][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:11,014][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:11,058][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:11,061][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:11,065][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:11,067][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:11,070][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:11,073][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:11,311][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric and high value for hats and books for both Alice and myself, let's propose an equitable split to ensure both parties advance their high-value items without any one-sided advantage. Here is the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:11,318][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:11,321][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:11,350][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric nature of the per-item values and the previous outcomes, it seems that splitting the items evenly often results in the highest earnings. In this round, since we both value books and balls highly, and hats only slightly, we can propose an even split to maximize our points. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:11,354][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:11,356][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:11,359][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:11,433][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and the symmetry in the situation, it seems optimal to propose an equal split to ensure we get a fair share of the items. Here's the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:11,436][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:11,595][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric and equal value distribution between the items and the fact that both you and Alice have equivalent valuations for the items, proposing to split the items evenly seems like the fairest and most beneficial approach. Here is the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:11,676][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:11,682][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:11,689][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:11,695][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:11,760][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:11,763][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:11,766][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:11,769][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:11,775][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:11,847][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:11,850][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:11,934][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric nature of the per-item values and the previous round's results, where proposing to take all items without considering the other's values led to equal but suboptimal splits, a more strategic approach would be to consider the total value from Alice's perspective and propose a split that maximizes our own points. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:12,104][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:12,111][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:12,274][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:12,339][mllm.models.large_language_model_local][WARNING] - Response Given our per-item values and Bob's, it's clear that both hats and books are more valuable to Bob, while balls are valued more by you. To maximize your points, you should propose to take more of the items you value more. Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:12,383][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:12,386][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:12,389][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:12,392][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:12,395][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:12,398][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:12,404][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:12,533][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:12,812][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:12,832][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:12,835][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:12,837][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:12,844][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:12,847][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:12,860][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:12,863][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:13,099][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:13,122][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:13,125][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:13,167][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:13,292][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric nature of this round where both you and Alice have high values for different items, a cooperative strategy might be effective. We can propose a fair split that maximizes the total points for both of us. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:13,311][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric and competitive nature of the per-item values, it's crucial to consider the potential for proportional allocation if the total proposed amounts exceed the item quantities. However, given that both Alice and I value hats and books relatively highly, a strategic proposal might involve slightly underproposing to avoid splitting items proportionally. Proposal: 9 hats, 9 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:13,337][mllm.models.large_language_model_local][WARNING] - Response Given the high value that both you and Alice place on books and balls, while the hats have a relatively low value for both of you, a cooperative strategy might be beneficial. Here's a proposal that allocates the items in a way that maximizes the total points for both of you while avoiding overclaiming: Proposal: 10 hats, 10 books, 10 balls This split ensures that you both get to keep all the items, and the points are maximized based on your and Alice's valuations. However, to be more strategic and avoid the possibility of overclaiming, let's consider a slightly different split that still ensures you both receive a fair share: Proposal: 10 hats, 10 books, 9 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:13,340][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:13,344][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:13,432][mllm.models.large_language_model_local][WARNING] - Response Given the values, it is in my best interest to maximize the items I value highly (books and balls) while still providing a fair split to ensure the allocation is not disproportionately in my favor. Here is the proposal: Proposal: 10 hats, 10 books, 10 balls This ensures that both Alice and I will not receive a lower share that would be less than our proportional value, which could lead to more points for both of us in the long run. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:13,451][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:13,454][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:13,473][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:13,476][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:13,479][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:13,482][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:13,554][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:13,557][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:13,560][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:13,563][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:13,644][mllm.models.large_language_model_local][WARNING] - Response Given that both you and Bob have the same preferences for hats and books, and the same low value for balls, the optimal strategy would be to ensure that both you and Bob get the items you value highly. Here's the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:13,648][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:13,650][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:13,698][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:13,701][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:13,704][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:13,706][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:13,709][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:13,720][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:13,723][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:13,746][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:13,749][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:13,752][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:13,754][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:13,757][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:13,821][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:13,848][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:13,919][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:13,933][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:13,936][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:14,093][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric nature of the per-item values and the fact that both Alice and I have the same optimal strategy in this configuration, I will propose: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:14,096][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:14,099][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:14,102][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:14,108][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:14,115][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:14,121][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:14,153][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:14,156][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:14,159][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:14,175][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:14,178][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:14,181][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:14,183][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:14,197][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both you and Bob place high value on books (10 points each) and balls (10 points each), while hats have a lower value for both (1 point each). To maximize your points, you should propose to take all the books and balls, and share the hats equally. Proposal: 5 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:14,200][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:14,203][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:14,205][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:14,219][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:14,222][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:14,224][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:14,227][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:14,230][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:14,260][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in values and the previous outcome where both you and Bob proposed the same split and earned the same points, it seems optimal to keep the strategy that maximizes our individual values. Since hats and books have higher values for you and balls have higher values for Bob, proposing the full allocation would maximize the points. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:14,293][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:14,310][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:14,313][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:14,463][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:14,466][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:14,514][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:14,517][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:14,534][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and Bob's strategy in previous rounds, it seems that both parties value hats and books highly, while balls are valued less by both but differently. To maximize your points, it's important to align your proposal with the value each item has for you. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:14,541][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:14,544][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:14,547][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:14,551][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:14,595][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the situation and the fact that both Alice and I value books the most, it makes sense to ensure we each get a significant portion of the books. However, we should also consider the other items to avoid any proportional loss if the total proposals exceed the item quantity. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:14,597][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:14,603][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:14,608][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:14,677][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:14,683][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:14,690][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:14,696][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:14,760][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:14,763][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:14,769][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:14,775][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:14,846][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:14,853][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:14,860][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:14,867][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:14,873][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:15,093][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:15,116][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:15,119][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:15,139][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:15,229][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:15,314][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:15,317][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:15,476][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it is clear that balls are highly valued by both you and Bob, but you value books more than hats, while Bob values hats more than books. To maximize my points, I should try to secure as many balls as possible, since they offer the highest value to both of us. Here is a strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:15,543][mllm.models.large_language_model_local][WARNING] - Response Given that both you and Alice value hats and books highly, and balls moderately, a cooperative strategy might be most beneficial. Proposing to divide the items evenly would likely lead to the best outcome for both parties. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:15,584][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:15,587][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:15,590][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:15,749][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the situation and Alice's different valuations, a strategic proposal would be to maximize the points based on the per-item values. Since the valuations are highly skewed, a proposed split that leverages these differences could yield higher points. Proposal: 10 hats, 10 books, 10 balls However, to consider a more strategic move, we could propose to take an advantage of Alice's lower valuation for hats and books, and higher valuation for balls. Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:15,875][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric and complementary nature of the values, proposing an equal distribution seems to be a fair and strategic approach. Here's the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:15,992][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric and high value of hats and books for both parties, it's crucial to preempt Alice's strategy by proposing a split that maximizes our points. Let's propose an allocation that both acknowledges the high value of hats and books and ensures we capture the full value of balls. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:16,011][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:16,014][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:16,048][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and equal per-item values for the total pool in both our favor, proposing the full allocation seems reasonable. Here’s the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:16,066][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:16,087][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:16,108][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:16,110][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:16,114][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:16,117][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:16,120][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:16,122][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:16,125][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:16,128][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:16,144][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's beneficial to maximize the items with higher personal value. Here, books have the highest value for both of us, followed by hats, and then balls. Since both of our valuations for books are high, it makes sense to propose keeping all the books for ourselves to maximize our points. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:16,147][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:16,150][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:16,153][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:16,155][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:16,158][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:16,160][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:16,180][mllm.models.large_language_model_local][WARNING] - Response Given the distribution of values, it's clear that both you and Bob have high values for different items. You value hats and books highly, while Bob values books and balls highly. To maximize your points, you should consider proposing a split that aligns with your higher values and negotiate a fair deal that doesn't leave too much value on the table. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:16,183][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:16,186][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:16,188][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:16,192][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:16,204][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:16,207][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:16,328][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:16,332][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:16,346][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:16,362][mllm.models.large_language_model_local][WARNING] - Response Given that both you and Alice value different items highly, a strategy that maximizes the value of the items you receive while considering the proportional allocation in case of over-proposal can be effective. Here, you value hats and balls highly (10) and Alice values books highly (10). A balanced proposal that considers the high value items and the potential for proportional distribution can be: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:16,393][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:16,396][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:16,446][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:16,464][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:16,482][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:16,485][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:16,488][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:16,494][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:16,501][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:16,507][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:16,510][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:16,513][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:16,519][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:16,522][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:16,525][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:16,527][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:16,531][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:16,537][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:16,540][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:16,543][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:16,545][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:16,560][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:16,563][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:16,566][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:16,598][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that the balls are the most valuable for you, followed by the books, and then the hats. Given Bob's values, he might try to snatch the balls for himself. A strategic proposal would be to secure as many of the balls as possible while also taking into account the likely behavior of Bob. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:16,657][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:16,660][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:16,673][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:16,688][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:16,719][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:16,725][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:16,735][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:16,738][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:16,751][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:16,754][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:16,770][mllm.models.large_language_model_local][WARNING] - Response Given the updated per-item values, where my values for hats, books, and balls are 1, 10, and 10 respectively, and Bob's values are 10, 10, and 1, it makes strategic sense to propose a distribution that maximizes the value based on our personal valuations. Here’s the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:16,773][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:16,790][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:16,805][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:16,811][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:16,822][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:16,840][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the high value of books for Alice and balls for you, a strategic split could involve trying to maximize the allocation of items where the values are more positive for one party. Here is a proposed split that aims to balance the items, considering the high value of books for Alice and the high value of balls for you: Proposal: 10 hats, 10 books, 10 balls However, to slightly favor your own advantage, slightly reducing the books and increasing the balls can be a strategy. Here’s an adjusted proposal: Proposal: 10 hats, 9 books, 11 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:16,844][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:16,850][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:16,856][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:16,863][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:16,869][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:16,875][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:16,881][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:16,887][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:16,893][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:16,898][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:16,909][mllm.models.large_language_model_local][WARNING] - Response Given the distribution of values, it is strategical to propose a split that accounts for the high values both you and Bob place on the books and hats, while also considering the lower value placed on balls. Proposition: 5 hats, 10 books, 5 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:16,911][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:16,916][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:16,920][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:16,925][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:16,985][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:16,991][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:17,005][mllm.models.large_language_model_local][WARNING] - Response Given Alice's values for books and balls, it might be advantageous to focus on the items where we both have high values to ensure we secure a larger share. Here's a strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:17,008][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:17,026][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:17,098][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:17,101][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:17,115][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:17,118][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:17,134][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in this round, where both you and Alice have the same per-item values for hats and books, and both value balls equally, the best strategy is to mirror Alice's proposal to ensure a fair outcome and maximize points. Therefore, the proposal will be: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:17,140][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:17,167][mllm.models.large_language_model_local][WARNING] - Response Given the values, it makes strategic sense to attempt to maximize the points by considering the high value of books and balls. Here's a proposal that balances the high-value items: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:17,351][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:17,434][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric and high value of books and hats for both Alice and me, it's beneficial to split all items evenly to maximize points. Therefore, the optimal strategy is to propose the same distribution as the previous rounds. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:17,438][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:17,457][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both you and Alice value the "books" the most. However, you value "books" significantly more than Alice, and Alice values "balls" much more than you do. A strategic proposal would be to maximize your own value while accounting for Alice's valuation. Proposal: 10 hats, 10 balls, 10 books This proposal allocates all items to both of us, recognizing Alice's high valuation of "books" and your own valuations. Since the total quantity of items is 30 and you are proposing to take 20 out of 30 items, this is a fair and balanced proposal that aims to maximize your points based on your valuations. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:17,460][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:17,463][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:17,530][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:17,536][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:17,549][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:17,552][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:17,555][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:17,561][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:17,573][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:17,655][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:17,658][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:17,660][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:17,663][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:17,692][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:17,709][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:17,712][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:17,727][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:17,730][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:17,733][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:17,736][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:17,816][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:17,837][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:17,840][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:17,842][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:17,845][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:17,856][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:17,860][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:17,862][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:17,904][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that balls are very valuable to you while hats and books have lower values. To maximize your points, you should focus on securing as many balls as possible. Here is a revised proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:17,907][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:17,910][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:17,928][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:17,968][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:17,971][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:18,008][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:18,011][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:18,013][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:18,016][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:18,043][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:18,057][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:18,060][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:18,073][mllm.models.large_language_model_local][WARNING] - Response Given the values, you are more valuable for hats (1) and Bob is more valuable for books and balls (10 each). To maximize your points, you should propose to keep all the hats and divide the books and balls in a way that maximizes your value. Since you have the same per-item value for hats as Bob, you can keep all the hats. For books and balls, you should propose to keep more of the items you value more. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:18,076][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:18,079][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:18,082][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:18,085][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:18,166][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:18,169][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:18,176][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:18,184][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:18,187][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:18,224][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:18,231][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:18,238][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:18,246][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:18,249][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:18,255][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:18,258][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:18,261][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:18,284][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:18,287][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:18,299][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:18,305][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:18,312][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:18,318][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:18,346][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:18,353][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:18,362][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:18,364][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:18,371][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:18,377][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:18,386][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:18,399][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both hats and books have higher values for you compared to Alice, while balls have a lower value for you but a higher value for Alice. To maximize your points, it makes sense to propose a distribution that reflects the higher value of hats and books to you. Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:18,460][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:18,477][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:18,484][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:18,572][mllm.models.large_language_model_local][WARNING] - Response Given the values, you should prioritize books and balls since they have higher values for you. Here's a strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:18,579][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:18,586][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:18,588][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:18,591][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:18,597][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:18,603][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:18,661][mllm.models.large_language_model_local][WARNING] - Response Given the values, I should prioritize the balls since they have the highest value for me. Here is the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:18,676][mllm.models.large_language_model_local][WARNING] - Response Given the high value of hats and books for both you and Bob, and the lower value of balls, we can exploit the asymmetry in values to maximize our points. Since both you and Bob value hats and books similarly but balls are valued much more by Bob, a strategic proposal would be to allocate more hats and books to yourself and more balls to Bob. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:18,679][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:18,709][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that hats have low value for both of us, books have high value for you and low for Bob, and balls have high value for both of us. To maximize your points, it makes sense to propose keeping most of the balls and splitting the books and hats evenly. Here's a strategy: - Take all 10 balls (since they are valuable for both of us). - Split the 10 hats and 10 books evenly to ensure you capture a significant portion of their value. Thus, the proposal would be: Proposal: 10 balls, 5 hats, 5 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:18,712][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:18,787][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:18,791][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:18,851][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:18,858][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:18,865][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:18,900][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:18,902][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:18,944][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:18,986][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:19,093][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that the books are highly valuable to both of us, and the balls are less valuable. Here's a proposal that aims to maximize the value based on our respective values for each item: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:19,100][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:19,103][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:19,196][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Alice place significant value on different items. You value hats and balls highly, while Alice values books and balls. To maximize your points, it would be strategic to propose a distribution that you can secure a majority of the items you value. Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:19,199][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:19,202][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:19,205][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:19,208][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:19,211][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:19,213][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:19,218][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:19,234][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:19,263][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:19,267][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:19,294][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:19,367][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:19,374][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:19,431][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:19,444][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:19,448][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:20,089][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:20,093][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:20,095][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:20,098][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:20,100][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:20,107][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:20,198][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:20,202][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:20,212][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:20,226][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:20,229][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:20,389][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:20,402][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:20,409][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:20,417][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:20,426][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:20,434][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:20,440][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:20,448][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:20,451][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:20,473][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:20,480][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:20,489][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it is clear that both you and Bob value hats the least and balls the most. To maximize your points, it would be wise to propose keeping as many of the high-value items (balls) as possible. However, since both you and Bob value balls highly, you'll need to propose a fair enough split to avoid splitting them proportionally. Here's a strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:20,490][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:20,507][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:20,508][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:20,610][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:20,614][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:20,660][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:20,716][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:20,718][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:20,752][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:20,767][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:20,850][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and the symmetry in the game, a good strategy would be to propose an allocation that maximizes the total points for both you and Bob. Since your values for hats and books are equal to Bob's values for books and hats, respectively, and your value for balls is high while Bob's value for balls is also high, it makes sense to propose an equal split. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:20,879][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:20,883][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:20,989][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:20,993][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:21,005][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:21,012][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:21,019][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:21,023][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:21,037][mllm.models.large_language_model_local][WARNING] - Response Given the current per-item values, you have a high value for hats (10) and low values for books (1) and balls (1). Bob has a high value for balls (10) and low values for hats (1) and books (1). In this situation, proposing to take all the hats and balls would maximize your points. Proposal: 10 hats, 10 balls, 0 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:21,051][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:21,148][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:21,155][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:21,376][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:21,389][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:21,438][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:21,442][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:21,533][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:21,537][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:21,619][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:21,701][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:21,782][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:21,786][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:21,991][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, the optimal strategy would be to propose an allocation that maximizes the value based on our high valuation for books and balls, while also considering Bob's high valuation for hats and balls. Here's the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:22,152][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:22,156][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:22,160][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:22,166][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:22,174][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:22,177][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:22,264][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:22,278][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:22,305][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:22,308][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:22,385][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:22,388][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:22,401][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:22,408][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:22,454][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric nature of the game and the values provided, where both you and Bob value the hats and books highly and the balls minimally, a cooperative approach seems optimal. Since both of us value the same items, we can split the balls equally to avoid splitting the items that have high value. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:22,491][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:22,516][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:22,529][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:22,532][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:22,534][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:22,611][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:22,619][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:22,639][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:22,650][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:22,663][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and Bob's strategy, it seems he values books significantly more than hats, and hats more than balls. To maximize my points, I should attempt to secure the books while giving up hats, as my value for hats and balls is very low. Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:22,675][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:22,711][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:22,737][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:22,750][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:22,753][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:22,760][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:22,862][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:22,875][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:22,900][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:22,938][mllm.models.large_language_model_local][WARNING] - Response Given that both you and Bob value the items differently and we have achieved equal earnings in previous rounds by splitting all items equally, let's analyze the current situation: - You value hats (1), books (10), and balls (10). - Bob values hats (10), books (1), and balls (10). Given these valuations, it might be beneficial to propose a split that maximizes your total points. A strategic approach could be to propose keeping more items with high value to you, while Bob keeps items he values highly. Proposal: 10 hats, 10 balls, 10 books This proposal keeps all items, which should result in the highest possible points for you since you value books and balls highly. However, since Bob values hats and balls highly, he might counter propose to keep the hats and balls for himself, splitting the books. A more balanced and strategic approach would be to propose a split that accounts for both our valuations and potentially earns more points for you. Proposal: 10 hats, 10 balls, 9 books This proposal keeps all items, and since you value books the most, you get 9 books, maximizing your points. Please confirm if this proposal is acceptable. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:22,951][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that hats are moderately valuable to you (10 points), books are the least valuable to you (1 point), and balls are also valuable to you (1 point). However, Bob values books and balls significantly more than hats. To maximize your points, you should propose to take the more valuable items to you. Since books and balls are equally valuable to you and more valuable to Bob, you should propose to take most of the books and balls, while keeping a few hats. Proposal: 10 hats, 10 books, 10 balls However, considering Bob's values, a better strategy might be to take the majority of the books and balls while splitting the hats to ensure the total quantity is met without overproposing on hats. Proposal: 5 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:22,963][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:22,975][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:23,016][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the values and the rounds so far, it seems that proposing the items equally is the strategy that maximizes points for both parties. Therefore, I will continue to propose the items equally. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:23,127][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:23,143][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:23,206][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:23,218][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:23,274][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:23,288][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:23,437][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:23,473][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:23,485][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:23,669][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:23,672][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:23,694][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:23,706][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:23,728][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:23,732][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:23,775][mllm.models.large_language_model_local][WARNING] - Response Given that both you and Bob have similar values for the items and the optimal strategy seems to be to propose equal shares to avoid any proportional sharing that would reduce your earnings, I will continue to propose an equal split. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:23,875][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:23,898][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:23,927][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's advantageous to propose keeping all the items to maximize your points, as the balls have the highest value for you and the hats and books have low value. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:23,940][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:23,943][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:24,121][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:24,133][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:24,141][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:24,496][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in values and the results of previous rounds, it seems proposing to take all items might result in a fair split. Let's propose to take all items to ensure we maximize our points based on our high values for books and balls. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:24,519][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric nature of the per-item values and the fact that both you and Bob will propose the same quantities to maximize your respective values, the optimal strategy is to propose the entire set of items. Therefore: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:24,622][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the fact that we both value hats and books similarly, and balls very little, it makes sense to propose an equitable split that maximizes our combined value. Let's propose to take all of the hats and balls, and split the books equally. Proposal: 10 hats, 10 balls, 10 books / 2 = 10 hats, 5 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:24,688][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:24,753][mllm.models.large_language_model_local][WARNING] - Response Proposals that simply split all items equally might not be optimal given the uneven value distributions. Since hats and books have higher values for Bob, and balls have a higher value for you, it might be strategic to focus on ensuring you get as many valuable balls as possible. Given your per-item values and Bob's, it would be wise to propose keeping as many balls as possible while still proposing something reasonable for hats and books to avoid making an easy split for Bob. Proposals: - Hats: You value them at 1, so propose keeping a small number to avoid giving Bob the chance to take them all. - Books: These are valued more by Bob, so keep a few for yourself to maintain some value. - Balls: These are highly valuable to you, so keep as many as possible. Proposal: 1 hat, 1 book, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:24,816][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:24,986][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both hats and books have a significantly lower value for you compared to Bob, while balls have a much higher value for you compared to Bob. The optimal strategy would be to maximize your score by taking as many balls as possible, while splitting hats and books proportionally. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:25,143][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:25,154][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:25,293][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:25,296][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:25,334][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:25,345][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:25,472][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:25,474][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:25,523][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:25,532][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:25,546][mllm.models.large_language_model_local][WARNING] - Response Given the situation, it's clear that splitting the items equally maximizes the points for both Alice and Bob in each round due to the symmetry in per-item values. Therefore, the optimal strategy is to propose the same distribution as the items available. Propostion: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:25,684][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:25,691][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:26,041][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:26,053][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:26,172][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:26,173][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:26,227][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:26,237][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:26,368][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:26,371][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:26,421][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:26,434][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:26,561][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:26,569][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:26,597][mllm.models.large_language_model_local][WARNING] - Response Given the values and knowing that both Alice and Bob will propose to take all items due to the symmetric nature of the game and the desire to maximize points, a prudent strategy is to propose an equal split that maximizes our chance of receiving a fair allocation. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:27,371][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both you and Bob value hats highly, while both of you value balls the least. To maximize points, we should try to take as many hats and books as possible while acknowledging that the balls will be valued lower. Here’s a strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:06:27,834][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric nature of the values and the previous rounds where proposing all items was successful, I will propose the same strategy to maximize my points. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:06:29,004][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's beneficial to secure the items you value highly and also ensure Bob values them less. Here, hats and books are valued similarly by both of us, but balls are valued much more by you and much less by Bob. A strategic proposal would be to take all the hats and books, and a significant portion of the balls to maximize your points. Proposer: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:06:30,218][__main__][INFO] - Number of regex retries in iteration 572: 1934 [2025-11-13 07:06:30,219][__main__][INFO] - agents played in iteration 572 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 07:06:31,188][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 07:06:31,207][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 07:06:31,228][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 07:06:31,248][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.57%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 07:06:31,249][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 07:06:31,250][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 07:06:32,095][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 07:06:32,559][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 07:06:33,063][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 07:06:33,562][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 07:06:34,065][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 07:06:34,563][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 07:06:35,067][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 07:06:35,568][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 07:06:36,069][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 07:06:36,571][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 07:06:37,073][mllm.training.trainer_common][INFO] - 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Processing mini-batch 43 of 64 [2025-11-13 07:06:54,028][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 07:06:54,526][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 07:06:55,020][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 07:06:55,519][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 07:06:56,029][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 07:06:56,526][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 07:06:57,027][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 07:06:57,539][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 07:06:58,042][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 07:06:58,540][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 07:06:59,038][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 07:06:59,536][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 07:07:00,041][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 07:07:00,540][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 07:07:01,035][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 07:07:01,532][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 07:07:02,025][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 07:07:02,531][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 07:07:03,028][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 07:07:03,521][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 07:07:04,034][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10903 tokens. [2025-11-13 07:07:04,820][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.20%, Current % of VRAM taken: 59.66%, Block Peak % of device VRAM: 62.53%, ΔTime: 00:00:32 [2025-11-13 07:07:05,449][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 07:07:05,450][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 07:07:05,452][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 07:07:06,227][__main__][INFO] - Iteration 573 took 1m 38s (63.33% Gen, 35.88% Train). Generation: 1m 2s, Training: 35s. Estimated remaining time: 72h 52m 45s. Estimated total time: 81h 50m 9s. Time estimates for 10 more iterations: 16m 22s, 100 more iterations: 2h 43m 40s, 500 more iterations: 13h 38m 21s. [2025-11-13 07:07:06,229][__main__][INFO] - Starting iteration 573. [2025-11-13 07:07:06,703][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 57 and human policies 1. [2025-11-13 07:07:06,703][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 07:07:27,908][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:27,933][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:27,936][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:27,939][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:27,942][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:27,946][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:27,950][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:27,952][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:27,955][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:27,958][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:27,961][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:27,964][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:27,967][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:27,969][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:27,972][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:27,981][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:27,983][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:27,986][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:27,988][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:27,989][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:27,992][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:27,995][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:27,997][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:27,998][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:28,000][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:28,002][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:28,005][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:28,007][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:28,016][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:28,018][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:28,019][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:28,021][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:28,023][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:28,025][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:28,026][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:28,028][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:28,029][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:28,031][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:28,033][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:28,035][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:28,036][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:28,037][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:28,039][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:28,040][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:28,041][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:28,045][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:28,046][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:28,047][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:28,049][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:28,050][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:28,052][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:28,054][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:28,056][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:28,058][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:28,060][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:28,061][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:28,070][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:28,073][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:28,075][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:28,077][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:28,078][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:28,079][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:28,081][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:28,082][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:28,083][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:28,084][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:28,084][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:28,085][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:28,087][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:28,089][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:28,091][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:28,092][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:28,093][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:28,095][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:28,097][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:28,098][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:28,099][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:28,101][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:28,103][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:28,104][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:28,106][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:28,107][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:28,108][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:28,109][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:28,110][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:28,112][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:28,113][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:28,114][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:28,116][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:28,117][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:28,229][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:28,245][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:28,248][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:28,251][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:28,254][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:28,257][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:28,259][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:28,265][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:28,268][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:28,271][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:28,273][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:28,275][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:28,278][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:28,280][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:28,282][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:28,285][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:28,294][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:28,296][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:28,298][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:28,301][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:28,303][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:28,305][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:28,307][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:28,309][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:28,311][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:28,313][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:28,316][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:28,319][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:28,321][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:28,323][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:28,325][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:28,328][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:28,336][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:28,338][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:28,340][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:28,342][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:28,344][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:28,345][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:28,347][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:28,349][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:28,350][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:28,351][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:28,352][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:28,354][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:28,355][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:28,357][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:28,358][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:28,359][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:28,360][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:28,360][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:28,361][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:28,362][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:28,366][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:28,373][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:28,374][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:28,375][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:28,377][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:28,379][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:28,381][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:28,383][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:28,384][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:28,386][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:28,387][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:28,388][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:28,389][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:28,389][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:28,390][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:28,391][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:28,392][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:28,394][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:28,396][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:28,398][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:28,399][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:28,400][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:28,401][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:28,402][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:28,403][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:28,405][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:28,405][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:28,406][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:28,410][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:28,411][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:28,412][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:28,413][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:28,414][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:28,416][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:28,417][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:28,418][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:28,419][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:28,498][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:28,513][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:28,517][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:28,528][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:28,533][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:28,537][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:28,541][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:28,545][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:28,549][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:28,556][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:28,560][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:28,564][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:28,568][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:28,571][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:28,575][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:28,584][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:28,587][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:28,591][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:28,596][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:28,600][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:28,604][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:28,607][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:28,610][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:28,614][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:28,617][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:28,621][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:28,628][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:28,631][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:28,634][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:28,636][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:28,639][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:28,641][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:28,644][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:28,646][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:28,648][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:28,651][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:28,653][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:28,658][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:28,661][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:28,663][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:28,666][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:28,668][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:28,670][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:28,673][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:28,675][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:28,677][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:28,679][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:28,682][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:28,685][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:28,688][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:28,690][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:28,692][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:28,695][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:28,697][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:28,699][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:28,704][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:28,706][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:28,708][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:28,710][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:28,712][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:28,715][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:28,717][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:28,719][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:28,722][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:28,724][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:28,726][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:28,729][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:28,731][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:28,734][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:28,736][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:28,738][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:28,740][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:28,742][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:28,745][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:28,747][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:28,749][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:28,752][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:28,754][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:28,756][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:28,763][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:28,774][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:28,777][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:28,780][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:28,783][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:28,786][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:28,789][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:28,791][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:28,793][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:28,796][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:29,764][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:29,790][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:29,814][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:29,817][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:29,838][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:29,842][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:29,845][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:29,848][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:29,862][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:29,865][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:29,868][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:29,872][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:29,885][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:29,888][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:29,891][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:29,908][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:29,911][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:29,914][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:29,932][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:29,935][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:29,938][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:29,940][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:29,943][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:29,945][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:29,947][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:29,952][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:29,954][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:29,956][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:29,958][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:29,960][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:29,976][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:29,979][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:29,981][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:29,983][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:29,986][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:29,988][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:29,990][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:29,993][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:30,000][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:30,002][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:30,004][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:30,007][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:30,010][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:30,012][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:30,014][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:30,015][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:30,022][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:30,023][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:30,025][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:30,026][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:30,027][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:30,028][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:30,030][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:30,031][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:30,032][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:30,033][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:30,050][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:30,051][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:30,053][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:30,057][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:30,059][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:30,063][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:30,065][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:30,066][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:30,076][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:30,077][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:30,080][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:30,081][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:30,083][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:30,085][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:30,087][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:30,088][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:30,090][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:30,090][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:30,091][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:30,092][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:30,094][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:30,098][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:30,100][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:30,101][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:30,102][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:30,103][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:30,104][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:30,107][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:30,140][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:30,142][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:30,145][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:30,192][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:30,211][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:30,224][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:30,227][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:30,242][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:30,245][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:30,248][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:30,251][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:30,257][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:30,260][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:30,263][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:30,265][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:30,276][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:30,279][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:30,282][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:30,295][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:30,297][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:30,300][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:30,312][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:30,314][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:30,317][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:30,320][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:30,322][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:30,330][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:30,332][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:30,335][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:30,337][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:30,340][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:30,342][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:30,345][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:30,349][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:30,351][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:30,353][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:30,355][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:30,357][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:30,360][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:30,366][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:30,369][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:30,370][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:30,373][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:30,375][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:30,378][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:30,380][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:30,382][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:30,383][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:30,385][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:30,390][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:30,392][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:30,394][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:30,395][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:30,397][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:30,400][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:30,401][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:30,402][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:30,403][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:30,403][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:30,407][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:30,410][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:30,412][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:30,413][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:30,414][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:30,417][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:30,418][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:30,422][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:30,424][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:30,425][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:30,427][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:30,429][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:30,430][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:30,432][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:30,433][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:30,434][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:30,435][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:30,436][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:30,440][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:30,441][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:30,443][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:30,444][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:30,445][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:30,446][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:30,448][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:30,449][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:30,451][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:30,452][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:30,459][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:30,460][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:30,462][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:30,521][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:30,537][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:30,554][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:30,561][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:30,571][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:30,576][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:30,582][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:30,592][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:30,597][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:30,602][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:30,608][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:30,613][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:30,621][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:30,626][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:30,630][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:30,637][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:30,641][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:30,645][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:30,651][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:30,655][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:30,658][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:30,662][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:30,667][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:30,670][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:30,673][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:30,676][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:30,679][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:30,682][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:30,686][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:30,688][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:30,691][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:30,694][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:30,697][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:30,700][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:30,703][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:30,707][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:30,709][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:30,712][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:30,714][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:30,717][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:30,720][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:30,729][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:30,731][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:30,734][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:30,736][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:30,739][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:30,741][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:30,744][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:30,747][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:30,752][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:30,754][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:30,756][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:30,758][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:30,762][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:30,764][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:30,766][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:30,768][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:30,770][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:30,773][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:30,775][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:30,777][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:30,780][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:30,782][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:30,784][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:30,787][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:30,789][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:30,792][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:30,795][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:30,797][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:30,800][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:30,802][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:30,804][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:30,808][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:30,810][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:30,813][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:30,815][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:30,817][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:30,820][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:30,822][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:30,824][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:30,827][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:30,829][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:30,831][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:30,833][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:30,837][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:30,839][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:30,841][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:31,891][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:31,928][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:31,977][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:31,980][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:32,000][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:32,027][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:32,030][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:32,033][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:32,036][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:32,039][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:32,049][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:32,052][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:32,054][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:32,075][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:32,079][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:32,082][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:32,084][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:32,098][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:32,100][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:32,103][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:32,106][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:32,124][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:32,127][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:32,129][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:32,132][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:32,135][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:32,138][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:32,139][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:32,141][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:32,149][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:32,151][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:32,153][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:32,155][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:32,158][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:32,160][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:32,163][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:32,171][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:32,173][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:32,176][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:32,178][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:32,180][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:32,182][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:32,184][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:32,186][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:32,192][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:32,194][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:32,196][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:32,197][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:32,198][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:32,199][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:32,200][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:32,201][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:32,202][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:32,203][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:32,205][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:32,213][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:32,215][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:32,217][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:32,219][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:32,220][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:32,221][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:32,223][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:32,225][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:32,227][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:32,229][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:32,236][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:32,238][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:32,240][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:32,242][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:32,244][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:32,246][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:32,247][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:32,249][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:32,252][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:32,253][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:32,256][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:32,260][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:32,262][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:32,263][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:32,265][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:32,266][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:32,268][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:32,269][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:32,293][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:32,295][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:32,312][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:32,327][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:32,363][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:32,366][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:32,379][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:32,397][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:32,400][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:32,403][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:32,406][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:32,413][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:32,416][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:32,418][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:32,420][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:32,431][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:32,434][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:32,436][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:32,438][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:32,447][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:32,449][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:32,452][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:32,454][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:32,463][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:32,465][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:32,467][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:32,469][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:32,481][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:32,483][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:32,485][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:32,487][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:32,489][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:32,491][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:32,493][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:32,494][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:32,499][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:32,501][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:32,502][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:32,503][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:32,504][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:32,505][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:32,509][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:32,510][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:32,518][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:32,520][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:32,522][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:32,523][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:32,525][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:32,527][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:32,528][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:32,530][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:32,532][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:32,534][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:32,536][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:32,540][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:32,542][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:32,544][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:32,546][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:32,548][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:32,549][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:32,552][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:32,553][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:32,555][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:32,560][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:32,562][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:32,564][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:32,566][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:32,568][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:32,569][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:32,570][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:32,570][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:32,578][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:32,580][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:32,582][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:32,583][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:32,585][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:32,587][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:32,589][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:32,591][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:32,592][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:32,597][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:32,599][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:32,615][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:32,618][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:32,633][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:32,649][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:32,691][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:32,697][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:32,708][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:32,725][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:32,731][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:32,737][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:32,748][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:32,752][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:32,757][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:32,764][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:32,772][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:32,776][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:32,781][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:32,784][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:32,791][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:32,795][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:32,799][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:32,803][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:32,810][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:32,814][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:32,817][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:32,820][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:32,830][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:32,834][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:32,837][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:32,840][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:32,843][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:32,852][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:32,855][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:32,858][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:32,861][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:32,864][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:32,867][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:32,871][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:32,874][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:32,877][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:32,880][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:32,886][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:32,889][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:32,892][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:32,895][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:32,898][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:32,900][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:32,903][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:32,908][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:32,911][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:32,915][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:32,917][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:32,920][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:32,923][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:32,926][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:32,929][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:32,931][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:32,934][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:32,938][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:32,941][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:32,944][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:32,946][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:32,949][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:32,952][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:32,954][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:32,957][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:32,959][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:32,963][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:32,966][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:32,968][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:32,970][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:32,972][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:32,975][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:32,976][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:32,979][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:32,981][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:32,987][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:32,990][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:32,992][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:32,995][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:32,997][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:33,015][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:33,018][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:33,334][mllm.models.large_language_model_local][WARNING] - Response Given the new per-item values, you value hats and balls significantly higher than books, while Alice values books and balls more than hats. To maximize your points, you should propose to take all hats and all of the balls, and split the books in a way that is fair but still benefits you more. Proposal: 10 hats, 10 balls, 5 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:33,541][mllm.models.large_language_model_local][WARNING] - Response Given the new valuations, it makes sense to propose a distribution that reflects the higher value you place on hats and Alice places on books and balls. Here is a strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:34,025][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:34,057][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:34,162][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:34,165][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:34,191][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:34,236][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:34,239][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:34,268][mllm.models.large_language_model_local][WARNING] - Response Given the updated values, where both you and Bob value hats and books at 10 and balls at 1, the optimal strategy would be to maximize the items you propose to keep since they have the highest value for both of you. Here's the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:34,271][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:34,274][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:34,277][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:34,280][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:34,283][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:34,286][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:34,289][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:34,296][mllm.models.large_language_model_local][WARNING] - Response Given the new per-item values where I value hats and balls highly and Alice values books and balls highly, I should propose a distribution that reflects these values to maximize my points. My proposal would be: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:34,301][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:34,303][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:34,306][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:34,308][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:34,310][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:34,311][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:34,312][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:34,314][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:34,321][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:34,323][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:34,325][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:34,345][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:34,347][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:34,369][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:34,371][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:34,373][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:34,374][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:34,375][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:34,398][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:34,400][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:34,402][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:34,404][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:34,407][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:34,409][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:34,411][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:34,412][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:34,415][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:34,417][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:34,419][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:34,424][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:34,426][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:34,428][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:34,429][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:34,430][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:34,432][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:34,434][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:34,435][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:34,442][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:34,444][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:34,445][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:34,446][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:34,448][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:34,449][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:34,450][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:34,452][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:34,453][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:34,455][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:34,457][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:34,463][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:34,465][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:34,467][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:34,468][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:34,469][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:34,501][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:34,502][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:34,504][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:34,520][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:34,522][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:34,523][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:34,570][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:34,572][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:34,586][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:34,621][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:34,623][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:34,638][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:34,643][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:34,646][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:34,649][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:34,651][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:34,657][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:34,659][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:34,662][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:34,664][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:34,666][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:34,667][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:34,672][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:34,674][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:34,676][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:34,682][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:34,683][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:34,684][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:34,685][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:34,686][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:34,697][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:34,698][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:34,733][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:34,735][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:34,738][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:34,754][mllm.models.large_language_model_local][WARNING] - Response Given the new values, where both you and Bob have a high valuation for hats and books and a lower valuation for balls, it's important to consider that the proportional allocation in case of over-proposing could significantly lessen the outcome. Considering this, a strategy to secure as many high-value items as possible while leaving the lower-value balls to Bob might be optimal. Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:34,755][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:34,757][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:34,758][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:34,759][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:34,760][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:34,762][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:34,763][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:34,792][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:34,793][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:34,796][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:34,797][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:34,799][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:34,800][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:34,802][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:34,803][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:34,804][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:34,806][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:34,814][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:34,816][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:34,818][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:34,820][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:34,821][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:34,823][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:34,824][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:34,825][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:34,826][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:34,827][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:34,828][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:34,834][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:34,835][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:34,836][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:34,838][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:34,839][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:34,841][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:34,873][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:34,877][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:34,879][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:34,891][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:34,894][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:34,945][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:34,949][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:34,961][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:34,997][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:35,002][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:35,015][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:35,033][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:35,036][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:35,039][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:35,043][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:35,047][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:35,050][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:35,055][mllm.models.large_language_model_local][WARNING] - Response Given the new per-item values, it seems that both of us place a high value on different items. To maximize my points, I should focus on the items I value more while still considering the possibility of Alice proposing to keep items I value. Proposal: 10 hats, 10 books, 10 balls This proposal ensures that I get the items I value more (hats and books) and still have the chance to get the balls, although they are less valuable to me compared to hats and books. However, to be strategic, I might consider slightly reducing the number of books and balls to slightly inflate the value I get for the items I value more, while still ensuring that the total does not exceed 30 (since the sum of items is 30). Proposal: 10 hats, 9 books, 11 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:35,057][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:35,060][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:35,063][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:35,066][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:35,067][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:35,069][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:35,072][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:35,076][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:35,079][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:35,083][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:35,091][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:35,094][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:35,100][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:35,102][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:35,105][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:35,107][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:35,121][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:35,124][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:35,127][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:35,129][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:35,133][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:35,135][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:35,137][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:35,140][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:35,144][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:35,146][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:35,148][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:35,150][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:35,152][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:35,154][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:35,156][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:35,158][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:35,160][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:35,163][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:35,172][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:35,174][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:35,176][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:35,178][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:35,180][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:35,183][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:35,185][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:35,187][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:35,190][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:35,192][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:35,194][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:35,200][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:35,202][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:35,205][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:35,207][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:35,209][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:35,212][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 books, 10 hats, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:35,251][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:35,255][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:35,278][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:35,376][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the fact that both you and Alice have the same values for hats and balls, and books and balls respectively, proposing a fair split seems reasonable to maximize points. Here’s the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:35,449][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric nature of the valuation and the item distribution, a fair proposal that aims to maximize points by balancing the split would be: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:35,580][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:35,640][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and knowing Bob's values, a strategic approach would be to negotiate the allocation to maximize my points. Since my values for books and balls are the same as Bob's but inverted, and my value for hats is the same as Bob's, the key is to secure more of the items I value more. Here's the breakdown: - Hats: My value = Bob's value = 1, so分配策略应倾向于保有一定数量的帽子。 - Books: 我的价值 = 10, 而Bob的价值 = 1，因此我应当争取尽可能多的书籍。 - Balls: 我的价值 = 1, 而Bob的价值 = 10，因此我应当争取尽可能少的球，或者与Bob协商争取一些球。 基于以上价值分配，一个合理的投标策略是争取尽可能多的书籍，并且和Bob商量争取获得一些球。对于帽子，我们可以分配相同数量，以确保如果数量分配不均，可以根据后续的策略调整。 因此，我的提案为：2 hats, 8 books, 1 balls。这意味着我保留了相对较少的帽子，但争取了较多的书籍和少量的球。这样做可以确保在出现分步不平衡时我仍然能得到有价值的书籍。 提案：2 hats, 8 books, 1 ball. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:35,642][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:35,643][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:35,699][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:35,701][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:35,781][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the fact that both you and Alice value hats and balls highly, and books moderately, a cooperative proposal might be the best strategy. Splitting the items evenly is a fair approach and would maximize the points for both players. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:35,783][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:35,859][mllm.models.large_language_model_local][WARNING] - Response Given the new per-item values where I value books significantly more than hats or balls, and Alice values hats more than books or balls, I should propose a strategy that maximizes my points by focusing on the items I value more. Here’s the optimal proposal: Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:36,294][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the high value Alice places on hats and balls while you value books relatively highly, a strategic proposal would be to recognize that proposing a fair split might still leave both parties satisfied. However, to maximize your points, you should aim to slightly outbid Alice in the items you value more. Proposal: 10 hats, 10 books, 9 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:36,381][mllm.models.large_language_model_local][WARNING] - Response Given the values and the symmetry in the situation, both you and Alice will likely propose the same distribution to maximize the points. Since you have a high value for hats and Alice has a high value for books and balls, proposing to take all of each type of item that you value highly seems like the optimal strategy. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:36,439][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the fact that both you and Alice value hats and balls highly, while both value books less, and the proportional allocation rule, it seems like proposing to take all items might lead to a fair allocation where both parties receive a benefit based on their high value for hats and balls. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:36,481][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:36,513][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:36,574][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:36,600][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:36,603][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:36,631][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:36,636][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:36,641][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:36,645][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:36,647][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:36,648][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:36,650][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:36,661][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:36,665][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:36,667][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:36,670][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:36,672][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:36,674][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:36,676][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:36,687][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:36,688][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:36,736][mllm.models.large_language_model_local][WARNING] - Response Given the new per-item values, where you value hats at 1 and books at 10, while Alice values hats at 10 and books at 1, the optimal strategy would be to ensure you receive as many books as possible, as they have much higher value for you. Here’s a proposal that maximizes your points: Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:36,738][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:36,740][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:36,743][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:36,745][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:36,781][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:36,783][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:36,786][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:36,787][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:36,788][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:36,790][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:36,791][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:36,792][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:36,794][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:36,795][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:36,797][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:36,799][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:36,829][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:36,832][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:36,833][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:36,834][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:36,835][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:36,838][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:36,840][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:36,843][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:36,845][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:36,853][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:36,856][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:36,858][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:36,860][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:36,877][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:36,899][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:36,902][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:36,903][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:36,905][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:36,973][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:36,991][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:37,015][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:37,034][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:37,073][mllm.models.large_language_model_local][WARNING] - Response Given the values where you and Bob both prioritize different items significantly, a more strategic approach might be necessary to maximize points. Here, you value hats and balls highly, while Bob values books and hats. Books are particularly valuable to you, but less so to Bob. A possible strategy is to propose slightly less of the highly valued items by the other player to ensure you get a fair share of the items you value more. Proposal: 10 hats, 10 books, 9 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:37,075][mllm.models.large_language_model_local][WARNING] - Response Given the new values, it seems both of us value different items more highly. To maximize my points, I should try to secure as many of the items I value more highly (hats and balls) while still splitting books fairly since Alice values them highly. Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:37,082][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:37,092][mllm.models.large_language_model_local][WARNING] - Response Given the updated per-item values, where my values are hats=10, books=1, balls=1 and Bob's values are hats=1, books=10, balls=1, I should prioritize keeping more hats and books as they provide higher value to me. Here’s my proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:37,095][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:37,097][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:37,114][mllm.models.large_language_model_local][WARNING] - Response Given the new values, where I value hats at 1 and balls at 10, while Bob values books at 1 and balls at 10, it makes sense to propose keeping all the balls and splitting the hats and books proportionally. Here’s a revised proposal: Proposal: 5 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:37,120][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:37,122][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:37,125][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:37,142][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:37,144][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:37,148][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:37,152][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:37,154][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:37,156][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:37,159][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:37,161][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:37,164][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:37,166][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:37,168][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:37,170][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:37,200][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:37,203][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:37,206][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:37,208][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:37,210][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:37,212][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:37,221][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:37,223][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:37,224][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:37,225][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:37,227][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:37,228][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:37,230][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:37,240][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:37,241][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:37,242][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:37,244][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:37,246][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:37,248][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:37,249][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:37,251][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:37,260][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:37,262][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:37,264][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:37,265][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:37,286][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:37,304][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the high value of hats and balls for both players, and the equal distribution of values for books, a balanced approach might be optimal. However, considering the value distributions, it might be beneficial to slightly prioritize items with higher individual value. Proposal: 10 hats, 10 books, 10 balls This proposal aims to maximize the distribution of high-value items while maintaining balance. However, let's refine it slightly to ensure we get a slight edge: Proposal: 11 hats, 9 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:37,306][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:37,307][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:37,309][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:37,359][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both hats and books have significantly higher values for Bob compared to balls. Balls have a much higher value for you. To maximize points, you should allocate more of the items with higher personal value to yourself. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:37,362][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:37,397][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:37,410][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:37,446][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:37,464][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:37,470][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:37,475][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:37,503][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:37,507][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:37,510][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:37,520][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:37,521][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:37,523][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:37,557][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:37,561][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:37,564][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:37,568][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:37,571][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:37,573][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:37,575][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:37,576][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:37,579][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:37,581][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:37,583][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:37,585][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:37,587][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:37,598][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:37,600][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:37,602][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:37,604][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:37,606][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:37,635][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:37,637][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:37,639][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:37,641][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:37,643][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:37,645][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:37,655][mllm.models.large_language_model_local][WARNING] - Response Given the new values, it seems that books are very valuable to Bob while balls are somewhat valuable to you. Let's propose to take advantage of this imbalance. Here's a strategic proposal: Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:37,656][mllm.models.large_language_model_local][WARNING] - Response Given the new values, you have a strong preference for hats and books, while Alice has a similar preference but for different items. Here is the optimal proposal to maximize your points: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:37,658][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:37,660][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:37,662][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:37,664][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:37,677][mllm.models.large_language_model_local][WARNING] - Response Given the new values where hats are highly valued by Bob and balls are highly valued by you, a strategic proposal might involve splitting the items in a way that takes advantage of these preferences while ensuring neither agent overclaims. Here is a proposed allocation: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:37,678][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:37,701][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:37,702][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:37,706][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:37,708][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:37,766][mllm.models.large_language_model_local][WARNING] - Response Given that my per-item value for hats and books is higher than Alice's, and lower for balls, a strategic proposal would be to maximize the items where I have a higher value. Here's the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:37,767][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:37,769][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:37,789][mllm.models.large_language_model_local][WARNING] - Response Given the new values, Alice places a higher value on books and balls, while I value hats more highly. To maximize my points, I should propose to take the majority of the hats, while splitting the books and balls more evenly. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:37,811][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:37,854][mllm.models.large_language_model_local][WARNING] - Response Given that both you and Alice place high values on hats and books, a competitive strategy would be to maximize your share of items that you value highly. Here's a proposal that aims to take advantage of the distribution of values: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:37,856][mllm.models.large_language_model_local][WARNING] - Response Given the values, you should focus on maximizing your points from the items you value highly, which are hats and books. Here is the optimal proposal: Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:37,892][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:37,913][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:37,937][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Bob value hats and books relatively highly, but you value balls more than Bob does. To maximize your points, you should try to secure as many of the balls as possible while still proposing a fair share of hats and books. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:37,940][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:37,942][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:37,997][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:37,999][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:38,120][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:38,127][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:38,134][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:38,140][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:38,174][mllm.models.large_language_model_local][WARNING] - Response Given the values, both you and Bob highly value books and balls. To maximize your points, you should try to secure as many high-value items (books and balls) as possible while also considering the total item constraints. Here's a strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:38,339][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:38,427][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric and competitive nature of the per-item values, it makes sense to propose an allocation that maximizes the points for both parties while ensuring both sides receive a fair share. Since both of us value hats and balls significantly more than books, we might want to ensure that we each get an equal share of hats and balls, and split the books proportionally. Proposal: 10 hats, 10 books, 10 balls This proposal ensures that both Alice and I get equal shares of the items we value highly (hats and balls) and splits the books, which we both value less, in a fair manner. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:38,518][mllm.models.large_language_model_local][WARNING] - Response Given the values, it seems optimal to focus on the items where you have a higher value and Bob has a lower value. Here, you have a higher value for hats and books, and Bob has a higher value for balls. A strategic proposal would be to take all of the items where you have a higher value and see if you can negotiate the balls. Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:38,599][mllm.models.large_language_model_local][WARNING] - Response Given the new values, where you have a high value for balls (10) and Bob has a high value for hats (10), it might be strategic to try to secure as many balls as possible to maximize your points. Here's the proposal: Proposal: 0 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:38,751][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:38,754][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:38,858][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:38,898][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:38,901][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:38,952][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:38,956][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:38,979][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:38,982][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:38,985][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:38,987][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:38,988][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:38,991][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:39,003][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:39,008][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:39,012][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:39,028][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:39,031][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:39,132][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in values and the previous outcomes, where both you and Alice propose the same distribution and end up with equal points, it suggests that proposing the entire allocation might still be beneficial if we assume Alice will follow a similar strategy. Therefore, the optimal proposal would be to take the entire allocation of hats, books, and balls. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:39,135][mllm.models.large_language_model_local][WARNING] - Response Given the updated values, where your per-item values for hats and books are relatively high compared to balls, and Alice values hats and balls much more highly, a strategic proposal might be to take more of the items you value highly while still considering the proportional sharing rule. Here's a proposed allocation: proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:39,138][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:39,141][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:39,173][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:39,176][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:39,196][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's beneficial to prioritize items that you value highly while also considering Alice's lower valuations for those items. Here, both you and Alice value hats and books highly, but you value balls more and Alice values them less. A strategic proposal could be: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:39,199][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:39,201][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:39,203][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:39,205][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:39,207][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:39,237][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that the books and balls are highly valued by Alice, while hats are valued less by both of us. To maximize my points, I should propose keeping all the items to capture the higher value of books and balls. Here is my proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:39,239][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:39,241][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:39,244][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:39,245][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:39,246][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:39,248][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:39,310][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values for this round, where both you and Alice have high value for hats and books but low value for balls, a strategic proposal would be to maximize the items you value the most. Here's the proposal: Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:39,312][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:39,314][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:39,353][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:39,360][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:39,568][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:39,588][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:39,605][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:39,606][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:39,632][mllm.models.large_language_model_local][WARNING] - Response Given the new per-item values, it seems that both you and Alice value hats and books more highly, while balls are valued less by both. However, since the values are balanced and the items are distributed equally, a symmetric proposal might yield the best results. Here is a proposal that attempts to balance the items based on their values: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:39,635][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:39,638][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:39,652][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values where hats are worth 1, books are worth 10, and balls are worth 1 for you, and hats are worth 1, books are worth 10, and balls are worth 10 for Bob, proposing an equal split might not be optimal. Since books have the highest value for both of us, it would be beneficial to secure as many books as possible. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:39,653][mllm.models.large_language_model_local][WARNING] - Response Given that both you and Bob value hats and balls highly, but you value books more than Bob values them, it's important to secure as many of the items you value highly as possible. Here’s a strategic proposal that maximizes your total points: Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:39,657][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:39,660][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:39,662][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:39,663][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:39,664][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:39,698][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both you and Alice place high value on different items. You value hats and balls highly, while Alice values books highly. To maximize your points, you should propose a distribution that aligns with your higher values while considering the possibility of Alice's allocation. To simplify and maximize your points, you can propose to take all the items that you value highly, i.e., hats and balls, and leave the books for Alice. Proposal: 10 hats, 10 balls, 0 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:39,700][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:39,738][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:39,739][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:39,761][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:39,783][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:39,805][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:39,808][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:39,809][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:39,825][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric and high value of books and balls for you and the low value of hats for both, a strategic split might be to maximize the high-value items. Since the values are close and the distribution is random, a fair split that emphasizes the items with higher personal value seems reasonable. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:39,828][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:39,830][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:39,831][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:39,832][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:39,847][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:39,849][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:39,852][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:39,854][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:39,856][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:39,857][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:39,870][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:39,874][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:39,876][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:39,877][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:39,879][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:39,881][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:39,882][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:39,892][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:39,911][mllm.models.large_language_model_local][WARNING] - Response Given the values and the pattern so far, it seems we should focus on items that have high value for either you or Bob but not both, to maximize the points. Here, hats and books are valued the same (1) by both of us, but balls are valued differently. You value balls at 10, while Bob values them at 1. This difference suggests a strategy where we can gain an edge by taking more of the balls since they are highly valued by you and less by Bob. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:39,951][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, the optimal strategy would be to propose keeping all the items that have a higher value for you and splitting the rest proportionally. Since you value books and balls relatively high compared to hats, it makes sense to propose keeping all the books and balls, and splitting the hats proportionally. Proposal: 5 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:39,954][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:39,955][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:39,991][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the fact that both you and Alice have identical values for the other's items, the optimal strategy is to propose the same division of items to ensure that the distribution is in your favor and maximizes your points. Since both of you will likely propose the same distribution, proposing a fair split would maximize the points for both. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:39,994][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:40,071][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:40,107][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:40,110][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:40,112][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:40,129][mllm.models.large_language_model_local][WARNING] - Response Given the revised values where you have a higher value for hats and books (1 and 10 respectively) and Bob has a higher value for balls (10), it's important to propose a distribution that maximizes your points. Since hats and books have higher individual values for you, it's wise to claim as much as possible of those items. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:40,130][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:40,131][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:40,134][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:40,136][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:40,138][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:40,165][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:40,166][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:40,167][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:40,197][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:40,199][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:40,219][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the proposed strategy from both Alice and myself in the previous rounds, where we each want to maximize our points, it's clear that proposing to take all items is a logical move. Since we both have the same strategy and the items are split proportionally if the sum exceeds the available items, it's best to mirror Alice's proposal to ensure a balanced outcome and maximize our points. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:40,221][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:40,241][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's beneficial to focus on the items where you have a higher value compared to Bob's. Since you value balls the most at 10 and Bob values hats the most at 10, we should consider keeping more of the items you value higher. Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:40,242][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:40,263][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:40,283][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:40,285][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:40,287][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:40,288][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:40,303][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:40,305][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:40,307][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:40,326][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:40,329][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:40,331][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:40,366][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and the previous round's outcomes, it seems that proposing an equal share might not be the best strategy, as it leads to lower earnings. Instead, we should try to maximize our earnings by proposing a distribution that takes advantage of our higher value for books and balls while still allocating a fair share of hats. Proposal: 10 hats, 10 books, 10 balls However, to increase our earnings, we can propose slightly less of the items we value highly to ensure we receive a higher per-item value when the items are divided proportionally. Proposal: 10 hats, 9 books, 9 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:40,367][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:40,958][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both hats and balls are highly valued by both parties, while books are relatively less valued. Our strategy should focus on securing as many hats and balls as possible since these items provide the highest value to us and Alice. Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:40,961][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric and high value for hats and balls, while books have relatively low value for both of us, proposing to take all items seems to maximize the potential points. Here is the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:40,969][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:40,973][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:40,987][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:40,990][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:40,994][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:41,070][mllm.models.large_language_model_local][WARNING] - Response Given the values: - Your per-item values: hats=1, books=1, balls=10 - Bob's per-item values: hats=10, books=1, balls=1 To maximize points, you should prioritize balls since you value them significantly more than hats and books. However, you also need to consider Bob's values and avoid proposing too much to either hats or books, as he values them relatively high. A balanced proposal could be: - Give Bob a significant portion of hats and books to satisfy his high values. - Allocate as many balls as possible to yourself since you value them the most. A good proposal might be: Proposal: 3 hats, 3 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:41,074][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the previous rounds' results, a strategic approach would be to consider Bob's likely behavior. If Bob values hats and books highly, he might try to maximize his earnings by taking all hats and books. To counter this, it's beneficial to propose an allocation that ensures a fair share, while also considering the potential for proportional distribution if my proposal exceeds the total items. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:41,112][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and Bob's strategy, we should propose to take as many balls as possible since they have the highest value for both of us, and we should share the hats and books to avoid unnecessary loss. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:41,155][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it seems Bob values books and balls more than hats, while you value hats and books more than balls. In this round, you should try to maximize your points by proposing to take more items that you value higher. ProposAl: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:41,158][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:41,161][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:41,178][mllm.models.large_language_model_local][WARNING] - Response Given the current values, it's optimal to propose a distribution that accounts for both our high value for hats and balls and Bob's high value for balls. A fair but strategic proposal would be to allocate most of the items based on their values. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:41,181][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:41,184][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:41,229][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:41,231][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:41,298][mllm.models.large_language_model_local][WARNING] - Response Given the symmetrical values and the fact that both you and Alice value hats and balls highly while devaluing books, it makes sense to try to secure as many hats and balls as possible while splitting books proportionally. However, since the allocations are random, we should also consider the potential for Alice to propose a similar distribution. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:41,321][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both sets of values favor books and balls over hats. To maximize my points, I should try to claim as many books and balls as possible while still proposing a fair share of hats to avoid having my proposals deemed excessive. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:41,324][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:41,356][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:41,358][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:41,437][mllm.models.large_language_model_local][WARNING] - Response Given Alice's strategy of taking books and balls if possible, and the high value you place on hats and books, it's important to ensure you secure at least some hats and books. Since the items are split proportionally if the total proposals exceed the quantity, it might be wise to propose keeping all the hats and books and offer a fair share of balls to avoid an under-proposal scenario where we might end up with less than our fair share. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:41,520][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both Alice and Bob prefer balls over hats and books. Since we don't know the exact distribution of items, the safest and most value-maximizing strategy is to claim as many balls as possible while splitting the hats and books evenly. This way, we maximize the points from balls, which have the highest value. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:41,523][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:41,526][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:41,529][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:41,530][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:41,602][mllm.models.large_language_model_local][WARNING] - Response Given the updated per-item values, I now have a higher valuation for books (10) compared to hats (1) and balls (1). Alice, on the other hand, values hats highly (10) and books and balls equally (1). To maximize my points, I should propose to take all the books since they have the highest value for me. I will keep the minimum amount of hats and balls to ensure my proposal does not exceed the item quantity. Proposal: 1 hat, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:41,604][mllm.models.large_language_model_local][WARNING] - Response Given the values, it makes sense to try to maximize the points by taking the items with the highest individual values. Since both you and Bob have the same per-item values for hats and balls, and you both value books very highly, but you value them higher, it might be strategic to propose slightly less books than Bob to ensure you get more of the books. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:41,605][mllm.models.large_language_model_local][WARNING] - Response Given that both you and Bob have similar valuations for hats and balls, but different valuations for books, a strategic proposal would be to ensure you get the items you value highly while also considering Bob's potential proposals. Here's a proposal that maximizes your points based on the given values: Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:41,607][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:41,609][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:41,610][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:41,691][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and Bob's strategic proposal, it seems that both you and Bob value hats and books in a similar way but have differing values for balls. To maximize your points, you should consider proposing a distribution that takes advantage of your higher value for balls while still accounting for Bob's valuation of hats. Proposal: 10 hats, 10 books, 9 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:41,693][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:41,696][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:41,698][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:41,776][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:41,779][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:41,948][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:41,955][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:41,959][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:41,963][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:42,032][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:42,039][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:42,046][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:42,049][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:42,051][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:42,203][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:42,290][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:42,376][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values: - Hats: You value at 10, Alice values at 10 - Books: You value at 10, Alice values at 1 - Balls: You value at 1, Alice values at 10 In this round, both you and Alice have the highest value for hats and balls, and both value books relatively low. Since the items are split proportionally if the total proposals exceed the item quantity, the optimal strategy would be to propose the full quantity for items where you and Alice have similar high values to maximize the allocation. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:42,380][mllm.models.large_language_model_local][WARNING] - Response Given the new values, it seems that both you and Alice value the items differently. You value hats and balls highly, while Alice values books more. To maximize your points, you should propose to take more of the items you value more. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:42,384][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:42,461][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:42,468][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:42,472][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:42,548][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:42,551][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:42,554][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:42,631][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:42,637][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:43,047][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:43,051][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:43,054][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:43,058][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:43,061][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:43,098][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:43,101][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:43,142][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:43,187][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:43,191][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:43,245][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Alice have a preference for different items. You value hats and balls more, while Alice values books and balls more. However, since the total quantity of items is equal to the number proposed, let's consider a strategy that maximizes your points based on your higher values for hats and balls. Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:43,274][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:43,323][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, you have a strong preference for books and balls, while Bob values hats more. To maximize your points, you should propose to take all the items that you value highly. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:43,492][mllm.models.large_language_model_local][WARNING] - Response Given the new per-item values where both you and Alice value hats and books at 10 and balls at 1, the optimal strategy is to propose splitting the items equally to ensure both you and Alice maximize your points. Here’s the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:43,533][mllm.models.large_language_model_local][WARNING] - Response Given the values, it seems both your and Bob's valuations for hats and balls are relatively low compared to books. However, as books are highly valued by both of you, it's crucial to secure as many books as possible. This round presents a good opportunity to maximize your points by focusing on books. Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:43,560][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:43,563][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:43,566][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:43,570][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:43,600][mllm.models.large_language_model_local][WARNING] - Response Given the new values, it seems that splitting the items equally may not be the optimal strategy anymore. Since my value for balls is much higher than Bob's, and Bob's value for hats is much higher than mine, it might be more strategic to allocate the balls to myself and let Bob take the hats and books. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:43,680][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both hats and books are highly valued by both parties, but balls are least valued. To maximize your points, you should propose to take most of the items that you value highly. Here’s a strategic proposal: Proposal: 10 hats, 10 books, 1 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:43,724][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both you and Alice have high values for books and low values for hats and balls. To maximize your points, you should propose to take the majority of the books while splitting the hats and balls more evenly. Proposal: 5 hats, 10 books, 5 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:43,773][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values for this round, the optimal strategy would be to gather as many of the items you value highly (books) while leaving the items valued less by both of you (hats and balls) to Alice. Here’s the proposal: Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:43,781][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:43,784][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:43,788][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:43,817][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:43,842][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:43,845][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:43,852][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:43,872][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:43,875][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:43,878][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:43,896][mllm.models.large_language_model_local][WARNING] - Response Given the new per-item values, where both you and Bob value hats and books at 1 and balls at 10, the most beneficial approach would be to propose an equal split of the balls since they are the most valuable item according to both of your valuations. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:43,902][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:43,905][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:43,941][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:43,945][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:43,947][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:43,949][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:43,984][mllm.models.large_language_model_local][WARNING] - Response Given the valuation discrepancies, it's crucial to propose a split that maximizes your benefit. Since you value hats and books more and balls less than Alice, you should propose a split that allocates more items to yourself for items you value more. Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:43,991][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:44,005][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:44,008][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:44,032][mllm.models.large_language_model_local][WARNING] - Response Given the new values where hats are less valuable for both of us and balls are more valuable, but Bob places a higher value on hats and balls while valuing books lower, it makes sense to secure as many balls as possible since they are the most valuable item for you. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:44,035][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:44,038][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:44,041][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:44,044][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:44,047][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:44,050][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:44,078][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Alice value hats and books highly but disagree on balls. Since you value hats and books more and Alice values them less, proposing to keep all items should still be a good strategy. However, since Alice values balls more, she might propose keeping all the balls. To maximize your points, you should propose to keep more of the items you value higher. proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:44,164][mllm.models.large_language_model_local][WARNING] - Response Given the current values, it's beneficial to propose a split that maximizes your points based on your higher values for hats and books. Here's a strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:44,168][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:44,171][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:44,207][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:44,210][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:44,241][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:44,267][mllm.models.large_language_model_local][WARNING] - Response Given the new values, where you have a high value for balls and Bob has a high value for hats, it makes sense to propose a distribution that maximizes your value from the balls while ensuring you don't overclaim on items where Bob values them more highly. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:44,269][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:44,272][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:44,274][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:44,277][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:44,279][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:44,282][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:44,288][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:44,291][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:44,293][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:44,300][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:44,314][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:44,338][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:44,341][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:44,420][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:44,502][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:44,505][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:44,508][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:44,509][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:44,579][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:44,615][mllm.models.large_language_model_local][WARNING] - Response Given the updated values, where you value hats and books highly and Alice values hats and balls highly, it's important to propose a distribution that considers both of our high-value items. Here's a strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:44,618][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:44,625][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:44,636][mllm.models.large_language_model_local][WARNING] - Response Given the high value of hats and books for both of us, and the low value of balls for both, it makes sense to ensure we get as many hats and books as possible while sharing some balls. My proposal will be: proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:44,640][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:44,642][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:44,649][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:44,655][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:44,663][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:44,666][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:44,682][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:44,687][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:44,692][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:44,695][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:44,705][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:44,785][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:44,788][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:44,790][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:44,863][mllm.models.large_language_model_local][WARNING] - Response Given the new per-item values, where you have a high value for hats and books, and Bob has a high value for balls, it makes sense to propose keeping all the items to capture your higher values. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:44,942][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:44,944][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:45,023][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:45,026][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:45,088][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:45,132][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:45,139][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:45,142][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:45,144][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:45,147][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:45,158][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:45,165][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:45,172][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:45,175][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:45,191][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:45,239][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:45,241][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:45,244][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:45,285][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, the best strategy is to propose a split that maximizes the points based on our high value for hats and books, and Bob's high value for balls. Our proposal should reflect that we value hats and books more than balls, and Bob values balls the most. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:45,288][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:45,295][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:45,301][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:45,308][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:45,327][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:45,347][mllm.models.large_language_model_local][WARNING] - Response Given the current per-item values and the symmetry in the game where both Alice and Bob have similar values for different items, it makes sense to propose a split that balances the allocation and ensures the highest possible points for both participants. Since we have 10 hats, 10 books, and 10 balls, and given the specific per-item values, a balanced proposal might be: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:45,354][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:45,369][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:45,393][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:45,398][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:45,430][mllm.models.large_language_model_local][WARNING] - Response Given the current per-item values, it's important to propose a distribution that maximizes your points. Since you have a higher value for books (10) and Alice has a higher value for balls (10), it's strategic to propose keeping all books and a combination of hats and balls that balances the total quantity. Proposal: 6 hats, 10 books, 4 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:45,432][mllm.models.large_language_model_local][WARNING] - Response Given the values, it seems that splitting the items equally leads to an equal distribution of points. However, to try to optimize the situation where my value for books and balls is lower than Bob's, I could propose a slightly different split to see if I can capture more points. Here's a revised proposal: Proposal: 10 hats, 10 books, 9 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:45,435][mllm.models.large_language_model_local][WARNING] - Response Given the alignment of values and the symmetry in the game, it makes sense to propose a fair split that maximizes the total value for both parties. Here's the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:45,437][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:45,442][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:45,683][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:45,761][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:45,764][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:45,767][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:45,770][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:45,772][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:45,827][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:45,834][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:45,909][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:45,991][mllm.models.large_language_model_local][WARNING] - Response Given the values, the optimal strategy might involve taking more of the items where you have a higher value. Since you value books at 10 and balls at 1, while Bob values books at 1 and balls at 10, you should propose to take all the books and balls to maximize your points. Proposal: 10 hats, 10 books, 10 balls However, since the total proposed items exceed the available items, we need to adjust the proposal to fit the available 30 items. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:46,073][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:46,238][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that hats are valuable to both of us, and books and balls are also valuable but to different agents. To maximize my points, I should propose keeping all items to secure the value I place on them. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:46,242][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:46,248][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:46,255][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:46,281][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:46,285][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:46,287][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:46,359][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:46,362][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:46,365][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:46,441][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:46,610][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:46,635][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:46,642][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:46,669][mllm.models.large_language_model_local][WARNING] - Response Given that both you and Alice have similar valuations for hats and books, and there is high value for balls for Alice, a strategic approach might involve coordinating to maximize the total points while accounting for the potential for Alice to claim all balls if she goes first. Here's a balanced proposal: Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:46,672][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:46,752][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:46,836][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and the symmetry in the situation, the best strategy is to propose a division that reflects the true value each of us places on the items. Since both of us value hats and books equally and balls relatively less, it's beneficial to提议分配这些物品以最大化自己的得分。 Proposal: 10 hats, 10 books, 10 balls This proposal ensures that we both get an equal share of items that we value highly (hats and books), and the balls, which are less valued by both of us, are divided equally. This approach should help maintain a fair and potentially mutually beneficial split. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:46,844][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:46,923][mllm.models.large_language_model_local][WARNING] - Response Given the values where you have a high value for hats and balls but a low value for books, and Bob has a high value for books and balls but a low value for hats, it's beneficial to propose a split that maximizes the items you value highly. Here's a strategic proposal: Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:46,930][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:46,933][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:46,936][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:46,938][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:47,009][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the values, it makes sense to mirror Alice's proposal to ensure a fair split and maximize our points. Here is the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:47,012][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:47,015][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:47,018][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:47,020][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:47,024][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:47,175][mllm.models.large_language_model_local][WARNING] - Response Given the high value of hats and books for both you and Alice, and the low value of balls, a cooperative strategy might be beneficial. However, since you both have similar values for hats and books, and both have high values for balls, proposing to split the items evenly might be a reasonable approach. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:47,300][mllm.models.large_language_model_local][WARNING] - Response Given the values, the optimal strategy would be to take the items that you value the most, while acknowledging that Bob values his items similarly. Since you value balls the most (10), followed by hats (1), and books the least (1), and Bob values books the most (10), followed by balls (1), and hats the least (1), proposing to take most of the balls and some hats and books seems strategic. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:47,322][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric and mixed value distributions, it seems optimal to propose splitting all items equally to maximize the points based on the values provided. Here's the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:47,403][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:47,406][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:47,442][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:47,496][mllm.models.large_language_model_local][WARNING] - Response Given the new per-item values, where both you and Bob value books at 10, it makes sense to compete more aggressively for the books. Here’s a strategic proposal that aims to maximize your points: ProposAl: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:47,504][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:47,507][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:47,510][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:47,517][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:47,523][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:47,527][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:47,530][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:47,560][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:47,606][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:47,946][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:47,949][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:47,952][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:48,030][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:48,034][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:48,037][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:48,039][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:48,042][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:48,045][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:48,194][mllm.models.large_language_model_local][WARNING] - Response Given Alice's valuation of books and balls, and your own high valuation of hats and balls, it's important to consider that Alice might try to take all the balls and books. However, splitting the hats evenly seems like a good strategy to ensure you get a significant portion of the high-value items. Here's a balanced proposal that aims to secure a good amount of hats and balls while also allocating some books: Proposal: 6 hats, 5 books, 9 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:48,273][mllm.models.large_language_model_local][WARNING] - Response Given Alice's valuation for books and balls, and your own valuations, it is beneficial to allocate more items that you value highly. Here's the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:48,280][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:48,401][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:48,434][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, the best strategy would be to maximize the items with higher personal value while also considering Bob's values. Since you value books and balls more and Bob values books and hats more, it makes sense to propose a split that aligns with these preferences. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:48,441][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:48,447][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:48,450][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:48,462][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:48,469][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:48,492][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:48,494][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:48,516][mllm.models.large_language_model_local][WARNING] - Response Given the values, it seems the optimal strategy might involve recognizing that Alice values books significantly more than I do, and balls slightly less. I should propose keeping all the hats and books to maximize my points since they are valuable to me, and I shouldn't take balls unless they are necessary to avoid excess claims. proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:48,523][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:48,525][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:48,529][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:48,532][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:48,545][mllm.models.large_language_model_local][WARNING] - Response Given the values, both you and Alice have a high value for hats and a lower but equal value for books and balls. To maximize points, we should ensure that we get the items we value the most. Here, we both value hats and balls highly, and we should try to claim them fully while considering proportional allocation if we exceed the item quantity. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:48,549][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:48,551][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:48,677][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:48,709][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:48,830][mllm.models.large_language_model_local][WARNING] - Response Given the asymmetric values where both you and Bob value hats and books equally but differ on balls, a strategic approach would be to recognize the importance of getting balls. However, splitting them equally might not be the best strategy if we can ensure a better allocation by sending a mixed signal or slightly jeopordizing one category to secure others. Proposal: 10 hats, 10 books, 5 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:48,833][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:48,837][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:48,918][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:48,921][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:48,928][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:48,931][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:48,934][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:48,937][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:48,943][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:48,950][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:48,956][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:48,963][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:48,971][mllm.models.large_language_model_local][WARNING] - Response Given the values, the optimal strategy would be to propose an allocation that maximizes the total value for both you and Alice, while ensuring that the total items proposed do not exceed the available items. Here, both you and Alice value books highly, so we should allocate as many books as possible. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:48,975][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:48,977][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:48,980][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:48,983][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:48,985][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:48,988][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:48,996][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:49,061][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:49,064][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:49,102][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:49,188][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both hats and books are highly valued by both parties, while balls are less valuable. To maximize points, we should propose to take as much of the items that we value highly. Here's the optimal proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:49,192][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:49,274][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric nature of the per-item values and the fact that both you and Alice value hats and books highly while valuing balls less, a cooperative strategy could be beneficial. However, to ensure fairness and maximize points, we should propose an equal split for the items we value similarly, while ensuring the total does not exceed the available quantity. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:49,278][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:49,359][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:49,362][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:49,415][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:49,422][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:49,425][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:49,442][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:49,445][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:49,452][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:49,466][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:49,469][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:49,505][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:49,508][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:49,554][mllm.models.large_language_model_local][WARNING] - Response Given the values, it appears that both you and Bob have complementary preferences. You value hats and balls highly, while Bob values books and hats. To maximize points, you might want to consider a cooperative strategy that takes into account the relative values each of you places on the items. Proposal: 10 hats, 10 books, 10 balls This proposal keeps the symmetry and fairness in the distribution, which seems to have worked well in previous rounds where both of you earned the same points. However, to explore a cooperative strategy that might yield more points, you could consider splitting the items in a way that maximizes the total utility for both players. Proposal: 10 hats, 10 books, 10 balls This proposal maintains an equitable distribution but may not fully exploit the cooperative potential. Let's try to split the books to better align with Bob's preferences, while still accounting for your own preferences: Proposal: 10 hats, 10 books, 10 balls Given the values, a more cooperative approach might look like this: Proposal: 10 hats, 10 books, 10 balls This keeps the distribution fair while taking into account the complementary nature of your and Bob's preferences. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:49,558][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Alice place high value on books and low value on hats and balls. Since proposing the full quantity of all items would likely lead to an equal split if Alice does the same, I will propose a strategy that takes into account the significant value both you and Alice place on books, while also trying to secure some value from hats and balls. Proposal: 5 hats, 10 books, 5 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:49,560][mllm.models.large_language_model_local][WARNING] - Response Given the values, you should propose to take as much as you can of the items you value highly to maximize your points. Since hats and balls are valued at 10, and books at 1, you should take all of the hats and balls, and if possible, a portion of the books to ensure maximum points. Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:49,563][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:49,566][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:49,578][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:49,582][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:49,601][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:49,625][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that hats are highly valued by both of us, while balls are the least valued. Books are somewhat valued by both but not as highly as hats. To maximize my points, I should propose keeping all the hats and balls, and分配一些书给Alice，以确保数量不超过总数。考虑到这样的分配，我决定分配5本书给Alice，其余的自己保留。 Proposal: 10 hats, 10 books - 5 for Alice, 5 for me, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:49,628][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:49,634][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:49,637][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:49,663][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:49,670][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:49,677][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:49,705][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:49,710][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:49,724][mllm.models.large_language_model_local][WARNING] - Response Given the distribution of values, it seems that proposing to take all items is leading to equal outcomes where both you and Alice receive the same points. To maximize your points, you should consider a more strategic approach to outmaneuver Alice. Since Alice values balls highly and you value them low, you might want to propose a split that takes advantage of this imbalance. Proposal: 10 hats, 10 books, 1 ball did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:49,726][mllm.models.large_language_model_local][WARNING] - Response Given the values, it seems advantageous to focus on the items where we have a higher value compared to Bob. Since I value books and balls more than hats, and Bob values hats more than books and balls, it might be a good strategy to propose a higher allocation for books and balls. Here is a revised proposal: Proposal: 10 hats, 10 books, 9 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:49,729][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:49,731][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:49,733][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:49,735][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:49,737][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:49,739][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:49,741][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:49,759][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:49,762][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:49,839][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Alice place the highest value on balls. However, you have a higher value for hats and Alice for books. Proposing the allocation of all items might still be the best strategy to ensure you get at least a share of the items you value highly, while Alice gets what she values the most. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:50,077][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:50,080][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:50,083][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:50,086][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:50,113][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:50,120][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:50,139][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:50,145][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:50,162][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:50,169][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:50,192][mllm.models.large_language_model_local][WARNING] - Response Given that both you and Alice value the items differently, a more strategic approach might be necessary to maximize your points. Since you value books and balls more, and Alice values hats more, you should propose a distribution that takes advantage of Alice's valuation of hats while ensuring you get a good share of the high-value items. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:50,195][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:50,197][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:50,209][mllm.models.large_language_model_local][WARNING] - Response Given the current per-item values and the symmetry in the situation, it's clear that both you and Bob value the same items differently. You value hats and books highly and balls less, while Bob values balls highly and hats and books less. Proposing to take all items would lead to proportional distribution if Bob also proposes to take all items. To maximize your points, you should try to anticipate Bob's strategy. A strategic approach is to offer a split that benefits you based on your higher valuation. Given that you and Bob likely have similar valuation strategies, a balanced proposal might encourage Bob to also propose a balanced split. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:50,212][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:50,214][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:50,371][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:50,378][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:50,384][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:50,454][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the values assigned, it seems strategic to propose an equal split that maximizes the points based on the per-item values. Here's the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:50,457][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:50,535][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric and complementary values between you and Alice, proposing to take all items seems optimal to maximize your points. Here’s the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:50,538][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:50,542][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:50,618][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:50,786][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:50,790][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:50,946][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and the symmetry in the game, it's clear that the optimal strategy is to propose the whole items if the other player is also proposing the whole items, as this maximizes the points based on the individual values. Here, since both items you and Bob value are concentrated differently, the equal split strategy might not be the best. Considering the values: - You value hats and books at 1, and balls at 10. - Bob values hats at 10, books at 1, and balls at 1. If Bob proposes to take all the hats and balls, and you take all the books, this would be a reasonable counter-proposal to maximize your points. Proposal: 10 books, 0 hats, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:50,953][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:50,956][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:50,959][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:50,965][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:50,972][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:50,978][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:50,985][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:50,991][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:50,997][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:51,000][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:51,003][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:51,019][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:51,021][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:51,041][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Alice have complementary interests. You value hats and balls highly, while Alice values books highly. To maximize points, you should propose to take the items you value more and let Alice take the items she values more. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:51,044][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:51,124][mllm.models.large_language_model_local][WARNING] - Response Given Alice's valuation of books and balls, it might be strategic to exploit the disparity in valuations to maximize points. Here's a proposal that accounts for these differences: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:51,129][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:51,132][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:51,134][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:51,210][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Bob place high value on different items. To maximize points, we need to consider the proportional allocation in case the total proposed amounts exceed the available items. Here's a strategic approach: 1. You have a high value for books (10) and balls (10). 2. Bob has a high value for hats (10) and books (10). To avoid splitting items and to maximize the points where possible, we can propose to take majority of the items we value highly, knowing that Bob will likely do the same. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:51,297][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Bob value books and balls highly, but you both value hats relatively low. To maximize your points, you should ensure you get as many books and balls as possible while also securing some hats. Here's a proposal that balances this: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:51,339][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:51,433][mllm.models.large_language_model_local][WARNING] - Response Given the values, proposing an equal distribution of all items seems reasonable to maximize the points considering both our high value for books and Alice's high value for balls. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:51,649][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:51,696][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:51,699][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:51,702][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:51,705][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:51,733][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric nature of the per-item values and the fact that both you and Bob value the same items differently, a balanced proposal that accounts for both of your valuations might be the best strategy. Here's a proposal that aims to maximize the total points for both of us: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:51,818][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:52,074][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:52,077][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:52,079][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:52,082][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:52,085][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:52,088][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:52,095][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:52,123][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the situation and the fact that both you and Bob have the same potential per-item values but in reverse, it makes sense to collaborate to maximize points. However, since both agents propose the same amount and receive proportionally, we can focus on ensuring that we get a fair distribution. Here's the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:52,157][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:52,196][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:52,200][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:52,222][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:52,225][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:52,245][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:52,247][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:52,250][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:52,253][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:52,268][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:52,274][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:52,277][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:52,292][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:52,314][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the situation and the values, splitting the items equally would likely result in a fair and optimal distribution. Here’s the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:52,317][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:52,320][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:52,323][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:52,326][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:52,329][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:52,338][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Alice place high value on different items. You value books and balls highly, while Alice values hats and books highly. To maximize your points, you should aim to get as many of the items you value highly as possible. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:52,362][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:52,366][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:52,369][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:52,383][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:52,386][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:52,447][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both you and Bob have a strong valuation for different items. You value hats and books highly (10), while Bob values balls highly (10). Books have a much lower valuation for both of you (1). To maximize your points, it's important to secure as many of the items you highly value as possible. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:52,504][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:52,507][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:52,510][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:52,589][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:52,592][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:52,595][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:52,598][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:52,600][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:52,676][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:52,679][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:52,738][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:52,741][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:52,748][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:52,754][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:52,757][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:52,763][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:52,779][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:52,782][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:52,785][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:52,859][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric nature of the per-item values and the fact that both you and Bob value the items differently, a strategic approach would be to propose a split that maximizes the utility of the items that you value higher. Since both you and Bob value books moderately and balls less, but you value hats and books more than Bob values hats, it makes sense to propose a balanced yet slightly advantageous split. Proposal: 5 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:52,862][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and understanding that both agents value the items differently, a competitive yet cooperative approach is necessary to maximize points. Since both hats and books have high values for one of the players, it's important to secure as many books and hats as possible while still leaving enough for Bob to ensure he values the items he receives. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:52,865][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:52,939][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Alice value balls the most at 10, and both of you value hats the least at 1. Books are valued moderately at 1 by you and valued slightly more at 1 by Alice. To maximize your points, it would be strategic to ensure you get a significant portion of the balls, while also trying to secure some hats and books. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:52,942][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:52,945][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:53,022][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:53,026][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:53,103][mllm.models.large_language_model_local][WARNING] - Response Given the values, both you and Alice place high value on hats and books, and both of you value balls less. Since the values are symmetric but you have slightly higher values for hats and books, it’s beneficial to propose a split that takes this into account while ensuring the total proposed amount does not exceed the available items. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:53,109][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:53,112][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:53,114][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:53,116][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:53,184][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric and competitive nature of the per-item values, it's strategic to match Bob's proposal to ensure fair distribution and maximize points. Here, both of us value hats and books highly, while balls are valued lower by both. A cooperative but competitive proposal would be to split the items evenly to avoid excess proposals. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:53,185][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's beneficial to propose a distribution that maximizes your value. You value hats and balls highly but Alice values books and balls more. To balance this, propose a split that takes advantage of your high value for hats and balls while considering Alice's preferences for books. proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:53,187][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:53,276][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:53,312][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the strategic nature of the game, let's consider proposing a split that maximizes the potential points while also considering Alice's valuation. Since both hats and books have high values for you and low values for Alice, and balls are valued the same for both, a balanced approach might be optimal. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:53,316][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both hats and balls are less valuable to both of us compared to books. The optimal strategy would be to propose keeping all the items to ensure we get as much value as possible from the books. Here is my proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:53,319][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:53,322][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:53,323][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:53,333][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:53,334][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:53,356][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:53,357][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:53,358][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:53,379][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:53,403][mllm.models.large_language_model_local][WARNING] - Response Given that both you and Alice value hats and books highly, and both of you are likely to have similar proposals, a strategic approach would be to recognize that proposing to take all items would result in a proportional split if the totals exceed the available items. Since both of your per-item values for hats and books are high, and Alice also values hats and books, it might be beneficial to propose an allocation that reflects this high value while considering the proportional distribution. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:53,405][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it is important to consider the significant disparity in values between hats and books compared to balls. Since books and hats are valued much higher by both of us, it makes sense to allocate as many of these items as possible, while still considering the lower value of balls. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:53,424][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:53,445][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:53,465][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:53,467][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:53,468][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:53,469][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:53,504][mllm.models.large_language_model_local][WARNING] - Response Given the value discrepancy between us and Bob, an optimal strategy would be to propose keeping all items to maximize our points, knowing that any excess proposal will be allocated proportionally but starting with our full claim. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:53,666][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric and evenly valued nature of the items and their respective per-item values, the optimal strategy is to propose a fair division that maximizes the total points for both players. Here, proposing an equal share ensures that both players receive a proportional value based on their per-item values. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:53,950][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:53,953][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:53,956][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:53,959][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:53,965][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:53,969][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:53,971][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:53,974][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:53,978][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:53,982][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:53,985][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:53,986][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:54,019][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:54,080][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:54,087][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:54,093][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:54,099][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:54,104][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:54,114][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both hats and balls have higher values for you compared to Bob, while books have higher value for Bob. To maximize points, it's strategic to propose keeping all items to ensure you receive all of them, as the allocation would be proportional and you would receive the full amount based on your higher values for hats and balls. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:54,117][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and the symmetry in the problem, a reasonable strategy is to propose an allocation that approximates what the other player might propose. Since both we and Bob value hats minimally and books and balls significantly, and given the symmetry in values, a fair split would be to propose an equal share of each item. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:54,121][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:54,126][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:54,138][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:54,141][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:54,145][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:54,219][mllm.models.large_language_model_local][WARNING] - Response Given the high value of balls for both of us and the low value of hats for both of us, it might be beneficial to propose a split that maximizes our combined points while ensuring we get a significant portion of the highly valued balls. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:54,223][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:54,260][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:54,263][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:54,315][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:54,341][mllm.models.large_language_model_local][WARNING] - Response Given the pattern in the per-item values and the previous outcomes, it seems strategic to propose an allocation that maximizes the points based on the values. Since I value books and balls highly but Alice values hats more and books equally, proposing a split that takes into account the relative values might be effective. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:54,344][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both hats and books are less valued by both of us, but balls are highly valued by both. We should try to maximize our share of the balls while also considering the distribution of hats and books. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:54,348][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:54,351][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:54,355][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:54,358][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:54,360][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:54,363][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:54,402][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that the items with the highest value to you are the balls (10 points), followed by hats (1 point) and books (1 point). To maximize your points, it's best to propose the distribution that weighs more towards the items you value the most. Here is a proposed strategy: - Allocate as many balls as possible to yourself since they are worth the most to you. - Share the hats and books more evenly to avoid Bob taking all of them, as they are worth more to him. Thus, a reasonable proposal would be: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:54,571][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Alice place significant value on hats and books, and less value on balls. A strategy that considers the high value of hats and books and the low value of balls can maximize points. Since the per-item values are symmetrical in terms of high value on hats and books, it would be strategic to propose a division that captures as much value as possible from these items while still proposing a fair share for balls. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:54,575][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:54,654][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the previous outcomes, it seems that both you and Bob are proposing to take all items equally. However, to maximize your points, it might be strategic to try to slightly outbid Bob in items where your value is significantly higher. Proposal: 10 hats, 10 books, 9 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:54,658][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:54,662][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:54,665][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:54,737][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:54,739][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:54,820][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:54,822][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:54,824][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:54,825][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:54,903][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:54,904][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:54,906][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:55,403][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:55,483][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric nature of the values and the items, it seems optimal to propose an equal split to avoid the situation where proposals might be outAllocated proportionally. Here is the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:55,491][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:55,587][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:55,598][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:55,867][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:55,974][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:56,783][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:57,894][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's important to recognize that we both have similar but not identical preferences. Since we both want more hats and balls, we need to split the books differently to ensure we each get our fair share of the items we value. A strategic split would be to give Alice most of the books since she values them highly at 10, while we keep most of the hats and balls. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:58,152][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and the interactions in previous rounds, a strategic approach is to consider the discrepancy in values between you and Alice. Since you value books and balls much higher than Alice, and she values hats much more than you do, it might be beneficial to propose an allocation that takes into account these differences. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:58,161][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:58,162][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:58,164][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:58,165][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:58,177][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:58,178][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:58,180][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:58,181][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:58,181][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:58,182][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:58,183][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:58,183][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:58,185][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:58,185][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:58,186][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:58,187][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:58,189][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:58,190][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:58,191][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:58,226][mllm.models.large_language_model_local][WARNING] - Response Given the values, you should propose a distribution that maximizes your points by taking into account the higher value you place on hats and balls compared to Alice. Here is a strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:58,308][mllm.models.large_language_model_local][WARNING] - Response Given the high value of hats and books for both you and Alice, and the low value of balls for both, it's beneficial to ensure we claim as many high-value items as possible. Here's a strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:58,477][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:58,561][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:58,584][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:58,585][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:58,602][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values where both you and Alice have high values for hats and books respectively, and low values for balls, a strategic proposal would be to maximize the items with your higher value. Here is the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:58,603][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:58,605][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:58,658][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:58,675][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:58,677][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:58,694][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:58,713][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:58,715][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:58,764][mllm.models.large_language_model_local][WARNING] - Response Given the values, it is clear that both you and Alice have high valuations for different items. You value books and balls highly (10), while Alice values hats and books highly. To maximize your points, you should propose an allocation that maximizes your value for the items you highly value. Proposal: 10 hats, 10 books, 10 balls However, this proposal might lead to a situation where the total proposed amount exceeds the available items, or where Alice might propose a similar split, leading to a proportional distribution. Given the high values for books and balls, you should propose to take as much of these items as possible. Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:58,782][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and equal expected values, a strategic approach would be to propose an allocation that maximizes the benefit from items where you have a higher value, while ensuring the total quantity is not exceeded. Here, you value hats (10) and balls (10) more than books (1). A reasonable proposal could be to take all of the items you value highly and share the less valued items. Given the values, you could propose: Proposal: 10 hats, 10 books, 10 balls However, to be more strategic and avoid proposing the same thing as Alice, you might want to slightly adjust the proposal. For instance: Proposal: 10 hats, 10 books, 9 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:58,784][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:58,786][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:58,787][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:58,789][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:58,790][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:58,867][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Alice value hats and books more than balls, but you both place a high value on different types of books. To maximize your points, you should try to secure as many high-value items (hats and books) as possible. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:58,914][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric values and the equal proposals from both Alice and myself in the previous rounds, it seems that proposing to take all items will result in a proportional split if Alice also proposes to take all items. To maximize points, I will continue this strategy. proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:58,917][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:58,920][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:58,961][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both you and Bob place high value on books and balls, while hats are less valuable. A cooperative and straightforward approach might yield the best results. Here's a proposal that splits the items evenly while taking into account the higher values of books and balls: Proposal: 10 hats, 10 books, 10 balls This ensures that both you and Bob get all items, which could lead to a fair distribution and maximize points based on the values provided. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:58,963][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:59,002][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:59,024][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:59,027][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:59,043][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:59,045][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:59,048][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:59,082][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:59,103][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:59,106][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:59,119][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:59,139][mllm.models.large_language_model_local][WARNING] - Response Given the current values, it's clear that both hats and balls are highly valued by both players, while books are valued less by both but more by Bob. To maximize my points, I should try to secure as many hats and balls as possible since they offer the highest value to me. Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:59,146][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:59,177][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's important to maximize the points by considering the high value of balls and the low value of hats and books. Here's a strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:59,198][mllm.models.large_language_model_local][WARNING] - Response Given the values, you should focus on items where your value is higher. Here, books and balls are more valuable to you than hats. Since both you and Bob value hats similarly, it might be beneficial to compete for hats in this round. A balanced approach could be to split the hats fairly, focusing more on books and balls where you have higher personal value: Proposal: 10 hats, 10 books, 10 balls However, if you aim to maximize your points by focusing on the items with higher value to you, a more strategic proposal would be to slightly favor the items you value more: Proposal: 5 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:59,219][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both hats and balls are significantly more valuable to you than to Alice, while books are valued similarly. However, since Alice values books highly, it might be strategic to offer a small amount of books to her and keep the other items to maximize your points. A balanced approach would be: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:59,222][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:59,224][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:59,226][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:59,230][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:59,235][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:59,248][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:59,250][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:59,294][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that hats and books are highly valued by both of us, while balls are valued much less by both of us. However, since we have the same values for hats and books, and we have to consider the proportional distribution if our proposals exceed the total items, we should aim to secure as many of these high-value items as possible. Here is a proposal that aims to maximize the value: proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:59,296][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:59,316][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:59,319][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:59,321][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:59,339][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:59,343][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:59,345][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:59,361][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:59,366][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:59,381][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both you and Bob value hats and books fairly highly, while balls have lower individual values but could add significant points when combined. Since both of you value hats and books highly, proposing to keep as many hats and books as possible would be a good strategy. For balls, it might be better to coordinate to avoid overvaluing them, as their individual points are low. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:59,418][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, the best strategy is to maximize the items with higher personal value while ensuring the total proposed items do not exceed the available items. Here, books have the highest value for both of us, followed by hats, and then balls. Proposal: 10 hats, 10 books, 10 balls This proposal allocates all items as evenly as possible, maximizing the utility based on our individual values. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:59,436][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Alice place significant value on balls, but you value hats and books less than Alice. Proposing to take all items would likely result in a proportional split if both propose the same, but it might not be the best strategy as it could lead to a lower overall payoff if Alice anticipates this and counters effectively. A balanced approach would be to take a reasonable share of the items that have significant value for both of you, while also securing some of the items that you value more. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:59,440][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:59,446][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:59,455][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:59,461][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:59,467][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:59,500][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric nature of the problem and the equal value of the items for both Alice and Bob, proposing an equal share is a reasonable strategy. This ensures that both Alice and Bob maximize their points based on their per-item values. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:59,519][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:59,526][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:59,538][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:59,559][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:59,639][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:59,673][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:59,674][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:59,759][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:59,845][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:59,848][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:59,852][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:59,855][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:59,927][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:07:59,931][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:07:59,938][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:07:59,941][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:00,009][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, the optimal strategy would be to propose a split that maximizes the points based on the value each of us places on the items. Since I value hats and balls at 10 each and books at 1, and Alice values hats at 1 and books and balls at 10 each, it makes sense to propose a split that takes advantage of these value discrepancies. Proposal: 10 hats, 10 books, 10 balls This proposal ensures that I get all the hats and balls, which I value highly, while Alice gets all the books. However, let's consider a more refined proposal that takes the proportional allocation rule into account in case the total proposals exceed the item quantities. Proposal: 10 hats, 10 books, 10 balls Even though this maximizes my points, it's worth re-evaluating to see if a more strategic split could yield slightly more points. Given the values, a balanced approach might yield better results. Proposal: 10 hats, 5 books, 10 balls This split allows me to take all the hats and balls while still proposing a fair share of books to Alice. Let's calculate the expected points: - Hats: 10 hats * 10 (my value) = 100 points - Books: 5 books * 1 (my value) = 5 points - Balls: 10 balls * 10 (my value) = 100 points Total points = 100 + 5 + 100 = 205 points This should be a good strategy to maximize my points. Proposal: 10 hats, 5 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:00,018][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:00,171][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:00,257][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:00,430][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values for this round, it's clear that both you and Alice value balls the least. To maximize your points, you should propose to take all of the balls since your value for balls is higher than Alice's. Here's a proposal that considers your and Alice's values for each item: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:00,601][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that the balls are the most valuable for both of us, followed by books, and hats are the least valuable. To maximize our points, we should propose to take the majority of the balls, followed by books, and a minimal amount of hats, while also considering Alice's values. Proposal: 1 hat, 1 book, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:00,686][mllm.models.large_language_model_local][WARNING] - Response Given the high value of hats and balls for you and the high value of books for Alice, while also considering the symmetry in the distribution and the potential for proportional allocation, a strategic proposal would be to mirror Alice's values to ensure a fair distribution that maximizes both our and Alice's points. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:00,694][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:00,697][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:00,728][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:00,810][mllm.models.large_language_model_local][WARNING] - Response Given the high value you and Bob place on different items, it's important to consider the potential for proportional distribution if our combined proposals exceed the total number of items. Given your per-item values and Bob's, you might want to propose a split that captures as much value as possible while considering the proportional distribution rule. Considering the values: - You value hats (1), books (10), and balls (10). - Bob values hats (10), books (1), and balls (10). A balanced yet aggressive proposal might be to take the majority of the items you value highly. Here’s a proposal: Proposal: 10 hats, 10 books, 10 balls However, to be more strategic, let's consider a split that captures the highest value while ensuring we don't exceed the total items if Bob also proposes a similar amount: Proposal: 10 hats, 10 balls, 10 books - this ensures we take the majority of items we value highly, and the combined proposal is exactly 30 items, which is the total available. Thus, my final proposal is: Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:00,974][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:00,978][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:00,982][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:00,985][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:00,987][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:01,114][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Bob place significant value on different items. You value hats and balls highly (10), while Bob values hats and books highly (10). Books have no value to you and no value to Bob, so we can assume they will be split evenly. To optimize your points, you should propose taking all the balls and hats, and let Bob take all the books since he values them the most. Here is the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:01,197][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the high value of hats and books for both parties, proposing the maximum number of items seems to be a reasonable strategy to maximize points. Here's my proposal: proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:02,082][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:02,122][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:02,126][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:02,128][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:02,217][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:02,220][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:02,268][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:02,289][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:02,292][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:02,294][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:02,444][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's advantageous to allocate the items in a way that maximizes your score. Since your values for books and balls are relatively high, and Alice values hats more, you should propose to take most of the books and balls. Here's a strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:02,483][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:02,499][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:02,503][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:02,543][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's beneficial to propose a split that maximizes the value based on both your and Bob's valuations. Since you highly value books and balls, and Bob values hats more, proposing to take most of the books and balls while ensuring you get at least some hats could be a good strategy. Proposal: 5 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:02,547][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:02,604][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:02,607][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:02,611][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:02,614][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:02,622][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:02,625][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:02,643][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in this round where both you and Alice value hats and books highly but balls less so, a cooperative strategy might be effective. Since both of you value hats and books more, you can propose a division that maximizes the points for both of you. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:02,650][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:02,654][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:02,657][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:02,660][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:02,662][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:02,665][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:02,667][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:02,671][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:02,673][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:02,710][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:02,712][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:02,713][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:02,762][mllm.models.large_language_model_local][WARNING] - Response Given the current values and Alice's strategic behavior, it seems that splitting the items evenly might not be optimal. Since Alice values hats and books less than balls, and you value hats and books more than balls, it would be beneficial to offer slightly more of the items you value highly to maximize your earning potential, while still ensuring you get a significant portion. Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:02,770][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and the symmetric nature of the problem, it's clear that both you and Alice have complementary valuations for the items. You value hats and books highly, while Alice values balls highly. A cooperative strategy would likely yield the best outcome. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:02,773][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:02,780][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:02,800][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:02,803][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:02,807][mllm.models.large_language_model_local][WARNING] - Response Proposal: 5 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:02,837][mllm.models.large_language_model_local][WARNING] - Response Given that both you and Bob have similar valuation for hats and books, and significantly different valuations for balls, it's important to consider the proportional shares. Here's a strategic proposal: - You value hats and books the same, so you might allocate them proportionally. - Balls should be allocated to you since you value them more. A balanced approach would be to allocate 10 hats, 10 books, and 10 balls to you and 0 hats, 10 books, and 10 balls to Bob. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:02,841][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the previous outcomes, it seems that proposing to take all items is a reasonable strategy as long as Alice is also proposing to take all items. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:02,844][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:02,853][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:02,872][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:02,889][mllm.models.large_language_model_local][WARNING] - Response Given the disparity in values, proposing to take all items might not be the最优策略。考虑到我方对书籍的价值远高于其他物品，而对方对帽子的价值远高于其他物品，我们应该尽量争取得到我们更看重的物品。因此，合理的策略是最大限度地获取书籍，同时保留一些帽子和球，以确保总价值最大化。 Proposal: 5 hats, 10 books, 5 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:02,910][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:02,916][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:02,969][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:02,972][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:02,976][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:02,990][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric nature of the values and the previous outcomes, it seems that proposing to take all items proportionally to their values is likely to lead to a fair and beneficial outcome for both players. Here is the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:02,993][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:02,996][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:03,002][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:03,010][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric and high value of books and balls, while hats have a lower individual value, it is beneficial to secure as many high-value items as possible. A strategic approach is to maximize the number of books and balls while respecting the total quantity of each item. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:03,014][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:03,029][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:03,035][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:03,040][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:03,043][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:03,045][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:03,048][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:03,051][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:03,094][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both you and Alice place high value on balls (10), while hats and books are valued much lower (1). To maximize your points, you should try to secure as many of the balls as possible since they offer the highest individual value to you. Here’s a strategy: propose to take all the balls, and share the hats and books proportionally. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:03,096][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:03,102][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:03,108][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:03,162][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:03,165][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:03,204][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:03,208][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:03,211][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:03,245][mllm.models.large_language_model_local][WARNING] - Response Given Alice's valuation of hats as 1, books as 10, and balls as 10, and your own valuations of hats as 10, books as 1, and balls as 10, it makes sense to propose a distribution that maximizes the value based on your higher valuations. Here's a strategic proposal: proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:03,253][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:03,256][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:03,265][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:03,284][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:03,318][mllm.models.large_language_model_local][WARNING] - Response Given the high value of books and balls for both of us, and the strategic symmetry in valuations, a cooperative strategy could lead to higher collective and individual earnings. I propose we each take an equal share of each item to maximize the total points. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:03,323][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:03,352][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:03,358][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:03,364][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:03,372][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:03,387][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:03,408][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:03,412][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:03,417][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:03,421][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:03,454][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:03,457][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:03,543][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:03,785][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:03,789][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:03,802][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:03,809][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:03,864][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:03,871][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:03,878][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:04,010][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:04,123][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's beneficial to propose a distribution that maximizes your points. Since you value balls the most, followed by hats and then books, a strategic proposal would be to take all the items to maximize your points. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:04,211][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:04,535][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the high value of hats and books for both Alice and myself, splitting the items equally seems to be the best strategy to maximize points. Therefore, I will propose: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:04,539][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:04,568][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Bob value books and balls significantly more than hats. Given the symmetry and the random nature of the assignments, a cooperative proposal where you both allocate the items roughly according to their values seems the best strategy. Here’s a proposal that takes this into account: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:04,624][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:04,638][mllm.models.large_language_model_local][WARNING] - Response Given that both you and Bob value the items similarly and the optimal strategy seems to be a proportional split, I will submit a proposal that reflects this. Here’s my proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:04,668][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:04,671][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:04,683][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:04,698][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Alice place high value on hats and books, while balls have a much lower value for both of you. Since the values for hats and books are quite close for both you and Alice, a cooperative approach might yield better results. Here's a strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:04,701][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:04,704][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:04,714][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:04,730][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:04,734][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:04,759][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:04,762][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:04,790][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:04,804][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:04,808][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:04,832][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:04,839][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:04,849][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Bob place a high value on different items. You value books and balls highly, while Bob values hats more and books least. Since splitting all items 10-10-10 has resulted in lower points for both in previous rounds where there was an imbalance in item values, a more strategic split might be better. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:04,863][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and equal per-item values between us, it is optimal to propose a fair split to ensure both Alice and Bob maximize their points. Therefore, I will propose: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:04,867][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:04,893][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:04,908][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Alice have a higher valuation for balls compared to the other items. To maximize your points, you should try to secure as many of the balls as possible while also ensuring you get a fair share of hats and books. Here's a balanced proposal that attempts to maximize your points: Proposal: 5 hats, 5 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:04,935][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:04,943][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:04,957][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:04,971][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:04,974][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:04,984][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:04,999][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:05,003][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:05,026][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:05,030][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:05,054][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:05,068][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:05,071][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:05,098][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:05,112][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:05,124][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:05,129][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:05,150][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:05,168][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:05,196][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:05,210][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and the symmetry in Bob's and your values, proposing an even distribution is likely to yield a fair and optimal outcome. Here's the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:05,214][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:05,227][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:05,234][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:05,243][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:05,267][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:05,271][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:05,280][mllm.models.large_language_model_local][WARNING] - Response Given the structure of the game and the values of the items, it seems reasonable to propose an allocation that maximizes the benefit based on the per-item values. Here, books and balls are valued significantly higher by you than hats, while hats are valued higher by Bob. Therefore, it is beneficial to keep as many books and balls as possible while also considering hats to balance the proposal. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:05,296][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:05,299][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:05,327][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:05,333][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:05,343][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:05,389][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:05,652][mllm.models.large_language_model_local][WARNING] - Response Given that both our per-item values for books and balls are significantly higher than those of Bob, and our values for hats are similar, proposing to keep the maximum of each item seems to be a strategic approach to maximize points. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:05,769][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:05,773][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:05,802][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:05,805][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 books, 10 hats, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:05,816][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:05,830][mllm.models.large_language_model_local][WARNING] - Response Given the high value of books for both of us and the low value for hats and balls, it's beneficial to ensure that we each propose to take all of the books. However, since the total proposed quantity cannot exceed the available quantity, we need to split the hats and balls between us. Since hats and balls have very low values, proposing to take all of them would maximize our points. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:05,834][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, the best strategy is to maximize the items with higher personal value. Since both you and Bob value hats and books highly and balls less, splitting the items equally might not be the optimal approach. Here, the optimal strategy would be to claim as many hats and books as possible, letting Bob take the balls if necessary. Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:05,857][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's beneficial to claim the items where your value is significantly higher than Alice's, and split the remaining items fairly. Here, hats and books have higher values for you, and balls have a higher value for Alice. A balanced proposal could be: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:05,864][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:05,874][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:05,902][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:05,906][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:05,916][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:05,920][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:06,032][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:06,035][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:06,047][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:06,050][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:06,074][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 books, 10 hats, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:06,102][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:06,104][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:06,132][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:06,144][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:06,172][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:06,175][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:06,190][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:06,193][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:06,207][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:06,210][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:06,299][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Alice place significant value on different items. You value books highly and balls moderately, while Alice values books and balls highly, but not hats. This suggests a potential for misallocation if not handled carefully. However, since the total quantity of items is equal to what each of you proposes, and assuming a proportional distribution, here's a proposal that aims to balance the distribution while maximizing your value: proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:06,326][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:06,329][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:06,340][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:06,345][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:06,372][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 books, 10 hats, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:06,399][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:06,406][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:06,424][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:06,438][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:06,466][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:06,473][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:06,482][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:06,484][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:06,487][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:06,493][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:06,499][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:06,502][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:06,588][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:06,595][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:06,803][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:06,806][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:06,817][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:06,824][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:06,918][mllm.models.large_language_model_local][WARNING] - Response Given the item values, it is beneficial to propose a distribution that maximizes the utilization of high-value items while also considering the proportional sharing if the sum exceeds the item quantity. Here, hats and books have high value for you, and both you and Bob value balls less. A fair and strategic proposal would be to take the maximum quantity of high-value items you can while also securing a fair share of balls. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:07,017][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:07,030][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:07,043][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:07,047][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:07,086][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Alice place the highest value on balls. However, since the proposal exceeds the total items available, they will be split proportionally. To maximize your points, you should propose to take the majority of the balls. Here's a strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:07,090][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:07,092][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:07,118][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:07,124][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:07,146][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:07,148][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:07,163][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:07,165][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:07,216][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:07,255][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:07,258][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:07,282][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the fact that both you and Alice value the same items more or less the same way, proposing the full allocation of items seems reasonable to maximize both your points. Here's the proposal: proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:07,295][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:07,309][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:07,324][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:07,327][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:07,365][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:07,372][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:07,375][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:07,427][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:07,431][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:07,442][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:07,445][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:07,448][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:07,451][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:07,530][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:07,531][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:07,589][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:07,604][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:07,611][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:07,645][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:07,653][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:07,727][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:07,734][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:07,743][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:07,745][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:07,749][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:07,755][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:07,826][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:07,833][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:07,951][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:08,047][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:08,054][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:08,225][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:08,229][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:08,239][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:08,242][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:08,245][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:08,382][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:08,386][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:08,397][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Alice have different preferences. You value hats and books less, while valuing balls highly, whereas Alice values books more than you do. To maximize your points, you should propose to keep more of the items you value more. proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:08,445][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:08,449][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:08,461][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:08,468][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:08,472][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:08,608][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:08,611][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:08,693][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:08,707][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:08,714][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:08,857][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:08,864][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:09,452][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric and high value of hats and books for both Alice and myself, I should aim to maximize the points by ensuring I get as many hats and books as possible, while still proposing a fair share of balls to avoid any proportional splitting that would reduce my overall earnings. proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:10,238][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:10,345][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric and strategic nature of the game, where both you and Bob have identical proposals in each round, let's consider the optimal strategy. Since both of your per-item values and Bob's per-item values are highly imbalanced, it makes sense to allocate items based on their relative values. Here's the optimal allocation: - Hats: You value hats at 10, Bob values hats at 1. You should take all 10 hats. - Books: You value books at 10, Bob values books at 10. You should take all 10 books. - Balls: You value balls at 1, Bob values balls at 10. Bob should take all 10 balls. Thus, the optimal proposal is: Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:10,419][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:10,441][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric and equal perceived value distribution between hats and books, and the lower value for balls, a strategic approach would be to maximize the allocation of items with higher personal value while ensuring the proposal does not exceed the total item quantity. Here, since both Alice and I value hats and books highly, proposing to take all of them proportionally could lead to higher earnings. proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:08:10,601][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:11,327][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's beneficial to propose a split that maximizes the points based on both our and Alice's valuations. Since hats and books are valued highly by both of us, and balls are valued highly by Alice, we should try to allocate as many hats and books as possible to ourselves while still proposing a fair share of balls to Alice. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:08:14,264][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both you and Alice place a high value on books and a low value on balls. However, distributing the items proportionally based on our values would lead to optimal points for both of us. Your per-item values: hats=1, books=10, balls=1 Alice's per-item values: hats=10, books=1, balls=1 A proportional split would be: - Hats: You get 1/11 of 10 hats, Alice gets 10/11 of 10 hats. - Books: You get 10/11 of 10 books, Alice gets 1/11 of 10 books. - Balls: Since both values are 1, you and Alice will split them equally. For simplicity, we can propose to split them this way: - Hats: 10 (you) and 0 (Alice) or 0 (you) and 10 (Alice) - Books: 10 (you) and 0 (Alice) or 0 (you) and 10 (Alice) - Balls: 5 (you) and 5 (Alice) To ensure both get their preferred items, we can propose: proposal: 10 hats, 10 books, 5 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:08:15,330][__main__][INFO] - Number of regex retries in iteration 573: 2292 [2025-11-13 07:08:15,331][__main__][INFO] - agents played in iteration 573 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 07:08:16,257][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 07:08:16,290][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 07:08:16,315][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 07:08:16,337][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.59%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 07:08:16,338][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 07:08:16,338][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 07:08:17,087][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 07:08:17,535][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 07:08:18,036][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 07:08:18,531][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 07:08:19,023][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 07:08:19,517][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 07:08:20,009][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 07:08:20,501][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 07:08:20,993][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 07:08:21,486][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 07:08:21,978][mllm.training.trainer_common][INFO] - 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Processing mini-batch 43 of 64 [2025-11-13 07:08:38,936][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 07:08:39,439][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 07:08:39,937][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 07:08:40,447][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 07:08:40,945][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 07:08:41,445][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 07:08:41,958][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 07:08:42,456][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 07:08:42,955][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 07:08:43,455][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 07:08:43,953][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 07:08:44,461][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 07:08:44,961][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 07:08:45,464][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 07:08:45,965][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 07:08:46,465][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 07:08:46,965][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 07:08:47,468][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 07:08:47,965][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 07:08:48,471][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 07:08:48,973][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 11319 tokens. [2025-11-13 07:08:49,865][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.24%, Current % of VRAM taken: 59.69%, Block Peak % of device VRAM: 67.05%, ΔTime: 00:00:32 [2025-11-13 07:08:50,512][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 07:08:50,514][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 07:08:50,517][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 07:08:51,313][__main__][INFO] - Iteration 574 took 1m 44s (65.60% Gen, 33.64% Train). Generation: 1m 8s, Training: 35s. Estimated remaining time: 78h 11m 25s. Estimated total time: 87h 10m 34s. Time estimates for 10 more iterations: 17m 26s, 100 more iterations: 2h 54m 21s, 500 more iterations: 14h 31m 45s. [2025-11-13 07:08:51,316][__main__][INFO] - Starting iteration 574. [2025-11-13 07:08:51,836][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 57 and human policies 1. [2025-11-13 07:08:51,836][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 07:09:09,961][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:09,964][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:09,984][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:09,988][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:09,990][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:09,993][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:09,995][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:09,998][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:10,001][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:10,003][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:10,006][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:10,008][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:10,010][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:10,012][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:10,015][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:10,018][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:10,020][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:10,022][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:10,024][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:10,026][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:10,029][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:10,041][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:10,043][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:10,045][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:10,047][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:10,049][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:10,052][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:10,053][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:10,055][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:10,057][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:10,060][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:10,062][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:10,064][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:10,066][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:10,068][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:10,070][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:10,072][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:10,074][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:10,076][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:10,077][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:10,079][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:10,081][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:10,083][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:10,084][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:10,087][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:10,088][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:10,091][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:10,092][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:10,094][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:10,096][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:10,098][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:10,100][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:10,102][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:10,104][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:10,105][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:10,107][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:10,117][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:10,119][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:10,120][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:10,122][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:10,124][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:10,125][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:10,126][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:10,127][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:10,129][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:10,131][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:10,132][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:10,134][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:10,136][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:10,137][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:10,138][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:10,140][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:10,141][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:10,143][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:10,144][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:10,146][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:10,148][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:10,150][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:10,152][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:10,153][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:10,154][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:10,155][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:10,156][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:10,157][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:10,159][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:10,160][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:10,161][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:10,162][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:10,163][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:10,164][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:10,164][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:10,165][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:10,167][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:10,168][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:10,170][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:10,171][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:10,172][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:10,172][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:10,173][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:10,174][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:10,277][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,279][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,294][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,297][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,299][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,302][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,305][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,308][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,310][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,317][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,319][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,321][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,324][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,326][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,328][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,331][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,333][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,336][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,342][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,344][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,346][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,348][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,351][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,356][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,357][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,360][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,361][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,364][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,367][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,369][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,371][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,372][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,374][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,379][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,380][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,381][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,381][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,384][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,385][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,387][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,388][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,390][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,392][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,393][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,398][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,401][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,403][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,405][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,407][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,414][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,416][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,418][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,420][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,421][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,424][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,426][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,427][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,429][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,431][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,432][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,433][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,433][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,438][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,439][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,441][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,443][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,444][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,446][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,447][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,448][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,449][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,451][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,452][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,454][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,455][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,456][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,457][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,458][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,459][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,460][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,462][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,465][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,466][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,468][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,468][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,472][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,473][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,475][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,477][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,478][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,479][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,480][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,482][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,483][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,483][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,484][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,484][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,485][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,486][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,487][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:10,577][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,582][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,596][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,601][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,606][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,615][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,620][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,625][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,629][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,636][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,641][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,644][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,648][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,651][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,655][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,658][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,662][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,667][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,671][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,675][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,678][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,682][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,684][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,691][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,693][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,695][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,698][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,700][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,703][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,706][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,709][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,712][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,715][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,718][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,720][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,722][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,725][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,730][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,733][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,736][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,738][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,740][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,743][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,745][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,747][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,750][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,752][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,754][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,756][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,759][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,761][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,764][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,768][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,771][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,774][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,777][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,779][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,781][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,788][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,792][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,795][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,798][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,801][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,804][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,808][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,810][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,813][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,815][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,817][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,819][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,821][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,824][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,826][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,830][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,832][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,834][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,836][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,837][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,839][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,842][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,844][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,846][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,848][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,850][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,853][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,855][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,859][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,861][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,863][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,865][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,867][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,870][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,872][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,873][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,875][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,878][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,880][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,883][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,885][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:10,888][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:11,872][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:11,874][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:11,891][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:11,895][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:11,915][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:11,918][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:11,923][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:11,939][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:11,942][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:11,945][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:11,949][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:11,951][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:11,954][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:11,957][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:11,966][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:11,968][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:11,970][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:11,981][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:11,984][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:11,987][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:11,989][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:11,991][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:11,994][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:11,995][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:11,996][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:12,005][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:12,006][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:12,008][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:12,010][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:12,011][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:12,012][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:12,013][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:12,015][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:12,028][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:12,029][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:12,031][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:12,034][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:12,036][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:12,037][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:12,039][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:12,041][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:12,050][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:12,052][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:12,054][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:12,056][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:12,058][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:12,060][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:12,061][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:12,063][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:12,065][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:12,070][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:12,072][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:12,074][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:12,076][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:12,077][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:12,079][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:12,082][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:12,084][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:12,086][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:12,095][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:12,097][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:12,099][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:12,100][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:12,102][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:12,104][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:12,106][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:12,108][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:12,109][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:12,111][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:12,113][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:12,114][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:12,116][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:12,117][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:12,123][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:12,125][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:12,127][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:12,129][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:12,131][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:12,132][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:12,134][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:12,136][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:12,137][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:12,139][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:12,145][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:12,146][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:12,148][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:12,150][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:12,152][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:12,153][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:12,155][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:12,157][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:12,159][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:12,160][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:12,165][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:12,166][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:12,168][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:12,170][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:12,172][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:12,252][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,255][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,267][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,270][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,284][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,287][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,289][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,299][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,301][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,303][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,317][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,320][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,322][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,324][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,327][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,329][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,331][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,338][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,340][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,343][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,345][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,347][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,350][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,352][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,357][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,359][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,361][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,363][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,365][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,367][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,369][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,371][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,373][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,379][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,381][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,382][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,384][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,386][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,387][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,389][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,390][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,393][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,394][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,396][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,398][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,399][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,401][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,403][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,409][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,411][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,412][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,414][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,416][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,419][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,421][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,422][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,424][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,429][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,431][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,433][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,434][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,436][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,437][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,439][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,441][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,442][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,447][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,447][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,449][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,450][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,451][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,451][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,453][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,454][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,456][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,456][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,465][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,467][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,469][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,471][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,472][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,473][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,476][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,477][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,479][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,481][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,482][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,485][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,487][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,488][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,489][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,490][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,492][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,493][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,494][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,495][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,502][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:12,570][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,575][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,587][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,592][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,603][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,608][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,621][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,626][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,630][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,634][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,641][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,645][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,648][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,652][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,664][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,668][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,671][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,675][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,679][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,685][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,689][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,692][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,695][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,699][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,702][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,705][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,708][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,713][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,717][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,720][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,723][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,726][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,729][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,732][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,735][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,739][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,742][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,745][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,747][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,750][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,753][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,755][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,757][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,760][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,763][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,765][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,768][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,772][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,774][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,776][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,779][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,782][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,784][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,787][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,790][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,793][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,796][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,798][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,800][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,802][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,804][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,807][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,809][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,811][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,815][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,817][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,819][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,821][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,824][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,826][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,828][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,830][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,832][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,835][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,837][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,839][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,842][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,844][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,846][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,848][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,850][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,852][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,854][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,856][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,860][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,862][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,864][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,866][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,868][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,869][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,871][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,873][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,876][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,878][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,880][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,882][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:12,885][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:13,498][mllm.models.large_language_model_local][WARNING] - Response Given the updated per-item values, where you have a high preference for hats and balls and Alice has a high preference for hats and books, a strategic proposal would be to max out the items you value highly while considering Alice's preferences. Here’s a proposal: Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:13,870][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:13,874][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:13,890][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:13,893][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:13,896][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:13,898][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:13,914][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:13,938][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:13,941][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:13,944][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:13,946][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:13,949][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:13,951][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:13,961][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:13,965][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:13,986][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:13,989][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:13,992][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:13,995][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:13,997][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:14,007][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:14,009][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:14,012][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:14,014][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:14,016][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:14,018][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:14,021][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:14,034][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:14,037][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:14,040][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:14,043][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:14,045][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:14,048][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:14,050][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:14,053][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:14,062][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:14,065][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:14,067][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:14,070][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:14,072][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:14,074][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:14,077][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:14,078][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:14,079][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:14,081][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:14,086][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:14,090][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:14,092][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:14,095][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:14,097][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:14,099][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:14,101][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:14,103][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:14,105][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:14,109][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:14,112][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:14,113][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:14,114][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:14,115][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:14,117][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:14,120][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:14,121][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:14,123][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:14,128][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:14,130][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:14,132][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:14,133][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:14,135][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:14,135][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:14,136][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:14,137][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:14,137][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:14,140][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:14,143][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:14,145][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:14,152][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:14,154][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:14,156][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:14,157][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:14,159][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:14,161][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:14,162][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:14,164][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:14,165][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:14,167][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:14,169][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:14,174][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:14,176][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:14,178][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:14,180][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:14,182][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:14,183][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:14,185][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:14,187][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:14,188][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:14,190][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:14,191][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:14,260][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:14,261][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:14,277][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:14,278][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:14,281][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:14,284][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:14,296][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:14,313][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:14,316][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:14,318][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:14,320][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:14,322][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:14,331][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:14,333][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:14,335][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:14,348][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:14,351][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:14,353][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:14,355][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:14,363][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:14,365][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:14,367][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:14,369][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:14,371][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:14,372][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:14,375][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:14,376][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:14,383][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:14,384][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:14,386][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:14,388][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:14,389][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:14,400][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:14,403][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:14,404][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:14,407][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:14,409][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:14,411][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:14,412][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:14,420][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:14,422][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:14,423][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:14,427][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:14,429][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:14,431][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:14,433][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:14,435][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:14,436][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:14,442][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:14,443][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:14,445][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:14,446][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:14,448][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:14,449][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:14,451][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:14,452][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:14,454][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:14,455][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:14,456][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:14,457][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:14,461][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:14,464][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:14,465][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:14,466][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:14,468][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:14,469][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:14,471][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:14,473][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:14,475][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:14,477][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:14,478][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:14,480][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:14,483][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:14,484][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:14,487][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:14,488][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:14,489][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:14,490][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:14,491][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:14,492][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:14,492][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:14,503][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:14,505][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:14,505][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:14,506][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:14,507][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:14,508][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:14,509][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:14,511][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:14,512][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:14,520][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:14,522][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:14,523][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:14,525][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:14,587][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:14,592][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:14,603][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:14,608][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:14,612][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:14,616][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:14,624][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:14,642][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:14,647][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:14,651][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:14,656][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:14,663][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:14,667][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:14,671][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:14,674][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:14,684][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:14,689][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:14,693][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:14,697][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:14,710][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:14,714][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:14,717][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:14,721][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:14,724][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:14,727][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:14,735][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:14,739][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:14,742][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:14,746][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:14,749][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:14,752][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:14,755][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:14,759][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:14,762][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:14,765][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:14,767][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:14,769][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:14,773][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:14,776][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:14,778][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:14,785][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:14,789][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:14,794][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:14,798][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:14,803][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:14,805][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:14,807][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:14,810][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:14,812][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:14,814][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:14,817][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:14,819][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:14,821][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:14,824][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:14,828][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:14,830][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:14,833][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:14,835][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:14,838][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:14,840][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:14,842][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:14,845][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:14,848][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:14,850][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:14,852][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:14,855][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:14,857][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:14,860][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:14,864][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:14,866][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:14,869][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:14,871][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:14,874][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:14,877][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:14,879][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:14,882][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:14,884][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:14,887][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:14,889][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:14,891][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:14,894][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:14,898][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:14,900][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:14,902][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:14,904][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:14,906][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:14,907][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:14,909][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:14,911][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:14,914][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:14,917][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:14,919][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:14,922][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:15,318][mllm.models.large_language_model_local][WARNING] - Response Given the values, both you and Bob place high value on books and low value on hats, while both value balls about the same. Here’s a proposal that maximizes your points by focusing on your higher value for books and balls while considering Bob’s preferences: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:15,489][mllm.models.large_language_model_local][WARNING] - Response Given the updated values where we both place high value on hats and books while valuing balls less, a more strategic proposal might be to maximize our points on the items we value higher. Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:15,491][mllm.models.large_language_model_local][WARNING] - Response Given the new per-item values, I should focus more on the items that I value highly (hats and balls) and less on the one that Alice values highly (books). Proposal: 10 hats, 10 balls, 0 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:15,968][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:15,970][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:15,971][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:15,972][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:15,999][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:16,000][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:16,002][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:16,024][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:16,048][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:16,049][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:16,071][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:16,072][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:16,074][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:16,075][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:16,076][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:16,077][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:16,098][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:16,099][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:16,100][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:16,122][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:16,125][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:16,127][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:16,149][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:16,151][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:16,153][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:16,155][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:16,158][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:16,160][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:16,170][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:16,172][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:16,194][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:16,197][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:16,199][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:16,214][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:16,220][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:16,226][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:16,229][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:16,231][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:16,233][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:16,235][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:16,238][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:16,240][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:16,242][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:16,249][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:16,251][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:16,254][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:16,255][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:16,257][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:16,259][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:16,260][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:16,263][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:16,266][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:16,269][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:16,271][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:16,273][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:16,274][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:16,275][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:16,278][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:16,280][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:16,283][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:16,285][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:16,286][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:16,287][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:16,289][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:16,290][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:16,290][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:16,291][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:16,293][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:16,308][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:16,309][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:16,329][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:16,331][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:16,347][mllm.models.large_language_model_local][WARNING] - Response Given the new round's item values, where you have a high value for hats and balls, and both you and Alice value books similarly, a strategic approach would be to minimize the risk of proportional allocation by ensuring the sum of our proposals does not exceed the total item quantity. Here’s the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:16,379][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:16,381][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:16,383][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:16,386][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:16,398][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:16,401][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:16,403][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:16,416][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:16,435][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:16,438][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:16,453][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:16,456][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:16,458][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:16,460][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:16,470][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:16,473][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:16,488][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:16,507][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:16,510][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:16,513][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:16,516][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:16,519][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:16,524][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:16,526][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:16,544][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:16,546][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:16,581][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:16,584][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:16,587][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:16,590][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:16,593][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:16,595][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:16,597][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:16,610][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:16,612][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:16,615][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:16,617][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:16,618][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:16,619][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:16,622][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:16,625][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:16,627][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:16,629][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:16,630][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:16,632][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:16,635][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:16,640][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:16,642][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:16,644][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:16,646][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:16,648][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:16,650][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:16,652][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:16,654][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:16,656][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:16,658][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:16,660][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:16,662][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:16,677][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:16,679][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:16,712][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:16,713][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:16,727][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:16,760][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:16,763][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:16,768][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:16,773][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:16,781][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:16,786][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:16,799][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:16,819][mllm.models.large_language_model_local][WARNING] - Response Proposals for this round, considering Alice's values, might involve trying to maximize the allocation of items based on their relative values. Since you value hats and books more, and Alice values balls more, a cooperative yet slightly competitive approach might be effective. Proposal: 10 hats, 10 books, 10 balls However, a more strategic approach might be to slightly withhold some of the items to ensure they are allocated proportionally, pushing Alice to agree on a slightly smaller but still valuable allocation. Proposal: 9 hats, 9 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:16,822][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:16,826][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:16,838][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:16,843][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:16,847][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:16,851][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:16,859][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:16,862][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:16,879][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:16,898][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:16,902][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:16,917][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:16,921][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:16,926][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:16,936][mllm.models.large_language_model_local][WARNING] - Response Given the values, both you and Alice place high value on the same items (books and balls), while valuing hats similarly but lower. Here's a strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:16,939][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:16,943][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:16,958][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:16,960][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:16,977][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:16,981][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:16,985][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:16,996][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:16,999][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:17,003][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:17,007][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:17,009][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:17,021][mllm.models.large_language_model_local][WARNING] - Response Given the new values, you highly value hats and books, while Bob highly values balls. To maximize your points, you should propose to take all the hats and books, leaving the balls for Bob. Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:17,022][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:17,026][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:17,029][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:17,033][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:17,036][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:17,040][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:17,044][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:17,047][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:17,050][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:17,053][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:17,055][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:17,059][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:17,062][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:17,065][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:17,067][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:17,070][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:17,073][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:17,076][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:17,078][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:17,081][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:17,083][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:17,086][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:17,089][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:17,091][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:17,097][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:17,099][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:17,101][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:17,103][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:17,152][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and Alice's strategy, it appears that Alice values hats and books highly and balls less. To optimize my points, I should allocate more of the balls to myself and distribute hats and books more evenly. Here is my proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:17,273][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both hats and balls are highly valuable to Bob, while books have a negligible value for both of us. To maximize my points, I should propose keeping all items to ensure I get the full value from hats and balls. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:17,434][mllm.models.large_language_model_local][WARNING] - Response Given the pattern of our previous proposals and the fact that both Alice and I have identical valuations for hats and balls, but different valuations for books, a strategic approach would be to ensure we get our high-value items while accommodating Alice's preferences for books and our own for hats and balls. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:17,437][mllm.models.large_language_model_local][WARNING] - Response Given that both of our per-item values for hats and books are relatively low, while balls have high values for both, it makes sense to prioritize getting as many balls as possible while also securing a few hats and books to avoid any one category dominating our loss. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:17,491][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:17,525][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:17,528][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:17,557][mllm.models.large_language_model_local][WARNING] - Response Given the values, you have a significant preference for hats and balls, while Alice has a similar preference but for balls and books. Since both of you have the same per-item values for balls, it's crucial to secure as many hats and balls as possible to maximize your points. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:17,630][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Alice place the highest value on books and balls, while hats have a relatively low value for both of you. To maximize your points, you should propose to take the majority of books and balls, while allocating a small number of hats. Proposal: 1 hat, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:17,633][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:17,636][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:17,693][mllm.models.large_language_model_local][WARNING] - Response Given the values, both you and Bob have high interest in the same items (hats and books), and both balls are less valuable to both of you. A strategic approach would be to maximize the items you value highly while ensuring Bob does not get more than you can afford to lose. Proposal: 10 hats, 10 balls, 10 books This proposal ensures that you get the maximum value from the items you and Bob both want, while also getting the less valued balls, thereby optimizing your points. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:17,700][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:17,928][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:18,091][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:18,094][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:18,169][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that we both have a significant value for hats (10) and Alice values balls more (10 vs. 1). To maximize our points, we should try to get as many hats and balls as possible while not overproposing books, which both of us value minimally. Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:18,172][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:18,175][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:18,219][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Bob value books and balls highly, while the hats are less valuable. Since the value of books and balls is relatively high compared to the hats, you should try to secure as many books and balls as possible while giving some hats to Bob to ensure he doesn't propose an unfair split. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:18,222][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values where you have a higher value for books and balls, but Bob also values these items, it's crucial to propose a distribution that effectively maximizes your points. Since both you and Bob value books and balls, it might be wise to propose a split that addresses these high-value items first and then share the hats equally. Proposal: 5 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:18,225][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the equal likelihood of receiving items, a strategic approach would be to value the items based on their per-item values. Since hats and books have higher values for you, and balls have a higher value for Bob, it might be beneficial to propose keeping more hats and books to maximize your points. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:18,227][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:18,243][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:18,246][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:18,265][mllm.models.large_language_model_local][WARNING] - Response Given the new per-item values where both you and Bob place high value on hats and books, and low value on balls, a strategic approach would be to align your proposal closely with these values to maximize your points. Here's the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:18,268][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:18,271][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:18,289][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both hats and balls have higher values for me compared to Alice. However, Alice values books highly. To maximize my points, I should focus on claiming more hats and balls while perhaps allowing Alice to take most of the books. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:18,292][mllm.models.large_language_model_local][WARNING] - Response Given the new per-item values, I should prioritize taking items that are highly valued by me and less valued by Bob. Here's my proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:18,298][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:18,313][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:18,336][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that the balls are highly valued by Bob, while hats are highly valued by you. To maximize your points, you should keep as many hats as possible and ensure you get a fair share of balls. Here's a proposal that tries to optimize your points: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:18,339][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:18,341][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:18,361][mllm.models.large_language_model_local][WARNING] - Response Given the high value of hats and books for both you and Bob, while balls have low value for both, a strategic approach would involve ensuring you get a fair share of the high-value items. A balanced proposal would be to split the items in a way that maximizes the points from the high-value items while minimizing loss from the low-value items. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:18,364][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:18,370][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:18,405][mllm.models.large_language_model_local][WARNING] - Response Given the values, you have a strong preference for hats and balls, while Alice prefers books and balls. Proposing to take the maximum of each item will likely lead to proportional allocation if the total proposed exceeds the available quantity. To maximize your points, you should maximize the items you value more, which are hats and balls, while still accounting for the possibility of proportional allocation. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:18,408][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the values and the fact that Alice and I both have high values for hats and balls respectively, and low values for books, I can leverage this by offering a deal that both of us can agree on. If I keep all the hats and balls, and Alice keeps all the books, we both stand to gain optimally based on our individual values. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:18,411][mllm.models.large_language_model_local][WARNING] - Response Given the values, Alice places high value on balls and I place high value on hats and books. If Alice gets the balls, she will likely propose to keep all of them. To maximize my points, I should propose to keep all the hats and books, as they are my highest valued items. Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:18,413][mllm.models.large_language_model_local][WARNING] - Response Given the values, I should focus on items where my value is significantly higher than Bob's. Here, books and balls are the items where my value is much higher. I will propose to take all books and balls, while taking just enough hats to ensure the total value is maximized. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:18,416][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:18,454][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric nature of the values and the previous outcomes where splitting everything led to a fair but not optimal distribution, we should reassess our strategy. Since both of us have high values for different items, a more strategic proposal might involve recognizing the importance ofballs, which have a value of 10 for you and 1 for Alice, and ensuring we maximize our points from those. Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:18,508][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:18,511][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:18,513][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:18,515][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:18,517][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:18,519][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:18,521][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:18,522][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:18,550][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:18,553][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:18,556][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:18,559][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:18,561][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:18,564][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:18,573][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:18,576][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:18,578][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:18,581][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:18,583][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:18,593][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:18,596][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:18,599][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:18,612][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:18,615][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:18,631][mllm.models.large_language_model_local][WARNING] - Response Given the shifted values, where Alice values books and balls significantly higher than hats, and you value books and balls lower than hats, a strategic approach would be to propose a split that takes into account the likely proportional allocation if the sum of our proposals exceeds the item quantity. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:18,638][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:18,665][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:18,683][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:18,686][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:18,689][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:18,706][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:18,708][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:18,711][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:18,725][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:18,732][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:18,747][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:18,762][mllm.models.large_language_model_local][WARNING] - Response Given the values, the optimal strategy would be to maximize the items with higher personal value while ensuring that the total amount proposed does not exceed the available items. Since both you and Bob have high values for hats and balls, and books have a lower value for both, a balanced approach is needed to avoid proposing more books than the other player, which would not maximize the score. Proposal: 10 hats, 10 balls, 0 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:18,767][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:18,773][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:18,775][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:18,796][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Bob have a strong preference for balls and relatively low values for hats and books. However, since both of you are proposing to take all of each item type, this suggests a competitive nature. To maximize your points, you should try to take as many of the items you value highly (balls) without overproposing, which could lead to proportional distribution if the total proposed amount exceeds the available quantity. Proposal: 10 hats, 10 books, 9 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:18,799][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:18,801][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:18,815][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Alice value hats and books more highly than balls. However, since balls are worth a lot to Alice and nothing to you, you should try to minimize the number of balls you take to avoid giving Alice a better allocation. At the same time, since you value hats and books, you should try to take a fair share of those items. Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:18,818][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:18,821][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:18,835][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:18,841][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:18,847][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:18,856][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:18,858][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:18,860][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:18,863][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:18,875][mllm.models.large_language_model_local][WARNING] - Response Given the values, you have a higher valuation for hats and books compared to Bob, while Bob values balls more. To maximize your points, you should propose to take all items where you have a higher value, while splitting the items where Bob has a higher value more fairly. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:18,881][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:18,883][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:18,894][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:18,897][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:18,899][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:18,902][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:18,904][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:18,907][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:18,919][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:18,922][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:18,924][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:18,926][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:18,936][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:18,939][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:18,941][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:18,943][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:18,945][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:18,955][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the previous round results, it appears that both Alice and I value hats and balls more than books. To optimize our points, we should focus on securing as many high-value items as possible. Since Alice values balls highly and I value hats and balls, we should try to propose a division that ensures we both get a fair share of the high-value items. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:18,957][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:18,959][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:18,974][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:18,979][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:19,015][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:19,036][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:19,047][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:19,052][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:19,066][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:19,068][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:19,073][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:19,086][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the strategic importance of the balls, it's crucial to consider the potential for misallocation. Here, both you and Alice value hats and books equally at 1, but balls are very important to you and to Alice only slightly. To maximize your points, you should aim to secure as many of the balls as possible while also taking into account the possibility of Alice also wanting to take balls. A balanced strategy would be to take all the balls and split the hats and books fairly. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:19,088][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:19,104][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:19,106][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:19,123][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:19,145][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:19,150][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:19,181][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:19,186][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:19,255][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Bob place a high value on balls and a low value on hats and books. However, since we cannot predict Bob's exact strategy and to maximize my score, I should propose to take the items that I value more while ensuring the total quantity of items proposed does not exceed what is available. Here is my proposal: Proposal: 10 hats, 10 balls, 10 balls This proposal ensures that I take all the hats, which I value at 1, and split the balls, which I value at 10, to maximize my potential points. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:19,257][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:19,263][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:19,266][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:19,269][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:19,271][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:19,289][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:19,292][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:19,299][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both hats and balls are highly valued by one of the players. To maximize points, we need to account for the fact that Bob values hats and books more than balls, while I value balls highly and hats and books less. Here's a strategic proposal: - I should keep all the balls since they are highly valued by Bob. - I should keep all the hats to maximize my total points. - I can split the books since their value is low for both of us, but I'd keep a few to ensure I get more points. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:19,302][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:19,308][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:19,313][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:19,318][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:19,328][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that hats and balls have higher values for you compared to Alice, while books have a similar value. Therefore, it's beneficial to maximize the allocation of hats and balls, while ensuring a reasonable share of books. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:19,330][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:19,335][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:19,339][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:19,344][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:19,348][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:19,380][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:19,385][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:19,389][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:19,394][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:19,458][mllm.models.large_language_model_local][WARNING] - Response Given the distribution of per-item values, where you value hats and books more and Alice values balls more, we can exploit this information to maximize our points. Here's the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:19,461][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:19,619][mllm.models.large_language_model_local][WARNING] - Response Given Alice's value for hats and balls, and your own values, proposing all items might not be the best strategy, as Alice would likely claim most of the hats and balls, while you could get more value from the books. Here's a revised proposal: Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:19,701][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric and competitive nature of the items and their values, it's important to consider the optimal allocation that maximizes your points. Since both hats and balls have high values, and books have a low value for both of us, we should aim to obtain as many hats and balls as possible while also securing some books. Considering the previous round where proposing 10 hats, 10 books, and 10 balls led to equal splits and lower points compared to the high-value items, a more strategic approach might be to propose slightly less for the high-value items to ensure a better distribution. Proposal: 8 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:19,705][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:19,743][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:19,784][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:19,843][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:19,894][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:19,900][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:19,902][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:19,905][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:19,907][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:20,000][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in values and the previous rounds' outcomes, where both agents tend to propose equal shares, a similar approach will likely yield good results. Here, I propose: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:20,002][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:20,069][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:20,075][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:20,082][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:20,089][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:20,107][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:20,125][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and Bob's per-item values, it makes sense to prioritize items based on their respective values. Since both you and Bob value hats and books highly, while balls are less valued, we can propose a fair split that maximizes our points. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:20,127][mllm.models.large_language_model_local][WARNING] - Response Given the new per-item values where you value hats and books more heavily, and Alice values balls more, a more strategic proposal might be necessary. Here's the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:20,179][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:20,265][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both hats and books are highly valued by one of the participants, while balls are valued equally but not as highly as hats and books. Here's a strategic proposal that maximizes the use of the higher values: Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:20,270][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:20,274][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:20,329][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:20,462][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:20,486][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:20,489][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:20,589][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in per-item values and the previous outcomes, it appears that proposing to split all items equally often results in the highest points due to the proportional allocation rule. Therefore, I will continue to propose to keep all items. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:20,593][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:20,622][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:20,625][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:20,693][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:20,762][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric and extreme values of per-item preferences, a strategic approach is needed to maximize points. To achieve this, I will propose a division that addresses the high preference for balls while ensuring that both Alice and I get a fair share of our preferred items. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:20,765][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:20,768][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:20,811][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:20,892][mllm.models.large_language_model_local][WARNING] - Response Given that Alice values books the highest and I value balls the highest, in this round, the optimal strategy would be to align our proposals to ensure we each get a fair share of the items we value highly. Since both of us have the same value for balls, it makes sense to propose splitting them evenly. Here is the proposal: Proposal: 10 hats, 10 books, 5 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:20,896][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:21,032][mllm.models.large_language_model_local][WARNING] - Response Given the values where I have a high value for hats and balls, but Alice has a high value for books, I should maximize my points by keeping as many hats and balls as possible, while also ensuring I get some books to contribute to my score. A balanced approach would be to propose a similar division to what was successful in prior rounds. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:21,035][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Bob value hats and books similarly but balls are less valued. To maximize your points, you should propose to take all the hats and books since you have a higher value for them compared to Bob, and leave the balls to him since he values them more. Here is the proposal: Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:21,084][mllm.models.large_language_model_local][WARNING] - Response Given the values, I should prioritize the items with higher personal value to maximize points. Since I value hats and balls more and Alice values them less, I should propose the majority of items for these categories while ensuring the distribution is fair enough to avoid being allocated proportionally if Alice disagrees. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:21,087][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:21,114][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the previous rounds' results, it appears that both Alice and I have similar strategies, resulting in equal shares of the items regardless of the proposed distribution. To maximize my points, I should aim to capture the items that I value more. Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:21,116][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:21,176][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:21,179][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:21,206][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both you and Bob value books the most and balls the least. To maximize your points, you should focus on proposing to keep more books and fewer balls, since books have the highest value for you and balls have the lowest. A strategic approach would be to propose keeping all the books and splitting the hats and balls as evenly as possible to avoid exceeding the total quantity of items. Proposal: 5 hats, 10 books, 5 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:21,210][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both hats and books are more valuable to me compared to balls. Since Bob values hats and books less than I do, and values balls more than I do, a competitive and strategic proposal would be to take as many hats and books as possible while giving Bob at least some balls to ensure the total quantity is met. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:21,213][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Bob value hats and books highly, and balls less so. A cooperative approach might yield the best results. You both value hats and books more than the other did in previous rounds, suggesting that sharing these items could lead to a better outcome. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:21,216][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:21,219][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:21,221][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:21,224][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:21,241][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it seems optimal to allocate the items in a way that maximizes the product of the amount received and the per-item value. Here, you have a higher value for hats (1) and balls (10) compared to Alice, while Alice values books more. Therefore, a strategic split would be to take all hats and balls and share the books evenly. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:21,244][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's beneficial to align your proposal with your higher values for hats and books, while recognizing Bob's higher value for balls. A strategic proposal would be to maximize your higher valued items and allocate the lesser valued items proportionally. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:21,246][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:21,256][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and the symmetry in the game, it seems optimal to propose an equal split of the items to ensure a fair distribution and maximize points based on our own valuations. Therefore, I will propose the following: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:21,268][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:21,295][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:21,315][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Alice have a strong preference for balls. In the previous rounds, proposing to take all the items led to an equal split, which resulted in lower points due to your low value for hats and books. A more strategic approach would be to take all the balls and split the hats and books proportionally. Here's the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:21,318][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Bob place a higher value on balls and hats rather than books. Since both of you have similar valuations for hats and books, and the balls are much more valuable to you, a strategic proposal might be to ensure you get more of the highly valued items (hats and balls) while leaving some items for Bob. Proposal: 10 hats, 10 books, 9 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:21,321][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:21,334][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:21,340][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:21,355][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:21,376][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:21,397][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:21,403][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:21,418][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:21,421][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:21,437][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Bob value hats and books highly, but balls less so. To maximize your points, you should propose to take as many of the items you value highly (hats and books) as possible while ensuring the proposal does not exceed the available items. Here’s the optimal proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:21,440][mllm.models.large_language_model_local][WARNING] - Response Given the values, it is clear that balls are much more valuable to both of us, and hats and books are relatively less valuable. To maximize my points, I should prioritize keeping as many balls as possible, while still considering hats and books since they have some value as well. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:21,443][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:21,445][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:21,473][mllm.models.large_language_model_local][WARNING] - Response Given the new valuations where both you and Alice value hats at 1 and balls at 10, but Alice values books at 1 while you value them at 10, a strategic approach would be to maximize the distribution of books since they have the highest value for you. Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:21,479][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:21,482][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:21,485][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:21,494][mllm.models.large_language_model_local][WARNING] - Response Given the values, it seems that splitting the items equally might not always be the best strategy. Since both of us value books highly and both of us value hats and balls less, it might be beneficial to propose a split that maximizes the value of books while still taking into account the values of hats and balls. Proposal: 10 hats, 10 books, 10 balls However, since books are highly valued by both and we want to maximize our points, we might want to consider proposing slightly less of books to avoid an equal split, which would result in lower points for both due to the low individual values of hats and balls. Proposal: 10 hats, 10 books, 9 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:21,500][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the high value of the items, the best strategy is to propose an equal split to maximize the points. Here is my proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:21,503][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:21,506][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:21,517][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:21,522][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:21,536][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:21,538][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:21,541][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:21,557][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:21,575][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that Alice values books the most and hats and balls equally. You value hats and books equally and balls the least. If we both propose to take all 10 items of each category, we will be splitting the items proportionally if our proposals exceed the available quantity. To maximize your points, you should propose a distribution that takes advantage of your value for hats and books while still considering the proportional split. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:21,578][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:21,580][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:21,593][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:21,595][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:21,612][mllm.models.large_language_model_local][WARNING] - Response Given the values, Alice places a high value on hats and books, while you place a high value on hats and books but slightly lower on balls compared to Alice. The optimal strategy would be to propose dividing the items in a way that maximizes the value you get from your high-value items (hats and books) while considering the proportional sharing rule if the total proposed exceeds the available items. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:21,615][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:21,621][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:21,624][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:21,626][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:21,633][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:21,650][mllm.models.large_language_model_local][WARNING] - Response Given the new per-item values, where both you and Alice value hats and books low (1), but value balls high (10), it makes strategic sense to focus on maximizing the number of balls, while also considering the potential for proportional allocation if the total proposed exceeds the available items. Proposal: 10 hats, 10 books, 15 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:21,684][mllm.models.large_language_model_local][WARNING] - Response Given the values, it seems both you and Bob have complementary preferences for hats and books, and both value balls less. Here’s a strategy to maximize points considering these values: - You value hats and books highly (10), while Bob values them low (1). - You value balls highly (10), while Bob values them highly (10). A balanced proposal would be to take the items that both value highly. Here is a proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:21,687][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:21,701][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:21,722][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:21,746][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:21,759][mllm.models.large_language_model_local][WARNING] - Response Given the values, you place a very high value on hats and books compared to balls, while Bob values hats and balls more than books. To maximize your points, you should propose to take the majority of the items you value highly. Here's a strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:21,796][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:21,834][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:21,840][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:21,867][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:21,874][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:21,880][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:21,892][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:21,895][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:21,911][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:21,914][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:21,934][mllm.models.large_language_model_local][WARNING] - Response Given the new values, my strategy should be to propose a split that aligns with Bob's lower values for hats and books, while securing the higher value for balls. Here's the optimal proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:21,937][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:21,956][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:21,958][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:22,547][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:22,632][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:22,639][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:22,654][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:22,730][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:22,737][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:22,739][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:22,746][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:22,787][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:22,854][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that hats and books are highly valued by both of us, while balls are of low value to both. To maximize points, we should try to secure as many hats and books as possible, and split or minimize the balls. Here's a proposed strategy: - Each of us values hats and books highly, so we should claim as many as possible. - We should be careful not to overbid on balls, as they have low value and can be split proportionally in case of excess claims. A reasonable proposal would be to each take 5 hats and 5 books, and split the balls equally. Here's the proposal: Proposal: 5 hats, 5 books, 5 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:22,901][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and the symmetry in the situation, it's clear that the optimal strategy would be to propose a split that maximizes the total value for both you and Bob, while ensuring that the total proposed quantity does not exceed the available items. Since both parties have the same per-item values for the same items, we can propose a fair split. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:22,904][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the fact that both Alice and Bob have identical and high values for hats and books, while values for balls are reversed, a strategic proposal would be to maximize the items with higher individual values while ensuring that the total proposed quantities do not exceed the available items. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:22,907][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:22,920][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Bob have a strong preference for books and balls, while hats have a relatively lower value for both. To maximize your points, you should try to secure as many books and balls as possible, since these items have a high value for you. A reasonable strategy here is to allocate the majority of items to books and balls, and the remaining to hats. Since both of you value hats less, proposing to keep all hats yourself is a good starting point. Then, you can propose to keep a significant portion of the books and balls. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:22,923][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Alice place very different values on the items. You value hats and balls highly and books minimally, while Alice values balls minimally and books highly. To maximize your points, you should try to secure more of the items you value highly. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:22,927][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:22,945][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:23,010][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric nature of the per-item values and the fact that both you and Bob have high values for different items, it's crucial to ensure that both of you receive a fair share to maximize points. Here’s a balanced proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:23,041][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:23,048][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:23,066][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:23,068][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:23,075][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:23,081][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:23,142][mllm.models.large_language_model_local][WARNING] - Response Given Alice's new per-item values, she values balls the most and hats the second most. To maximize my points, I should propose an allocation that reflects the high value of balls for both of us. A reasonable strategy is to propose an equal split of hats and books, but allocate all balls to myself to maximize the points from the balls. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:23,145][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:23,147][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:23,195][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values where you significantly value hats and balls more than books, and Alice values books and hats more than balls, a strategic allocation would be to maximize your points by keeping more of the items you value highly. Here’s a proposal that takes advantage of this: Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:23,278][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both hats and books have significantly higher values for you compared to Bob. Balls, however, have higher value for Bob. To maximize your points, you should propose to take all the hats and books, and divide the balls proportionally. Proposal: 10 hats, 10 books, 5 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:23,440][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and the previous round's outcomes, it seems that both Alice and Bob are valuing the items similarly, with a preference for books and balls over hats. A good strategy in this round would be to negotiate a fair division that reflects each agent's valuation. Proposal: 10 hats, 10 books, 10 balls This proposal ensures that both Alice and Bob get a fair share of all items, which should lead to a higher combined value for both agents compared to any other proposal that might lead to a division of items in a way that could be less optimal for either party. However, let's consider a slightly adjusted proposal to reflect the higher value of books and balls for both Alice and Bob. Proposal: 10 hats, 15 books, 15 balls By slightly increasing the number of books and balls each gets, we can potentially maximize the value while still maintaining a balanced approach. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:23,447][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the high value of books and balls for you, while hats have a higher value for Bob, a strategic proposal would be to maximize your points by keeping as many books and balls as possible while still valuing hats. Here's a proposal that balances both items: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:23,464][mllm.models.large_language_model_local][WARNING] - Response Given the values, the best strategy is to maximize the items that have higher value for you. Since your per-item values for books and balls are higher, you should propose to keep all of them, while dividing the hats equally or giving most to you, as hats have a lower value for you. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:23,468][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:23,472][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:23,490][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and equal per-item values in this round, the optimal strategy would be to propose an allocation that reflects the equal distribution of items, ensuring that both Alice and Bob receive a fair share. Therefore, the proposal should be: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:23,517][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:23,520][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:23,643][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:23,685][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Bob value hats and books relatively higher compared to balls. Since both of you have very similar valuations for hats and books, and balls have a much higher value per item for both of you, it might be strategic to prioritize ball distribution to maximize earnings. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:23,688][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Alice have a high value for different items. You value hats and balls highly, while Alice values balls and hats highly. To maximize your points, you need to secure the items you value the most. Proposal: 10 hats, 10 balls, 0 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:23,750][mllm.models.large_language_model_local][WARNING] - Response Given Alice's new values, she will disproportionately value hats and books over balls. Since my value for balls is significantly higher, and Alice's value for hats and books is higher, we need to strategically allocate the items to maximize points. If both propose to take all items, they will be allocated proportionally. However, since my value for balls is 10 and Alice's is only 1, I should try to ensure I get as many balls as possible, while splitting the hats and books in a way that doesn't significantly disadvantage me. Here's a proposal: - I will take all 10 balls. - I will propose to take 5 hats and 5 books. This ensures I get all the balls, which are worth the most to me, and splits the hats and books in a way that balances the items. Proposal: 10 balls, 5 hats, 5 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:23,756][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:23,759][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:23,831][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:23,834][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:23,997][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Bob place high value on the same items (books and balls) but have different preferences for hats. To maximize your points, you should propose to keep as many of the items you value highly (books and balls) as possible, while keeping a few hats for bargaining or to ensure a fair split. Proposal: 5 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:24,001][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:24,078][mllm.models.large_language_model_local][WARNING] - Response Given the values, you have a high value for books (10) and a low value for balls (1), while Alice values balls more highly. To maximize your points, you should propose to take all the books and balls, and share the hats equally. Proposal: 5 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:24,082][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, hats have little value to you and balls have high value, while the opposite is true for Bob. To maximize your points, you should propose to take all the balls and distribute the hats and books to maximize your score. Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:24,089][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:24,092][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:24,095][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:24,147][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:24,150][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:24,275][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:24,325][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:24,392][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and the symmetric nature of the game, it's optimal to propose an allocation that maximizes the value for both you and Alice. Since you value hats and books the same (10 each) and value balls much higher (10), while Alice values balls very low (1) and hats and books similarly (10 each), a cooperative proposal that splits the items evenly would be reasonable. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:24,395][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:24,423][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric nature of the values and the previous rounds' outcomes, it seems beneficial to propose an equal split to maximize the individual's points. Here is the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:24,488][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:24,494][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:24,497][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:24,520][mllm.models.large_language_model_local][WARNING] - Response Given the updated per-item values where I value hats and books much less (1) and Bob values them more (10), but both value balls the same (10), it makes sense to maximize the allocation of balls to ensure the highest points. However, since we both know the values, there's an incentive to propose fewer items to reduce the likelihood of proportional splitting. A strategic proposal would be to take just enough to secure the balls and not give Bob more than necessary for the hats and books. Proposal: 0 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:24,523][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:24,526][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:24,680][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both you and Bob value hats and books highly, while balls are less valued. However, to ensure fairness and maximize points, we should propose a split that respects these values while considering the proportional distribution if the total proposals exceed the item quantity. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:24,779][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values where I value hats and books at 10 and balls at 1, and Alice values hats at 1, books at 10, and balls at 1, it's clear that we both have the same values for balls and different values for hats and books. To maximize my points, I should propose to take all the balls since they have the highest value for me. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:24,797][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, I should focus on grabbing as many high-value items as possible since both I and Alice value hats and books highly, but Alice values balls more than the other items. Here's my proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:24,823][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the values and the previous outcomes, it seems that proposing to take all items is successful as both Alice and I have proposed to take all items and have received full returns. However, to ensure a strategic approach, let's distribute the items more carefully to prevent any misinterpretation or potential issues. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:24,856][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and the symmetry in both our and Alice's valuations, it's crucial to consider the strategic allocation to maximize points. Here, both you and Alice value the books and balls highly, while the hats have a low value for both of you. To maximize points, you could propose to take all the items in a way that seems fair but slightly favors you, given the high value you place on books and balls. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:24,859][mllm.models.large_language_model_local][WARNING] - Response Given that our valuations are very similar and the allocations are proportional, it might be beneficial to propose a split that maximizes our individual gains while considering Alice's potential strategies. Since both of us value hats and balls highly, we can try to secure as many of these items as possible while leaving some for Alice to ensure a fair distribution. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:25,002][mllm.models.large_language_model_local][WARNING] - Response Given that both you and Bob have high values for hats and balls, and low values for books, a strategic approach would be to try to maximize the items where you and Bob have high values. This can be achieved by proposing to take all the hats and balls and splitting the books equally, if possible. Here's the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:25,010][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:25,013][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:25,016][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:25,040][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:25,042][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:25,077][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the values of the items, proposing to take all of each item (10 hats, 10 books, 10 balls) is a reasonable strategy since both Alice and Bob will have similar per-item values. However, if we anticipate that taking all of each item might lead to a proportional allocation in case of over-proposals, we should consider the risk. But since we have observed that proposals match, we might as well stick to our values to maximize points. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:25,081][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:25,242][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric nature of the per-item values and the previous outcomes where proposing to take the entire allocation results in equal splits and thus equal points, a strategic proposal might involve recognizing that splitting the items in a way that accounts for the marginal value differences could be more beneficial. Since both you and Alice value balls highly, it makes sense to ensure that both of you get some balls. However, distributing hats and books more evenly might be a better approach to maximize utility given the high value items. Proposal: 10 hats, 10 books, 10 balls / 2 = 5 balls each So, the proposal would be: Proposal: 10 hats, 10 books, 5 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:25,326][mllm.models.large_language_model_local][WARNING] - Response Given Alice's per-item values for hats and books are much lower than mine, and her value for balls is the same, I should aim to take as many hats and balls as possible while giving her at least one book to keep the proposal fair and avoid a proportional split. Here is my proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:25,370][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both you and Alice value hats and books highly, while balls are valued less. However, to maximize points, we should consider the proportional distribution of items based on their values. Your per-item values for hats, books, and balls are 10, 10, and 1 respectively. Alice's per-item values are 1, 10, and 10. Given that both you and Alice value hats and books highly, we should aim to get as many of these items as possible to maximize our points. A balanced approach would be to propose a distribution where we take the majority of the hats and books, and a reasonable number of balls. Let's propose: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:25,373][mllm.models.large_language_model_local][WARNING] - Response Given Alice's updated per-item values, she values hats and books much less than before, and balls similarly to how I value them. To maximize my points, I should propose to take the majority of the items, especially the balls where my value is highest. Here's the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:25,376][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:25,442][mllm.models.large_language_model_local][WARNING] - Response Given that both Alice and Bob value the items differently, a proposal that takes into account these differences could lead to a more advantageous distribution. Since Alice values hats and books more, and Bob values books and balls more, it might be beneficial to propose a split that aligns more closely with these values. Proposal: 10 hats, 10 books, 10 balls However, to optimize the allocation, let's consider a more nuanced proposal: Proposal: 10 hats, 10 books, 10 balls This proposal takes into account the high value of books and balls for Bob and the high value of hats and books for Alice. Given the equal sharing of items and values, this seems like a fair and balanced proposal. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:25,469][mllm.models.large_language_model_local][WARNING] - Response Given the high value that Bob places on hats and books, and your lower but consistent values across items, it might be more strategic to propose an allocation that takes advantage of the fact that Bob values hats more highly. However, we also need to account for the fact that both of us will receive some benefit from this strategy since the items will be split proportionally if we exceed the quantity. Proposal: 10 hats, 10 books, 10 balls This proposal aligns with the observed pattern where both Alice and Bob claim all items. Given the symmetry and the proportional split, this is a reasonable initial move. If Bob continues to propose the same, we might need to adjust our strategy in subsequent rounds based on the outcomes. However, for this round, let's stick with the initial proposal. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:25,472][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both you and Bob have a high valuation for books and low valuation for balls. Since neither of you values balls highly, we can propose to keep as many balls as possible to ensure we get as many high-value items (hats and books) as we can, while also trying to account for the proportional distribution of hats and books. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:25,476][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:25,507][mllm.models.large_language_model_local][WARNING] - Response Given Alice's values, proposing a split that maximizes your value while considering the proportional allocation if the sum exceeds the item quantity, a strategic proposal would be to mirror Alice's proposal to encourage a fair split. Thus, the proposal is: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:25,555][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the fact that both you and Alice have the same preferences for hats and balls but different for books, it might be beneficial to recognize that splitting the items equally is not optimal. Alice values books more than you do, so she is likely to propose keeping more books for herself. To maximize your points, you should propose keeping the majority of the books for yourself and distributing the hats and balls more evenly. Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:25,561][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, the optimal strategy would be to propose a distribution that reflects the higher value items to ensure maximum points. Since both Alice and Bob value books the most at 10 each, it's beneficial to claim as many books as possible. Proposition: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:25,601][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:25,608][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:25,636][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:25,639][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:25,642][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:25,658][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:25,661][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:25,681][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:25,684][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:25,687][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:25,703][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:25,727][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:25,730][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:25,736][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:25,743][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:25,746][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:25,766][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:25,816][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:25,850][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Alice place the highest value on different items. You value hats and balls highly, while Alice values balls and books highly. In the previous rounds, proposing to take all items led to an equal split since both of you claimed the same amount. To potentially maximize my points, I should focus on items I value more and hope that Alice does not propose to take all of them. Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:25,855][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:25,858][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:25,860][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:25,863][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:25,911][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:25,914][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:25,984][mllm.models.large_language_model_local][WARNING] - Response Given the values, it makes sense to try to maximize the more valuable items (balls) while still considering the value of hats and books. Since both you and Bob value balls the most, trying to claim most of the balls could be beneficial. However, if a full allocation of balls would not be possible without exceeding the total item counts, we should also propose some hats and books to ensure we get some points from those items too. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:25,991][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:25,997][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:26,004][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:26,018][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both hats and books have higher value for both of us compared to balls. To maximize our points, we should try to allocate more hats and books to ourselves and fewer balls. Here's a strategic proposal: Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:26,039][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:26,060][mllm.models.large_language_model_local][WARNING] - Response Given the values, the optimal strategy is to propose an allocation that maximizes the points based on our high values for books and balls, while also considering Bob's values. Here's the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:26,063][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:26,066][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:26,069][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:26,133][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:26,136][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:26,152][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:26,176][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Bob place high value on books and balls, but you value hats much less than Bob does. To maximize your points, it's important to recognize that you should try to capture as much value from books and balls as possible, while also considering Bob's likely greedy strategy for hats. Proposal: 10 hats, 10 books, 10 balls However, since you value hats much less, you might want to propose slightly less in hats to ensure that you get more from books and balls. Here’s a more strategic proposal: Proposal: 5 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:26,179][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both hats and balls are highly valuable to you compared to Alice, while books are valuable to Alice but not to you. A strategic proposal would be to allocate the items in a way that maximizes your points while also considering Alice's valuation. Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:26,182][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:26,184][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:26,212][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:26,215][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:26,251][mllm.models.large_language_model_local][WARNING] - Response Given Alice's values for hats and balls being very low, and her value for books being significantly higher than yours, you might want to propose keeping more of the items she values less to maximize your points. Here’s a proposal that takes this into account: Proposal: 10 hats, 10 books, 10 balls This proposal keeps the distribution even, valuing each item type equally, but since Alice values books much less, you might want to consider giving her fewer books and more hats or balls. However, without more strategic insight into her future valuations, an even split seems reasonable. If you want to adjust slightly to give Alice fewer books, you could try: Proposal: 10 hats, 8 books, 10 balls This would give Alice fewer books but still maintain a balanced distribution of the other items. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:26,258][mllm.models.large_language_model_local][WARNING] - Response Given the values, it is clear that both you and Bob place high value on books and balls. Since the items are split proportionally and you both propose the same distribution, it results in a fair split. However, to potentially gain a strategic advantage, you could try to ensure you get a larger share of the items you value more. Proposal: 10 hats, 10 books, 10 balls Since the initial proposal led to a fair split, you might consider proposing a slightly different distribution to see if it can lead to a more favorable outcome. Here is a revised proposal: Proposal: 10 hats, 10 books, 9 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:26,261][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both you and Bob value hats and books significantly more than balls. Given the symmetry and the high value of hats and books, a strategic proposal would be to maximize the value of these items. Here’s a proposal that takes this into account: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:26,264][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:26,267][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:26,270][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:26,277][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:26,280][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:26,283][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:26,297][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:26,304][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:26,307][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:26,338][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric nature of the situation where both you and Bob value hats and books significantly more than balls, the optimal strategy is to maximize the items you receive, since the per-item values for hats and books are higher for both of you. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:26,340][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:26,379][mllm.models.large_language_model_local][WARNING] - Response Given Alice's valuation for hats and balls, and your own valuation for balls, it's clear that balls are highly valued by both of you. To maximize your points, you should try to secure as many balls as possible. Here's a proposal that focuses on this: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:26,382][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:26,385][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:26,439][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:26,509][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:26,596][mllm.models.large_language_model_local][WARNING] - Response Given the values, the optimal strategy would be to aim for a Split that maximizes the points based on our high values for books and balls. Since Bob values hats the most and we value books and balls highly, proposing a split that takes most of the books and balls while giving Bob some hats might be beneficial. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:26,680][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:26,724][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:26,727][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:26,734][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:26,761][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:26,764][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:26,807][mllm.models.large_language_model_local][WARNING] - Response Given the current per-item values and the symmetry in the game, it's clear that both hats and books are highly valued by Bob, while balls are valued more by you. To maximize your points, you should propose to maximize your share of the books and balls, while taking into account the possibility that Bob might also propose for them. A strategic proposal would be to take a small portion of the high-value items to ensure you get some points from them. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:26,812][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:26,814][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:26,829][mllm.models.large_language_model_local][WARNING] - Response Given your per-item values and Bob's, it's clear that both sets of values complement each other. You value hats and books highly, while Bob values hats and balls highly. Proposing an equal split might still yield a good outcome, but you can try to slightly adjust the proposal to better align with your values. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:26,831][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:26,850][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:26,853][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:26,859][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:26,865][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:26,872][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:26,879][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:26,886][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:26,896][mllm.models.large_language_model_local][WARNING] - Response Given that both you and Bob have similar valuations for the items, and the previous rounds have shown that proposing to take all items results in an equal split, a strategic approach might be to slightly adjust the proposal to try and gain a small advantage. However, since the total item quantity equals the proposed quantity, any deviation might be met with a similar counter-proposal, leading to a proportional split. Proposal: 9 hats, 9 books, 9 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:26,920][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:26,926][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:26,933][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:26,939][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:26,946][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:26,948][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:26,955][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:26,972][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:26,974][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:27,033][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:27,036][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:27,154][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:27,157][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:27,161][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:27,164][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:27,176][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Bob have different valuations for the items, but both place high value on books and low value on balls. To maximize your points, you should propose a split that takes advantage of this imbalance. Since both of you value books highly, you should try to get as many books as possible while still proposing a fair share of hats and balls. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:27,214][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:27,217][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:27,257][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:27,260][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:27,295][mllm.models.large_language_model_local][WARNING] - Response Given the new values: - Your per-item values: hats=10, books=1, balls=10 - Alice's per-item values: hats=1, books=10, balls=10 Since the items are split equally and both agents have similar but different preferences, it seems advantageous to propose an allocation that considers both quantity and value. Here, we can try to maximize the value by offering a balanced proposal. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:27,299][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:27,306][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:27,348][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the values provided, both you and Alice will likely propose the same allocation each time, leading to a proportional split. To maximize points, you should aim to capture as much value as possible from the items you and Alice value highly. Since both you and Alice value hats and balls significantly, and books not as much, a good strategy is to propose a high allocation of hats and balls, and a low allocation of books. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:27,354][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:27,357][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:27,419][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Alice place a high value on balls, but you value hats and books less. To maximize your points, you should propose to take all the balls, and share hats and books based on their relative values. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:27,422][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:27,424][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:27,515][mllm.models.large_language_model_local][WARNING] - Response Given that Alice values books much higher than hats or balls, and you value hats and books more equally while valuing balls the least, a strategic approach would be to mislead Alice slightly by proposing a split that doesn't fully align with the true values but also keeps the ball distribution fair, since Alice values balls more than you do. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:27,518][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, Alice values hats and books more and balls less, while I value balls the most. To maximize my points, I should try to take as many balls as possible, while still proposing a fair share of hats and books. Here’s a strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:27,546][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:27,583][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:27,636][mllm.models.large_language_model_local][WARNING] - Response Given the values, you have a strong preference for hats and books, while Bob has a strong preference for books and balls. Proposing to take all items would maximize your points based on your per-item values. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:27,692][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both hats and books are highly valued by both players, while balls are valued less by both. Knowing Alice values books the most and hats and balls the least, and you value hats and books the most and balls the least, proposing an even split might be a suboptimal strategy as it allows Alice to capture more of the high-value items. A more strategic approach would be to aim for a distribution that maximizes your points given the item values. Since you have a higher value for hats and books, proposing a higher number of these items would be beneficial. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:27,700][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:27,706][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:27,736][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:27,739][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:27,741][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:27,755][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:27,788][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:27,792][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:27,871][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that balls are the most valuable for you, followed by hats and then books. Given Bob's values, he places more importance on hats and books, but not as much on balls. To maximize your points, you should try to secure as many of the balls as possible, since they are the most valuable for you. You could also compete with Bob for hats and books, but since he values books more than you do, you might need to secure as many of those as you can. A proposed strategy here is to take all of the balls, most of the hats, and a reasonable share of the books. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:28,175][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values where both you and Alice have significant value for different items, a more strategic approach might be needed. You value hats and books highly (10), while Alice values books and balls highly (10). Balls are valued less by both of you (1). A possible strategy is to split the items in a way that maximizes the value for both you and Alice, avoiding over-/underproposing any item. Given that: - You value hats and books highly and Alice also values books and balls highly. - If we propose to take all hats and books, we should balance the proposal to ensure the allocation is fair and both can get a good share. Proposal: 10 hats, 10 books, 0 balls This proposal gives you the maximum value for hats and books while ensuring Alice gets a good share of the balls, which neither of you value as much but which Alice values highly. So, the proposal is: Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:28,328][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it seems strategic to focus on items where we have a higher value. Since both of us value books and balls highly, and Alice values hats highly, it might be beneficial to propose a split that captures the majority of the high-value items. Proposal: 10 hats, 10 books, 10 balls This proposal evenly splits all items, which might not be optimal if we consider the per-item values. A better strategy might be to propose keeping more books and balls since they are highly valued by both of us. Proposal: 10 hats, 10 books, 10 balls However, to maximize points, we should propose keeping more of the items we value more. Therefore: Proposal: 10 hats, 10 books, 10 balls Given the symmetry and the high mutual values, this balanced proposal is likely optimal. Let's stick with: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:28,396][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:28,430][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:28,433][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:28,455][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the high values for hats and balls, and the moderate value for books, a strategic proposal would involve splitting the items in a way that maximizes the value based on the given per-item values. Since both Alice and I have high values for hats and balls, and moderate values for books, a good strategy would be to propose an allocation that reflects the value of the items. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:28,459][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:28,462][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:28,464][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:28,493][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both you and Alice prioritize balls the most, followed by hats, and then books least. In this round, proposing all items equally might not be the best strategy since balls and hats are more valuable to both of you. Here's a better approach: propose keeping more of the items you value more, which are balls and hats. Proposal: 10 hats, 10 books, 9 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:28,495][mllm.models.large_language_model_local][WARNING] - Response Given that the values are symmetric and both you and Bob value the items similarly, a competitive but cooperative strategy would be to propose an equal split to ensure both sides benefit equally. This prevents any one-sided exploitation and maximizes the points for both agents in each round. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:28,496][mllm.models.large_language_model_local][WARNING] - Response Given the high value of books and balls for both of us, and the equal distribution of values for hats, a fair proposal would allocate the items such that both of us benefit maximally. Here is a balanced proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:28,549][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:28,552][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:28,598][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:28,601][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:28,606][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:28,612][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:28,796][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Bob place high value on books and a lower value on hats and balls. In this scenario, a cooperative strategy might be effective to maximize the points. We can propose splitting the books and balls more evenly while taking all the hats, as you value hats less and Bob values them more. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:28,804][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:28,807][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:28,837][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:28,840][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:28,843][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:28,895][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Alice have a strong affinity for books and balls, while hats are valued less by both of you. To maximize points, it would be strategic to ensure that we both get as many books and balls as possible, while sharing the hats fairly since they are least valuable to both. Proposal: 5 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:28,901][mllm.models.large_language_model_local][WARNING] - Response Given the values, both you and Alice have high value for hats and Alice values books more than you do. To maximize your points, you should propose to take the hats and books, while ensuring the total items proposed do not exceed their available quantity. Here's a strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:28,905][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:28,907][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:28,934][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:28,937][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:28,940][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:29,007][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:29,010][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:29,014][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:29,046][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the high value of books and balls for both parties, a reasonable strategy is to propose an equal split of items to ensure that both parties receive a fair share, maximizing the likelihood of both achieving a high score. Here's my proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:29,049][mllm.models.large_language_model_local][WARNING] - Response Given the high value of books and balls for both parties, and the similar per-item values, it's important to ensure that we fully utilize the items to maximize points. Here’s a strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:29,051][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:29,054][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:29,056][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:29,059][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:29,130][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:29,153][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:29,156][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:29,209][mllm.models.large_language_model_local][WARNING] - Response Given the values, you have a significant advantage in valuing books (10) and a slight advantage in valuing hats (1 compared to Alice's 10 for balls), while Alice has a similar advantage in valuing balls. To maximize your points, you should propose to take all items that you value highly. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:29,213][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:29,215][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:29,218][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:29,231][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's crucial to propose a distribution that maximizes the points for both you and Bob. You value hats and books more than balls, while Bob values balls the most. Here's a strategic proposal: Proposal: 10 hats, 10 books, 10 balls This proposal ensures that both you and Bob get a share of all items, and we split everything proportionally as the items are equal in number. This should help both of you earn points based on your per-item values. However, considering the specific values, you might want to slightly adjust the proposal to prioritize distribution based on your higher values for hats and books. Here’s a refined proposal: Proposal: 10 hats, 10 books, 10 balls This should balance the distribution according to the values each of you has for the items. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:29,234][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:29,236][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:29,239][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:29,242][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:29,299][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it is clear that both you and Bob place high value on different items. You value books and balls highly, while Bob values hats and books highly. In this round, proposing to take all items might still be a viable strategy since the sum of your proposals will not exceed the total quantity, assuming Bob does the same. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:29,302][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:29,305][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:29,379][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:29,383][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:29,450][mllm.models.large_language_model_local][WARNING] - Response Given the values where I have a high value for hats and books, but a low value for balls, and Bob has the opposite scenario, proposing the full quantity for each item might lead to a proportional split that benefits us both but doesn't fully exploit our values. However, if we propose less for the items with lower value for Bob and more for the items we value highly, we might gain slightly more points. Proposal: 10 hats, 10 books, 9 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:29,456][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both you and Bob place higher value on hats and books compared to balls. A cooperative strategy where we propose splitting the items evenly will likely maximize our points, as we can ensure that both of us receive items that we value highly. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:29,463][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:29,503][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:29,506][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:29,509][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:29,511][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:29,515][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:29,565][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:29,569][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:29,646][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:29,987][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that we should prioritize grabbing as many of the items we value highly (books and balls) while still taking at least some hats to ensure a fair split in case the totals exceed the available items. Therefore, a balanced but prioritizing strategy would be: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:30,028][mllm.models.large_language_model_local][WARNING] - Response Given the strategic importance of maximizing value based on per-item values and the symmetric nature of the items in this round, it's crucial to allocate items in a way that maximizes the total points. Since both you and Bob value hats and books highly, but you value balls more than Bob does, a balanced proposal would be optimal. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:30,031][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Bob have high values for hats and books, but Bob values balls more than you do. To maximize points, you should try to capture as many of the high-value hats and books as possible while also securing a portion of the balls. Here's a strategic proposal: Proposal: 10 hats, 10 books, 5 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:30,125][mllm.models.large_language_model_local][WARNING] - Response Given the current values, hats are highly valued by both of us, and balls are the least valued. However, books are valued more by Alice than by me. To maximize my points, I should keep as many hats as possible, which I value highly at 10, and give Alice the books, which she values more. I will not take any balls since they are not valuable to me. Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:30,128][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric and high value of the items, proposing an equal share seems optimal. Here’s the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:30,261][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:30,349][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both hats and books have a higher value for Alice, while balls have a higher value for you. To maximize your points, you should propose to take as many balls as possible, while Alice is likely to take the hats and books. Here's the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:30,400][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it is clear that hats have a low value for both you and Bob, while books and balls have high values for you and Bob respectively. However, since we are competing for these items, it's strategic to claim as many items as possible to maximize our points. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:30,440][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the fact that both you and Alice value different items highly, a competitive but cooperative strategy might be most effective. Since both of you seem to value books and balls highly, it's important to secure as much of these items as possible while also taking into account the hats, which both of you value less. A possible strategy is to propose an allocation that ensures you get a significant portion of the valuable items (books and balls) while still taking into account the value of hats, which are less significant to both of you. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:30,570][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's strategically beneficial to allocate items in a way that maximizes the points based on our individual values. Since both of us have high values for books and balls, a cooperative approach might be beneficial to ensure we both get a fair share of the items we value highly. Proposal: 10 hats, 10 books, 10 balls This proposal ensures that we both get the full number of items, maximizing our points based on our individual values. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:30,623][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:30,626][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:30,684][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:30,687][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:30,778][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:30,781][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:30,784][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:30,786][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:30,789][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:30,792][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:30,862][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:30,865][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:30,867][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:30,944][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:30,947][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:31,026][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:31,033][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:31,036][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:31,038][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:31,041][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:31,188][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:31,272][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:31,275][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:31,278][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:31,432][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:31,516][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:31,519][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:31,522][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:31,525][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:31,528][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:31,530][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:31,595][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:31,598][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:31,601][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:31,604][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:31,607][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:31,675][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it is clear that both hats and books are highly valued by me and Bob, while balls are less valued. However, proposing to take everything might not always work if Bob also wants to maximize his points. Here, a strategic split could be beneficial, trying to get as many of the highly valued items as possible while leaving enough for Bob to accept my proposal. Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:31,682][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:31,685][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:31,836][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Alice place a high value on different items. You value balls the most (10), while Alice values hats the most (10). Books have a low value for both of you (1). To maximize your points, you should try to get as many balls as possible since they have the highest value for you. Here's a proposal that focuses on getting the most balls: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:31,840][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:31,842][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:31,845][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:31,847][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:31,917][mllm.models.large_language_model_local][WARNING] - Response Given that both you and Alice have similar but complementary valuations for the items, it's important to consider how to maximize your points by taking into account both the per-item values and the potential for proportional allocation when totals exceed the available items. Here’s a strategic approach: - Hats: Both you and Alice value hats equally at 10, so it's in your interest to maximize your share. - Books: You and Alice value books very differently. You value books at 1 (low), while Alice values them at 10 (high). You should keep this in mind and try to get as many as possible. - Balls: You and Alice value balls very differently. You value balls at 1, while Alice values them at 10. You should also aim to get as many balls as possible. Given the potential for proportional allocation, let's propose a split that maximizes your points while ensuring you get a significant share of the items with high personal value. ** Proposal: 10 hats, 10 books, 10 balls. ** This proposal ensures you maximize your share of the items you value highly (hats and balls) while also trying to get a fair share of books. If Alice counters with a similar or close proposal, the items will be split proportionally, but given your valuations, this should give you a good advantage. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:31,921][mllm.models.large_language_model_local][WARNING] - Response Given the values, it is important to consider that we both have a high value for hats and books, but Bob values balls highly. To maximize the points, we need to分配帽子、书本和球的分配，确保我们能够最大化自己的收益。 考虑到我们的价值和Bob的均值，可以采取以下策略：尽量保持与Bob的相比比例一致，同时确保自己在帽子和书籍上的收益最大化。由于我们和Bob在帽子和书籍上的值都非常高，尽量分配更多的帽子和书籍给我们的比例应该相匹配。 所以，我将提出以下分配方案： Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:31,927][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:31,930][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:32,002][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values of hats=1, books=1, and balls=10 for both you and Bob, it seems that both of us value the balls the most, followed by the books, and then the hats. To maximize our points, we should allocate the majority of items to the ones we and Bob value most, which are the balls. A strategic approach would be to propose taking all the balls and splitting the hats and books evenly. This way, we maximize the value from the items we highly value. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:32,009][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:32,012][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:32,015][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:32,180][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:32,183][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:32,339][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric values and the fact that both Alice and Bob have the same proposed strategy in previous rounds, leading to equal earnings, it suggests a balanced approach. However, the current per-item values show that books and balls are more valuable for both parties, while hats are less valuable but shared. A strategic proposal considering these values would be to prioritize books and balls while still claiming hats. Here's a proposal that maximizes value while considering potential proportional distribution: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:32,904][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:32,906][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:32,908][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:32,910][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:32,912][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:32,914][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:33,030][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both you and Alice have a high valuation for different items. You value hats and books highly, while Alice values balls highly. In this round, proposing to take all the items might not be optimal because if the total sum of proposals exceeds the item quantity, they will be allocated proportionally. Given that, a strategic approach would be to propose a split that maximizes your value while ensuring the total does not exceed the item quantity. Proposal: 10 hats, 10 books, 0 balls This proposal ensures that you potentially capture the high value items (hats and books) and avoids the risk of proportional allocation if Alice also proposes to take a large number of balls. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:33,053][mllm.models.large_language_model_local][WARNING] - Response Given the dynamic of the game, where both players have similar values for different items, it's important to consider the value each item holds for both Alice and Bob. Here, hats are valued the same by both, books are highly valued by both, and balls are also valued by both but less than hats and books. Based on past rounds, where splitting items equally tends to maximize points, and considering the high value items like books and hats, a balanced approach seems optimal. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:33,078][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and the symmetry in Bob's and my values for different items, it seems that proposing an equal split might not be optimal in this round since it results in a lower score due to the differing values each of us places on different items. A more strategic approach would be to allocate items in a way that maximizes the points for both of us. Based on our values, books and balls are more valuable to me and hats are more valuable to Bob. A reasonable proposal could be: Proposal: 10 hats, 10 books, 10 balls However, we could also consider a more balanced approach based on the values: - I value hats and books highly and Bob values hats and balls highly. - Allocating the items in a way that maximizes the benefit for both of us might involve giving more of the items I value more to myself and more of the items Bob values more to him. Let's try to propose a split that leverages these values while ensuring the total does not exceed the item quantity: Proposal: 10 hats, 10 books, 10 balls This direct proposal reflects the high value both of us place on hats and the fact that we have an equal distribution of books and balls to split. If this direct proposal does not seem to be optimal, we can consider a more nuanced split. For now, let's stick with the direct proposal. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:33,082][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:33,084][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:33,088][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:33,090][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:33,093][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:33,096][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:33,099][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:33,102][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:33,105][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:33,108][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:33,111][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:33,114][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:33,144][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:33,149][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:33,151][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:33,164][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric nature of the per-item values and the fact that both you and Alice value hats and balls similarly but value books differently, it might be beneficial to propose a split that takes advantage of these differences. However, since both of us have the same total value for the items, proposing the entire set could still lead to a fair split. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:33,169][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:33,171][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:33,207][mllm.models.large_language_model_local][WARNING] - Response Given the values, both you and Alice place a high value on balls and a low value on hats and books. Proposing to take all the items maximizes the potential points, but if Alice does the same, the items will be split proportionally. To ensure we get the maximum possible points, we can propose to take all items, which will likely result in an equal split if Alice does the same. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:33,231][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:33,234][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:33,252][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:33,274][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:33,277][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:33,322][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:33,325][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:33,344][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the situation and the values, it's best to propose an equal distribution to maximize the chance of proportional allocation if the totals exceed the item quantity. Here’s the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:33,347][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:33,350][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:33,353][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:33,366][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:33,369][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:33,372][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:33,375][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:33,378][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:33,381][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:33,391][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:33,394][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:33,397][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:33,399][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:33,412][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:33,415][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:33,422][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:33,428][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:33,431][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:33,434][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:33,441][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:33,444][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:33,475][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:33,511][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:33,515][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:33,517][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:33,533][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:33,540][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:33,546][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:33,553][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:33,559][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:33,565][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:33,572][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:33,575][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:33,577][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:33,592][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both hats and books have a much higher value for Bob compared to balls, which have a much higher value for you. To maximize your points, you should propose to take the balls while letting Bob have the hats and books. Here's the proposal: Proposal: 0 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:33,594][mllm.models.large_language_model_local][WARNING] - Response Given the values, the most strategic approach is to recognize that both you and Bob value books and balls highly, while your preference for hats is minimal. To maximize points, it makes sense to ensure that you secure as many books and balls as possible while dividing hats proportionally. Proposal: 5 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:33,599][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:33,601][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:33,604][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:33,605][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:33,607][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:33,610][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:33,612][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:33,614][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:33,616][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:33,619][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:33,633][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:33,635][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:33,637][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:33,650][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:33,692][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:33,694][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:33,711][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both you and Alice place the highest value on balls. Since the分配方式是公平的，为了最大化自己的得分，可以考虑这样的策略：尽可能多地获取自己价值更高的物品。在这种情况下，你可以提议尽可能多地获取书籍和球类，而较少获取帽子，因为它们的价值对你来说较低，但Alice对帽子的价值较高。 为此，建议的分配如下： Proposal: 10 hats, 10 books, 10 balls 这样可以确保你获取大部分书籍和球类，因为这些对你来说更有价值。由于Alice对帽子的价值更高，她可能会更多地获取帽子，但你也能够获得部分帽子。这样的分配可以最大化你的总得分。 did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:33,718][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and Alice's strategic response, it seems she is trying to maximize her points by getting more of the items she values highly. To counter this, we should propose a distribution that maximizes our points while also making it less advantageous for Alice to take all of the highly valued items. Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:33,731][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:33,734][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:33,752][mllm.models.large_language_model_local][WARNING] - Response Given the current values, it's clear that both you and Alice value balls highly but differently. You value balls at 10 and Alice values them at 1. To maximize your points, it's best to propose taking all the balls while splitting the other items proportionally. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:33,755][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:33,758][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:33,761][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:33,774][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:33,777][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:33,780][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:33,783][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:33,796][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both you and Alice place higher value on hats and books, but the balls are seen as less valuable. However, since the values are high, we should aim to maximize our share in hats and books, while still considering the allocation of balls. Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:33,798][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:33,801][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:33,804][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:33,807][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:33,809][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:33,812][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:33,842][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:33,849][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:33,855][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:33,862][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:33,877][mllm.models.large_language_model_local][WARNING] - Response Given the values, it appears that both you and Alice have very different preferences, but the items are valued the same in quantity. To maximize your points, you should propose the items in a way that aligns with your higher value for hats and balls, and her higher value for books. Here's a strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:33,880][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:33,989][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both hats and books are more valuable to you than to Bob, while balls are more valuable to Bob than to you. The optimal strategy would be to propose to take all items that you value highly to maximize your points, while leaving the less valuable items to Bob. Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:33,992][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:33,995][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:34,092][mllm.models.large_language_model_local][WARNING] - Response Given the values where both you and Bob have high preference for different items, it's important to consider how to split the items to maximize your own points. Here, you value hats and balls highly but Bob values books and balls highly. A strategic split might involve proposing to take all the items you value highly while allowing Bob to take the books and a portion of the balls. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:34,095][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:34,101][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:34,107][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:34,141][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:34,143][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:34,145][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:34,209][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:34,215][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:34,217][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:34,223][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:34,229][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:34,231][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:34,234][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:34,239][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:34,288][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the fact that we have the same per-item values as Alice for the items "balls" but different values for "hats" and "books", a cooperative strategy might be optimal to maximize points. However, since the values are significantly different, we should consider proposing to take all items to maximize our points from the "balls" which have the highest value for both of us. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:34,292][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:34,369][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:34,376][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:34,451][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:34,533][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:34,539][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:34,613][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the values and the previous outcomes, it's clear that both you and Alice are trying to maximize your points by keeping items that you value highly. In this round, you both might benefit from a cooperative or mixed strategy to ensure that you each get the items you value highly without overproposing and causing fractional allocation. Proposal: 10 hats, 10 books, 10 balls However, to slightly optimize and avoid the risk of the total sum exceeding the available items, we can slightly modify our proposal to ensure proportionality and fairness: Proposal: 10 hats, 10 books, 9 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:34,616][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:34,623][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:34,630][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:34,696][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Bob place high value on books and balls, while both of you value hats less. Since you propose equally and both end up with equal shares, a strategic proposal would be to maximize your share of the items you value more. Here, you should try to get as many books and balls as possible, while allowing enough hats for Bob to also get some. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:34,699][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:34,705][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:34,712][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:34,781][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:34,788][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:34,795][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:34,845][mllm.models.large_language_model_local][WARNING] - Response Given the dynamic nature of the per-item values and to maximize the points, it's important to propose a distribution that reflects the value each of us places on the items. Since you value hats very low and Bob values them high, and you both value books and balls highly, a strategic proposal would be to propose an allocation that aligns with these values. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:34,848][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:34,854][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:34,866][mllm.models.large_language_model_local][WARNING] - Response Given the values, the optimal strategy is to propose an allocation that maximizes the points based on the values you and Bob have for each item. Since you value hats (10) and books (10) more than balls (1), and Bob values balls (10) more than hats (10) and books (1), it's best to propose an equal split of each item to avoid any proportional distribution that might favor Bob for items you value less. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:34,893][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:34,900][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:34,929][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:34,931][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:34,937][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:35,128][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the fact that both Alice and I have similar per-item values for hats and balls but different for books, a strategic approach would be to split the books to ensure both of us get some value from them, while maximizing the items where we both have high values. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:35,135][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:35,138][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:35,141][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:35,155][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:35,158][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:35,218][mllm.models.large_language_model_local][WARNING] - Response Given the current per-item values, the optimal strategy is to propose the allocation that maximizes the product of the quantity received and the per-item value for each item. Your per-item values for hats, books, and balls are 10, 10, and 1 respectively. Alice's per-item values for hats, books, and balls are 1, 10, and 10. Proposing to take all items ensures that you get the full value from each item type, as Alice's values are lower for the items you value highly. Here's the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:35,291][mllm.models.large_language_model_local][WARNING] - Response Given the values and Bob's likely strategy to maximize his points by taking books and balls, it's important to propose a balanced distribution to ensure I receive a fair share of high-value items. Here is a strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:35,314][mllm.models.large_language_model_local][WARNING] - Response Given that books and hats have significantly different values for you and Bob, a strategic approach would be to propose a split that maximizes the value while considering Bob's likely response. Since both of you have the same per-item values for balls, these can be divided equally without much strategic consideration. A good strategy would be to propose keeping all the books and the maximum number of hats possible, while splitting the balls. Here's a potential proposal: Proposal: 10 hats, 10 books, 5 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:35,395][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:35,432][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:35,582][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:35,653][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:35,656][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:35,659][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:35,731][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:35,770][mllm.models.large_language_model_local][WARNING] - Response Given that both our per-item values are similar for books and balls, and we both value hats highly, proposing to take all items will likely result in a proportional distribution if Bob also takes all items. This approach maximizes our points by ensuring we get as close to the full value of each item as possible. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:35,774][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:35,776][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:35,779][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:35,796][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:35,799][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:35,822][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:35,825][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:35,828][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:35,830][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:35,844][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Bob have a high valuation for hats and books, while balls are valued less. To maximize points, one strategic approach is to ensure that both agents receive items they value highly, as this will lead to a higher total valuation for each item. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:35,848][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:35,851][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:35,869][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both you and Bob value hats and books similarly but balls are less valuable for both of you. However, since both of you have the same per-item values for hats and books, and balls are of low value, proposing to take all items should still be a reasonable strategy. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:35,901][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:35,908][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:35,926][mllm.models.large_language_model_local][WARNING] - Response Given the values, proposing an equal split seems optimal to maximize points considering both you and Alice value hats and balls similarly, but books are valued more by Alice. Thus, a balanced approach might be: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:35,929][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:35,932][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:35,935][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:35,948][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:35,950][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:35,970][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:35,973][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:35,976][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:35,992][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:35,995][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:35,998][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:36,000][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:36,003][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:36,006][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:36,008][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:36,011][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:36,019][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:36,022][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:36,025][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:36,027][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:36,045][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:36,066][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Bob place high value on the same items. To maximize your points, it's important to coordinate our proposals to ensure we both receive a fair share of the items we value. Here, we can propose splitting the items in a way that both of us value the items equally. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:36,069][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:36,075][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:36,088][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both you and Alice have very different preferences for the items. You value hats and books highly, while Alice values balls highly. To maximize your points, you should propose to take all the items that you value highly. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:36,111][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:36,114][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:36,117][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:36,151][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:36,154][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:36,172][mllm.models.large_language_model_local][WARNING] - Response Given that my per-item value for balls is 10 and Alice's per-item value is only 1, and vice versa, proposing the maximum number of balls while splitting the other items equally seems like a good strategy. Here is my proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:36,191][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's important to remember that I value balls the most (10), followed by hats and books (both 1). Alice values hats the most, followed by balls and books. Since we both propose to take all items, we end up splitting them equally due to the rules of the game. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:36,194][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:36,197][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:36,203][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:36,210][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:36,220][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:36,223][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:36,225][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:36,231][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:36,241][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:36,244][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:36,246][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:36,248][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:36,260][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:36,266][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:36,269][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:36,282][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values where both you and Alice value hats at 1, books at 1, and balls at 10, the optimal strategy would be to propose an equal split to ensure neither of you gets less than the proportional share. Here’s the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:36,285][mllm.models.large_language_model_local][WARNING] - Response Given Alice's high value for hats and balls, and my own high value for balls, we might need to be strategic to maximize our points. Here's a proposal that balances my high value for balls with Alice's high value for hats: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:36,290][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:36,302][mllm.models.large_language_model_local][WARNING] - Response Given the current values and knowing that Alice values balls significantly more than you do, and you value hats and books more than she does, you should propose to take the items where you have higher value to ensure you get more points. Here's a strategic proposal: Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:36,305][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:36,325][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:36,331][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:36,333][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:36,367][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:36,370][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:36,441][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:36,447][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:36,450][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:36,478][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:36,485][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:36,488][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:36,490][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:36,528][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:36,531][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:36,534][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:36,536][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:36,539][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:36,607][mllm.models.large_language_model_local][WARNING] - Response Given the high value of hats and books for both of us and the low value of balls, it's important to secure as many hats and books as possible while leaving some balls for Alice. Let's allocate the items as follows: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:36,610][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:36,689][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:36,848][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:36,855][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:36,862][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:36,873][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both "hats" and "balls" are very valuable to you, while "books" is valuable to Bob. To maximize your points, you should propose to take all the items that you value highly and offer just enough of the less valuable item to Bob to get a fair split. Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:36,952][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:36,955][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:37,117][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that books are highly valued by both of us, while hats and balls are less valued. Since our proposals in the last round were the same and resulted in a proportional division, let's try a different approach this time to maximize our points. Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:37,120][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:37,127][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:37,181][mllm.models.large_language_model_local][WARNING] - Response Given the values, you have a strong preference for balls (10 points each) and Alice has a strong preference for books (10 points each). To maximize your points, you should try to get as many balls as possible while ensuring the total quantity分配不超过10。因此，一个合理的策略是在保证所有物品都被分配完的同时，尽量多拿到价值高的物品。 Proposal: 10 hats, 10 books, 10 balls 这个策略确保你可以最大化获取价值10的物品（即balls），同时让总分配正好为10，避免任何物品被不公平地分配。 did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:37,184][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both you and Alice have a strong preference for balls. However, you value hats and books more than Alice does. To maximize your points, you should propose to take all the balls since you value them the most, and then split the hats and books proportionally. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:37,187][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:37,191][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:37,204][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the history of the game, it seems that proposing to take all items is互利的策略。这样可以最大化利用每种物品对自己和对方的价值。因此，我会继续保持这种策略。 Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:37,211][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:37,217][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:37,224][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:37,236][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:37,243][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:37,262][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:37,267][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:37,276][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:37,282][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:37,677][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it is clear that both you and Alice have a strong preference for balls. However, you value hats and books less than Alice does. A cooperative strategy might yield better results, as it can help avoid the scenario where both overstate their claims on balls, leading to a proportionatesplit where you only receive a fraction of their value. Proposal: 10 hats, 10 books, 9 balls This proposal aims to: 1. Fully claim all hats, which are valuable to you. 2. Fully claim all books, which are also valuable to you. 3. Claim 9 balls, which maximizes your total points without overstepping and risking a proportionate split. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:37,796][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:37,799][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:37,806][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:37,813][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:37,920][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's strategically beneficial to propose a distribution that maximizes the point potential based on our per-item values. Since hats and balls are highly valuable for me, and books are valuable for Bob, proposing an even split might not be optimal. A better approach is to propose taking all the items that are highly valuable for me to maximize the points. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:37,923][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:37,926][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:37,932][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:38,018][mllm.models.large_language_model_local][WARNING] - Response Given the high value of hats and books for both you and Bob, and the low value of balls, a strategic proposal would be to maximize the high-value items while ensuring that both you and Bob do not exceed the total quantity of items. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:38,042][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:38,045][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:38,069][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the equal per-item values, it would be rational to propose a split that reflects the value each agent places on the items. Since both agents value the same items differently, proposing an equal split might not be optimal. Here, we can propose to take the items that have higher value for us and give away the ones that have higher value for Alice. Proposal: 10 hats, 10 balls, 10 books This proposal takes into account the higher values we place on hats and books and acknowledges the higher value Alice places on balls. If Alice agrees, we will both receive our full proposed amounts. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:38,073][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:38,079][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:38,082][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:38,084][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:38,111][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Alice place a higher value on hats and balls compared to books. You value hats and balls at 10 and 1 respectively, while Alice values hats and books at 1 and 10 respectively. This suggests that splitting the items evenly might not be optimal, as both of you have similar but inverted preferences for hats and books. A strategic approach might involve proposing a split that maximizes your points based on these preferences. Since both of you value hats highly and balls slightly, you should propose to take more of the items you value more. Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:38,114][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:38,117][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:38,148][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both you and Alice value hats and books more than balls, but you and Alice have different preferences. You value hats and books the same, while Alice values books the same as hats and you value balls the least. To maximize your points, you should propose to take all the items that you value higher, ensuring that you get as much value as possible. Here’s a strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:38,155][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric nature of the per-item values and the fact that both you and Alice are likely to submit the same proposal, we need to consider the optimal strategy to maximize our points. Since both you and Alice have similar value distributions, the optimal strategy would be to recognize that splitting the items equally will result in each of you earning the same amount. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:38,161][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it is beneficial to propose a distribution that reflects the higher value you place on hats and books. Here's a strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:38,164][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:38,167][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:38,178][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:38,181][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:38,201][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Bob highly value hats and books, while balls are less valued. However, the distribution of these values suggests that neither of you is the only one who values hats and books highly. To maximize your points, you should propose to take as many high-value items (hats and books) as possible, while still allowing for a fair split on balls, as this will ensure you get a substantial portion of the items you value highly. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:38,204][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:38,207][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:38,210][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:38,223][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric and highly valuable nature of the items for both of us, it's optimal to propose equally splitting the items to ensure we both get a fair share of the valuable balls and minimize the risk of losing any of the other items. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:38,246][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and the symmetry in the game, a balanced proposal would be optimal. Here, you have a high value for hats and books, while Bob values books and balls highly. A fair and strategic proposal would be to mirror Bob's values slightly to ensure both agents maximize their points. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:38,249][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:38,252][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:38,266][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:38,270][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:38,272][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:38,340][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:38,343][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:38,346][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:38,377][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:38,409][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's important to recognize the asymmetry in the per-item values between you and Alice. You value hats and books significantly more than balls, while Alice values balls more than the other items. However, since the allocation is proportional, proposing equal shares can still lead to a fair split. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:38,461][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:38,465][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:38,467][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:38,479][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:38,482][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:38,484][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:38,488][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:38,495][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:38,501][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:38,504][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:38,507][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:38,522][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:38,524][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:38,529][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:38,543][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:38,546][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:38,548][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:38,551][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:38,586][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:38,592][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:38,594][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:38,597][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:38,615][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:38,631][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:38,634][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:38,637][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:38,673][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:38,677][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:38,679][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:38,756][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:38,759][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:38,762][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:38,774][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Bob place high value on books and low value on hats and balls. Proposing to take all items ensures that you capture the full value of the books and hats that you highly value, while Bob will do the same for the balls he values. Here is the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:38,794][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:38,813][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both you and Bob value hats and books relatively low, while both value balls very highly. To maximize points, it's beneficial to propose keeping as many balls as possible while also considering the value of hats and books. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:38,820][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:38,832][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:38,835][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:38,838][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:38,870][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:38,877][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:38,884][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:38,892][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, the optimal strategy would be to recognize that you and Alice have complementary valuations for the items. You value hats and books highly, while Alice values balls highly. Therefore, it makes sense to propose an allocation that maximizes the shared benefit. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:38,898][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:38,900][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:38,903][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:38,915][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:38,921][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:38,924][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:38,939][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:38,942][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:38,947][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:38,963][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:38,969][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:38,974][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:38,980][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:38,985][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:38,987][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:39,008][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:39,013][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:39,018][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:39,034][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:39,036][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:39,052][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:39,057][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:39,132][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:39,138][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:39,145][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:39,464][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:39,471][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:39,477][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:39,513][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values where I value hats and balls more and Alice values books more, I should propose to take more of the items I value higher to maximize my points. Here, I can take all the balls since they have the highest value for me. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:40,213][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:40,300][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:40,381][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:40,384][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:40,386][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:40,463][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric nature of the per-item values and the proposals from previous rounds, it seems that both Alice and I value hats and books similarly and balls less. To maximize our points, it makes sense to propose an allocation that reflects these values while ensuring the total quantity does not exceed the available items. A reasonable proposal would be to propose an equal split that maximizes the value for the items we both value highly. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:40,519][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:40,526][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:40,532][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:40,539][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:40,551][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:40,557][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:40,568][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:40,571][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:40,574][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:40,596][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:40,598][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:40,742][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Bob have high values for different items. You value hats and books highly, while Bob values hats and balls highly. Since the allocations are proportional, proposing to take all items might lead to an equal split, which is optimal in this case because of the symmetry in the setup. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:40,762][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both of us place high value on hats and books, and lower value on balls. If we both propose to take all the available items, we will split them proportionally, which might not maximize our points since we will likely end up with only a portion of the high-value items. To maximize our points, let's propose to take all the items and hope that Alice does the same, thereby allocating the items entirely to us. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:40,781][mllm.models.large_language_model_local][WARNING] - Response Given the values, the most strategic proposal would be to allocate items based on their perceived value to both Alice and Bob. Since Alice values books the highest at 10 and Bob values books and hats moderately, a balanced but strategic approach is needed. Here’s a refined proposal: proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:40,801][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and the symmetry in the game, it's optimal to propose a split that closely matches the other player's valuation to ensure a fair and mutually beneficial outcome. Here, we have: - Hats: you value 1, Bob values 10 - Books: you value 10, Bob values 1 - Balls: you value 1, Bob values 10 Proposing a split that reflects a balanced approach based on the values would be: - 10 hats (Bob gets all) - 10 books (you get all) - 10 balls (Bob gets all) Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:40,894][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it is clear that both hats and books have higher value for one player and balls for the other. To maximize points, it's strategically wise to propose splitting the items in a way that minimizes proportional allocation issues while maximizing individual value. Since hats and books are highly valued by you and balls are highly valued by Bob, a plausible proposal would be to maximize the items that have higher value for you while ensuring you get a fair share of the balls that Bob values highly. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:40,898][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both hats and books have a higher value for you (10 and 1 respectively) compared to Bob, while balls are more valuable to Bob (10 compared to 1 for you). To maximize your points, it makes sense to propose keeping all items to capture the higher value that you have assigned to them. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:40,936][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:40,940][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:40,947][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:40,950][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:40,952][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:40,959][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:40,990][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:41,090][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it is clear that both you and Alice have complementary preferences. You value hats and books highly, while Alice values balls highly. In previous rounds where the items and values were similar, proposing to take all items led to fair splits since both valued the items equally. Given this round's values: - You: hats=1, books=10, balls=10 - Alice: hats=10, books=1, balls=10 To maximize points, it would be strategic to propose taking all items, as this aligns with both of your preferences and ensures you receive the items with the highest value to you. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:41,129][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:41,132][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:41,135][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:41,141][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:41,144][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:41,147][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:41,178][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:41,181][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:41,187][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:41,194][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:41,198][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:41,214][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:41,217][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:41,228][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Bob value books the most, followed by hats, and balls the least. In this round, proposing a fair split might not yield the maximum benefit since the values are significantly different. A strategic approach would be to propose a split that maximizes your points based on your higher value for books. Proposal: 10 hats, 10 books, 10 balls However, since books are highly valued by both of us, we might consider proposing a slightly adjusted split to take advantage of the higher value of books. Proposal: 10 hats, 10 books, 9 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:41,324][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:41,331][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:41,333][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:41,502][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:41,505][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:41,520][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:41,527][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:41,570][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:41,573][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:41,576][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:41,579][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:41,747][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:41,757][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:41,760][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:41,774][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:41,781][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:41,783][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:41,786][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:41,793][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:41,812][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:41,816][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:41,819][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:41,822][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:41,825][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:41,840][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both hats and books are highly valuable to me, while balls are also valuable but to a lesser extent. To maximize points, I should ensure I get a significant portion of both hats and books, and a balanced amount of balls. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:41,846][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:41,887][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:41,888][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:41,905][mllm.models.large_language_model_local][WARNING] - Response Given the values, the optimal strategy would be to propose a distribution that maximizes the net value for both you and Bob, as any allocation that exceeds the total number of items will be proportionally reduced. Since both of your per-item values are high for hats and books, while Bob values balls highly, the most balanced and strategic proposal would be to mirror Bob's values to ensure a fair and potentially mutually beneficial outcome. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:42,015][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:42,016][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:42,018][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:42,020][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:42,061][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:42,076][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:42,077][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:42,110][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:42,116][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:42,121][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:42,128][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:42,132][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:42,137][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:42,147][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's beneficial to propose a split that reflects the high value items. Here, both you and Bob value books and balls highly, but Bob particularly values hats. A strategic proposal might involve taking more of the items you value highly while respecting Bob's values. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:42,149][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:42,151][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:42,156][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:42,166][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Bob will benefit the most by keeping the items that align with their high values and sharing the items with lower values equally. Here’s a strategic proposal: - You value hats and books highly (10 each), and Bob values balls highly (10). - If you both propose to keep the full set of 10 items, it will be split proportionally since the total proposals will exceed the available items and will be split equally. To maximize your points while accounting for the proportional split: Proposal: 10 hats, 10 books, 10 balls This ensures you get the full value of hats and books, and since balls are valued by Bob more than you, you’ll share them equally. Given that the items will be split proportionally, this is a balanced and strategic move. If a proportional split seems unfavorable, you can slightly adjust by keeping more of the moderately valued items and splitting the rest, but the above proposal seems optimal given the equal valuation scenario. So, the final proposal is: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:42,172][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:42,200][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric nature of the per-item values and the fact that both you and Alice value hats and balls quite highly, while valuing books less, it makes strategic sense to propose a split that maximizes the value for both hats and balls since they are highly valued by both parties. Here's a proposed allocation: Proposal: 10 hats, 10 books, 10 balls This proposal aims to allocate all items between the two of us, which should lead to a fair distribution that maximizes our points based on our high values for hats and balls. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:42,202][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:42,205][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:42,216][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:42,219][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:42,221][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:42,284][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Bob have a strong preference for different items. You value hats and balls highly (10) while Bob values books and balls highly (10). To maximize your points, you should aim to get as many hats and balls as possible, while allowing Bob to take the books. To balance the proposal and ensure that the total quantity is respected, a good strategy would be to propose to take the items that you value the most while allowing Bob to take the high-value items he prefers. Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:42,298][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:42,302][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:42,315][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both hats and balls have higher values for you (10 for hats and 10 for balls) compared to books (1 for books). Meanwhile, Bob values books the most (10) and hats and balls the least (1 each). A strategic approach would be to propose to take all items that you value highly, which are the hats and balls, and split the books in a way that also ensures a balanced proposal. Since the points are split proportionally if the total proposed exceeds the available items, and since the values are very different, a balanced proposal might lead to a fairer distribution of points. Proposal: 10 hats, 10 balls, 10 books This proposal maximizes the value of items you highly prefer while ensuring that Bob also gets some of the books, avoiding any proportionality issues since the values are balanced. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:42,319][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:42,322][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:42,326][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:42,329][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:42,370][mllm.models.large_language_model_local][WARNING] - Response Given the pattern in the games, it seems that splitting the items equally is the optimal strategy, as both you and Bob value different items differently but always propose to take all items. To maximize your points, you should ensure you get items that align well with your per-item values. Proposal: 10 hats, 10 books, 10 balls However, since the optimal strategy in previous rounds was to propose taking all items, you might want to consider a strategy that balances taking items you value highly with the risk of Bob taking items he values highly. Given your values for hats, books, and balls, a balanced proposal might still be best. Proposal: 10 hats, 10 books, 10 balls Since both you and Bob propose taking all items, and the items are split proportionally if the sum exceeds the total items, a more strategic proposal might be to slightly adjust to ensure you get more valuable items for yourself. Proposal: 10 hats, 10 books, 5 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:42,377][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:42,387][mllm.models.large_language_model_local][WARNING] - Response Given the differing but symmetric per-item values between you and Bob, proposing a balanced distribution is a reasonable strategy. Here’s a proposal that maximizes the utility based on the values: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:42,390][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:42,419][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the proposed strategy in previous rounds, it seems that proposing to take the entire items provides a balanced and effective strategy. Here is my proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:42,435][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the situation where both you and Alice value the same items in similar ways, but with a potential to gain more by cooperating or misallocating in a way that maximizes your specific values, a strategic approach would be to propose an allocation that aligns with your higher values for certain items. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:42,439][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:42,460][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:42,462][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:42,467][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:42,595][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:42,600][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:42,638][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:42,693][mllm.models.large_language_model_local][WARNING] - Response Given the values, it appears that both you and Alice have a similar valuation for books and a different valuation for hats and balls. To maximize your points, you should propose a split that takes advantage of your higher valuation for hats and balls compared to Alice. Here's a proposal that could help: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:42,725][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:42,729][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:42,743][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and the symmetry in the items and values, it seems optimal to propose a split that reflects the value each agent places on the items. Here’s a proposal that tries to maximize value: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:42,747][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:42,754][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:42,760][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:42,766][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:42,782][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:42,790][mllm.models.large_language_model_local][WARNING] - Response Given the values, both you and Bob have a high valuation for different items. You value hats and books highly while valuing balls less, whereas Bob values balls highly. Given this, a cooperative approach might yield better results. Here’s a proposal that splits the items as evenly as possible: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:42,835][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:42,852][mllm.models.large_language_model_local][WARNING] - Response Given the values, you have a strong preference for hats and balls, while Alice values books highly. To maximize your points, you should try to allocate as many hats and balls as possible to yourself, assuming Alice will similarly try to keep books. Here is a revised proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:42,920][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:42,937][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:42,957][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:42,974][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:42,977][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:42,981][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:43,104][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:43,180][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both you and Bob value hats and books very differently. You value hats and books much more than balls, while Bob values hats and balls more. To maximize your points, you should propose keeping as many books and hats as possible since you value them more highly. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:43,195][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:43,199][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:43,242][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:43,270][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:43,273][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:43,274][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:43,285][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:43,286][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:43,318][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, I should strategically propose to maximize my points. Since I value hats and balls highly (10), and books less (1), while Alice values books highly (10) and hats and balls less (1), we both have similar but inverted preferences. To maximize my points, I should propose to keep as many balls and hats as possible, and only a few books. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:43,319][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:43,337][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:43,340][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:43,352][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's advantageous to maximize the allocation of items you value highly (hats and books) while minimizing the allocation of items you value less (balls). Since both of us have symmetric but opposite preferences for balls, a strategic proposal would be to claim all items you value highly. Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:43,413][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:43,416][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:43,418][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:43,420][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:43,527][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:43,530][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:43,543][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both hats and balls have high value for one of the players but are less valuable to the other. To maximize points, we should try to claim the items that we value highly while also attempting to negotiated a fair split for the middle-value item. Here's a strategic proposal: Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:43,577][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:43,593][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:43,625][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:43,628][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:43,639][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the high value of hats and balls for both of us, and the moderate value of books, I will propose an allocation that reflects the high value of hats and balls while also considering the books. Here is my proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:43,646][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:43,649][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:43,695][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's important to maximize the points based on the per-item values. You value hats and balls more and books less, while Alice values books and balls more and hats less. To maximize your points, you should propose to take more hats and balls, and fewer books compared to the items' total quantity, while still considering Alice's preferences. Proposal: 10 hats, 10 books, 10 balls Since the per-item values are significant and the items are indivisible, this proposal balances valuing your preferred items while acknowledging that Alice values books and balls highly. However, since the values of the items are equal and the items are indivisible, there's no inherent reason to split them differently based on the valuations alone. We can still try to maximize our points by taking more of the items we value highly. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:43,702][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:43,709][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:43,737][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:43,740][mllm.models.large_language_model_local][WARNING] - Response Proposal: 5 hats, 5 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:43,755][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:43,758][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:43,760][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:43,774][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:43,776][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:43,779][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:43,811][mllm.models.large_language_model_local][WARNING] - Response Given the values, Alice values hats and balls more than books, while you value books more than hats and balls. In this round, you should aim to propose a distribution that maximizes your value while considering Alice's preferences. Proposal: 10 hats, 10 books, 10 balls However, since Alice values hats and balls more than books, she might propose to take more of those items. To optimize your points, you should propose a mix that keeps a balance but also ensures you get a good amount of books, which you value second highest. Proposal: 10 hats, 10 books, 10 balls This proposal keeps the distribution even, which might lead to a proportional split if the total is not less than the item quantity. Given the values, this seems like a balanced approach to maximize your points. Proposal: 5 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:43,870][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:43,874][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:43,885][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:43,888][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:43,900][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:43,915][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:43,930][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric nature of the values and the previous rounds where both you and Alice have benefited from proposing the full number of items, it's reasonable to assume that both of you are likely to propose the full allocation again. However, to ensure a fair split and to maximize points, let's propose as follows: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:43,933][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:43,962][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:43,969][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:43,978][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:43,984][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:44,016][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:44,019][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:44,035][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:44,052][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:44,058][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:44,092][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:44,095][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:44,099][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:44,102][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:44,110][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:44,116][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:44,122][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:44,131][mllm.models.large_language_model_local][WARNING] - Response Given the significant disparity in value for books and balls between you and Alice, it's crucial to propose an allocation that maximizes your points while considering the proportional distribution if the sum of proposed amounts exceeds the available items. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:44,194][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:44,201][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:44,210][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:44,216][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:44,227][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:44,243][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:44,318][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both balls and books are highly valued by Bob, while hats have low value for both of us. To maximize my points, I should focus on securing the items that Bob values more, namely books and balls. Here's a strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:44,357][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:44,360][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:44,394][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:44,397][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:44,401][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:44,416][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:44,430][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:44,436][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:44,468][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:44,640][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Alice place a high value on hats and books, while balls are valued less. To maximize your points, you should try to secure as many hats and books as possible since they have higher values for you. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:44,645][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:44,673][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:44,694][mllm.models.large_language_model_local][WARNING] - Response Given the values, you have a strong preference for balls (10 points each) and Bob has a strong preference for balls (10 points each). Both hats and books have a value of 1 point each. To maximize your points, you should allocate as many balls as possible to yourself, while ensuring the total allocation does not exceed the available quantity. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:44,757][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:44,773][mllm.models.large_language_model_local][WARNING] - Response Given that both Alice and I have symmetric values for hats and balls but different values for books, a coordinated strategy might be beneficial. To maximize points, we should propose splitting the books more fairly, while keeping the hats and balls as we both value them equally. Proposal: 10 hats, 10 books, 10 balls This keeps both hats and balls fairly shared, addressing the imbalance in value for books. Alice will likely propose a similar distribution, leading to an equal split as in previous rounds. Let's submit this proposal. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:44,781][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:44,785][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:44,788][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:44,790][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:44,814][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:44,817][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:44,832][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:44,834][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:44,902][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:44,905][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:44,909][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:44,911][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:44,915][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:44,917][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:44,986][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:45,001][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:45,016][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:45,018][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:45,023][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:45,079][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:45,110][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:45,125][mllm.models.large_language_model_local][WARNING] - Response Given the values, both you and Alice have strong preferences for books and balls, respectively. To maximize your points, it's important to secure as many books and balls as possible, while still accounting for hats. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:45,131][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:45,138][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:45,145][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:45,151][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:45,154][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:45,202][mllm.models.large_language_model_local][WARNING] - Response Given the values, proposing equally might not be the best strategy since Alice values balls much more highly than you do. To maximize your points, you should try to hog as many of the balls as possible while splitting the other items more equally. Here is a more strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:45,217][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:45,220][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:45,223][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:45,225][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:45,234][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:45,237][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:45,280][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and the symmetric nature of the problem, where Alice values books very highly and hats and balls low, while you value hats and books highly and balls low, a strategic proposal would be to offer Alice the items she values more and keep the ones you value more. Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:45,297][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:45,314][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:45,329][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:45,333][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:45,336][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:45,346][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:45,349][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:45,362][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:45,377][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the fact that proposing to take all items is the optimal strategy in this situation, I will submit the same proposal as before: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:45,395][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:45,432][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:45,447][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:45,450][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:45,463][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:45,470][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:45,482][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that hats and balls are highly valued by me but not by Alice, while books are valued the same by both of us. To maximize my points, I should propose to take all the hats and balls myself, and split the books evenly. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:45,500][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:45,533][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:45,539][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:45,549][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:45,555][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:45,561][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:45,633][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and the symmetry in the game, a reasonable strategy is to propose an allocation that maximizes the points based on the values. Since both agents are equally likely to have the same expected value, the best strategy is often to propose a split that maximizes your points based on your values. Proposal: 10 hats, 10 books, 10 balls This proposal will be fair and likely result in a good outcome for both agents, given the values: - You value hats and books highly and balls less. - Bob values books and balls highly and hats less. This split ensures that you get the items you value more, while Bob gets the items he values more. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:45,637][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:45,650][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:45,655][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:45,660][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:45,667][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:45,670][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:45,688][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:45,705][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:45,765][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:45,767][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:45,779][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:45,799][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's beneficial to propose a split that maximizes the points based on both the perceived value and the likely response from Alice. Since both of us have a high value for hats and Alice highly values books, it's strategic to offer a compromise that still benefits us significantly. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:45,803][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:45,805][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:45,807][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:46,072][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the situation and the values, it's beneficial to propose an even split to avoid leaving any items unallocated. Here’s a balanced proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:46,075][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:46,079][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:46,083][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:46,090][mllm.models.large_language_model_local][WARNING] - Response Given the values, it seems that both you and Bob have a strong preference for different items. You value hats and books highly (10 each), while you value balls less (1), and Bob values balls highly (10) and hats and books less (1 each). To maximize your points, you should propose to take all of the items you value highly, while letting Bob take the balls. Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:46,121][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:46,130][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:46,145][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:46,226][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:46,253][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:46,338][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:46,342][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:46,375][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:46,377][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:46,389][mllm.models.large_language_model_local][WARNING] - Response Given the high value of books and balls for both parties, and the low value of hats, a strategic proposal would be to maximize the high-value items while still proposing a fair share. ProposAl: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:46,433][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:46,436][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:46,474][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:46,477][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:46,555][mllm.models.large_language_model_local][WARNING] - Response Given the asymmetric values, a strategic proposal would be to maximize the points based on the higher values each of us attaches to the items. Since I value hats and balls at 1 and books at 10, and Bob values books at 10 and balls at 1, it makes sense to propose splitting the items in a way that maximizes our respective values. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:46,567][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:46,580][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:46,597][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:46,601][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:46,604][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:46,607][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:46,615][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric and equal distribution of values, proposing a fair split might be the best strategy to maximize points consistently. Therefore, I will propose to keep all 10 of each item. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:46,622][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:46,626][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:46,635][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:46,638][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:46,676][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both you and Alice have a strong preference for balls. Since balls are worth 1 point for you and 1 point for Alice, it might be wise to consider how to split the balls to take advantage of this common preference. However, we also need to consider the hats and books, which have different values for both of us. Proposal: 10 hats, 10 books, 10 balls This proposal splits the items evenly and takes into account the different values each of us places on the items. While this might not maximize our points in every permutation, it is a balanced approach that avoids potential conflict and ensures we each get a portion of the items we value highly. Let's submit it. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:46,688][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:46,695][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:46,727][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:46,730][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:46,754][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the previous rounds, proposing a fair split seems to maximize the points for both agents. Let's propose an equal share: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:46,817][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:46,829][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:46,843][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:46,845][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:46,878][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:46,879][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:46,894][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:46,898][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:46,987][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:46,989][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:47,027][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric nature of Bob's and your per-item values, proposing an equitable distribution would likely maximize points for both parties. Here, you both have a strong preference for hats and books, and a weaker preference for balls. Given this, a reasonable proposal would be to split the items as evenly as possible. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:47,109][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:47,120][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:47,123][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:47,125][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:47,161][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the fact that both of us have the same proposed strategy in the previous rounds, which resulted in equal points, I will propose the same distribution to maintain this equilibrium and ensure that we continue to maximize our points consistently. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:47,204][mllm.models.large_language_model_local][WARNING] - Response Given the symmetrical nature of the per-item values and the previous outcomes, it seems that proposing to take all of the items is a good strategy since it aligns with both your and Bob's maximal value for the items. Therefore, I will继续保持这种策略。 Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:47,230][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:47,231][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:47,271][mllm.models.large_language_model_local][WARNING] - Response Given that both you and Alice value balls highly but in different items, a strategic approach would be to propose a distribution that maximizes the value for the items you both value more. Here's a proposal that aims to secure more of the balls while still getting a fair share of hats and books: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:47,314][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:47,315][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:47,317][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:47,452][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:47,454][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:47,465][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and the symmetric situation where proposing the full quantity of items is a balanced strategy to ensure both parties receive their maximum possible value based on their preferences, I will propose: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:47,535][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:47,537][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:47,562][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:47,563][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:47,589][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric and equal distribution of values between hats and balls for both players, and the different values for books, a strategic proposal would be to maximize the items we value highly while ensuring the total quantity does not exceed the available items. Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:47,602][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:47,604][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:47,667][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:47,680][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:47,682][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:47,708][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:47,745][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:47,747][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:47,771][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:47,775][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:47,798][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:47,801][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:47,824][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:47,836][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:47,887][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Alice place high value on different items. You value hats and books highly, while Alice values hats and balls highly. Proposing to take all items ensures that you get the maximum possible value for the items you highly value, even if the items are split proportionally later. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:47,900][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:47,918][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:47,921][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:47,946][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:47,986][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:47,989][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:48,037][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:48,044][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:48,077][mllm.models.large_language_model_local][WARNING] - Response Given the consistent pattern where both you and Alice propose to take all items, and both of you receive equal shares, it seems that a cooperative strategy could maximize points. However, to be strategic, let's consider a split that might encourage Alice to propose a fair split, ensuring that we don't overextend and face the proportional allocation rule. Proposal: 10 hats, 10 books, 10 balls This proposal ensures that we take all items without exceeding the quantity, thereby avoiding proportional allocation. Since both sets of values are symmetric in this round, it should result in an equal split if Alice follows a similar strategy. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:48,149][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Bob value hats and books significantly more than balls. To maximize your points, you should propose to take all the items that you value highly. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:48,162][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric and competitive nature of the per-item values, a strategic approach would be to propose a split that maximizes the utilization of high-value items. Here, both you and Bob value hats and balls highly, while only you value books highly. Proposing to keep most of the items could lead to a proportional split that still maximizes your points. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:48,178][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:48,194][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:48,278][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:48,282][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:48,374][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:48,433][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:48,459][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:48,540][mllm.models.large_language_model_local][WARNING] - Response Given the high value of hats and books for both of us, I will propose a distribution that maximizes our combined points. Here, we both value hats and books highly, while balls are of lower value. To ensure we both receive items we value, I will propose an equal split. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:48,630][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:48,632][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:48,642][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:48,679][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Alice place a very high value on balls (10 for you, 1 for Alice) and a low value on hats and books (1 for both of you). Proposing to take all 10 hats, 10 books, and 10 balls should ensure you maximize your points since you value balls the most. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:48,691][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:48,714][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's beneficial to maximize the distribution of items where there's a higher value discrepancy between you and Alice. Since you value balls at 10 and Alice values them at 1, proposing to take all 10 balls would be strategically advantageous. Similarly, you value hats and Alice does as well, so splitting hats evenly might be reasonable. Books are valued similarly, so splitting them evenly could again be a good strategy. Proposal: 5 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:48,770][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:48,782][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:48,784][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:48,841][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:48,845][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:48,856][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the fact that both agents are proposing to take all items in every round, a rational approach would be to mirror Bob's proposal to ensure a fair split and maximize your points based on your values. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:48,862][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:48,908][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:49,031][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:49,035][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:49,061][mllm.models.large_language_model_local][WARNING] - Response Given the values, you have a high valuation for hats and balls, and Bob has a high valuation for books and balls. A cooperative strategy might be beneficial here, as splitting the items evenly could lead to a good outcome for both. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:49,097][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:49,111][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:49,134][mllm.models.large_language_model_local][WARNING] - Response Given the values, proposing an equal split might not be the best strategy, as the values are highly skewed. Here, my per-item values for hats and books are much lower compared to Alice's, while for balls, they are much higher. To maximize points, I should try to capture the items with higher personal value. Proposal: 10 hats, 10 books, 10 balls This proposal assumes a fair split, but given the values, a more strategic approach might involve trying to get more of the items with higher personal value to Alice and less of the items with lower personal value from Alice. However, without knowing Alice's strategy, a fair split might still be optimal. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:49,145][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:49,158][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:49,204][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:49,236][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:49,297][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:49,304][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:49,323][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:49,439][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:49,452][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:49,464][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:49,475][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:49,487][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:49,617][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:49,641][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:49,651][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:49,662][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:49,675][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:49,687][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:49,797][mllm.models.large_language_model_local][WARNING] - Response Given our per-item values and Bob's, it's clear that both of us place the highest value on "books" and "balls," while "hats" have a slightly lower value for both of us. A strategy where we propose to take all items could lead to a proportional split if Bob also takes all items, which results in zero points for each of us since the total exceeds the available quantity. A better strategy might be to propose a split that maximizes our points while ensuring we get at least some value from each item. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:49,820][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:49,864][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:49,999][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that books are highly valued by both of us, and balls are valued less by both of us. To maximize my points, I should try to secure as many books as possible while also getting some hats and balls. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:50,010][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:50,032][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:50,076][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:50,078][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:50,129][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:50,130][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:50,221][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:50,233][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:50,305][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:50,308][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:50,311][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:50,343][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:50,345][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:50,366][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Alice have a high value for hats and books, and a low value for balls. Since the values are symmetrical, proposing to take all items might lead to an efficient outcome. Therefore, I will propose to take all items to ensure we maximize our points. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:50,525][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:50,532][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:50,539][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:50,561][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:50,567][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:50,652][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:50,655][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:50,743][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:50,853][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:50,854][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:50,994][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:50,998][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:51,092][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:51,100][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:51,207][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:51,219][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:51,222][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:51,420][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:51,431][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:51,438][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:51,539][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:51,614][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:51,729][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:51,908][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:52,072][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that hats and balls are more valuable to you than books, while Bob values books more than hats and balls. To maximize your points, you should propose to take more hats and balls than books. Here's the optimal proposal: Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:52,247][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:52,326][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric nature of the per-item values and the results from previous rounds, it appears that proposing to take all items is not the最优策略。考虑到Alice的价值分布，她对书籍和球的价值较高，而你对帽子和书籍的价值较高，对球的价值较低。因此，合理的提议应该是尽量针对Alice的价值分布来分配物品。 提议：10 hats, 10 books, 10 balls 虽然这种提议在上一轮得到了满额，但在目前情况下，考虑到价值分配，这种提议可能并不是最优的。我们应该试图通过调整分配比例，以最大化自己的收益。 提议：8 hats, 10 books, 10 balls 这样，你可以保留你价值较高的帽子和书籍，同时分配一些球给Alice，这样既可以利用你的优势，又可以避免完全被Alice的高球价值所击败。 did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:52,502][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:53,097][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both hats and balls are highly valued by me but not by Bob, while books are valued by Bob but not by me. To maximize points, I should propose to take all the items that are highly valued by me to ensure I receive full value for them. Here is the proposed allocation: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:53,493][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:53,663][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:53,833][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:53,933][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both Alice and Bob place high价值在书籍和球上，而低价值在帽子上。为了最大化收益，合理的策略是尽量多获取高价值的物品。因此，提议如下： Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:09:54,116][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:09:55,524][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's beneficial to allocate items based on their value to maximize points. Here, both you and Bob value hats and balls similarly but books are valued very differently. To ensure you get the maximum value, you should propose to take the items that you value highly and leave the items valued highly by Bob. Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:09:56,947][__main__][INFO] - Number of regex retries in iteration 574: 2606 [2025-11-13 07:09:56,948][__main__][INFO] - agents played in iteration 574 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 07:09:58,020][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 07:09:58,051][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 07:09:58,076][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 07:09:58,100][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.61%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 07:09:58,101][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 07:09:58,101][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 07:09:58,945][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 07:09:59,404][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 07:09:59,901][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 07:10:00,396][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 07:10:00,890][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 07:10:01,388][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 07:10:01,881][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 07:10:02,373][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 07:10:02,871][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 07:10:03,361][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 07:10:03,854][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 07:10:04,361][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 07:10:04,856][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 07:10:05,376][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 07:10:05,872][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 07:10:06,364][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 07:10:06,866][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 07:10:07,367][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 07:10:07,865][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 07:10:08,369][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 07:10:08,870][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 07:10:09,393][mllm.training.trainer_common][INFO] - Processing mini-batch 21 of 64 [2025-11-13 07:10:09,895][mllm.training.trainer_common][INFO] - Processing mini-batch 22 of 64 [2025-11-13 07:10:10,399][mllm.training.trainer_common][INFO] - Processing mini-batch 23 of 64 [2025-11-13 07:10:10,897][mllm.training.trainer_common][INFO] - Processing mini-batch 24 of 64 [2025-11-13 07:10:11,400][mllm.training.trainer_common][INFO] - Processing mini-batch 25 of 64 [2025-11-13 07:10:11,902][mllm.training.trainer_common][INFO] - Processing mini-batch 26 of 64 [2025-11-13 07:10:12,402][mllm.training.trainer_common][INFO] - Processing mini-batch 27 of 64 [2025-11-13 07:10:12,903][mllm.training.trainer_common][INFO] - Processing mini-batch 28 of 64 [2025-11-13 07:10:13,405][mllm.training.trainer_common][INFO] - Processing mini-batch 29 of 64 [2025-11-13 07:10:13,905][mllm.training.trainer_common][INFO] - Processing mini-batch 30 of 64 [2025-11-13 07:10:14,411][mllm.training.trainer_common][INFO] - Processing mini-batch 31 of 64 [2025-11-13 07:10:14,912][mllm.training.trainer_common][INFO] - Processing mini-batch 32 of 64 [2025-11-13 07:10:15,412][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 07:10:15,915][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 07:10:16,414][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 07:10:16,914][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 07:10:17,411][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 07:10:17,911][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 07:10:18,411][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 07:10:18,907][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 07:10:19,404][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 07:10:19,908][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 07:10:20,407][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 07:10:20,906][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 07:10:21,406][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 07:10:21,904][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 07:10:22,406][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 07:10:22,903][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 07:10:23,402][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 07:10:23,903][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 07:10:24,401][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 07:10:24,904][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 07:10:25,402][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 07:10:25,902][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 07:10:26,402][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 07:10:26,895][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 07:10:27,392][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 07:10:27,941][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 07:10:28,437][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 07:10:28,931][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 07:10:29,422][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 07:10:29,918][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 07:10:30,414][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 07:10:30,909][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 11336 tokens. [2025-11-13 07:10:31,711][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.26%, Current % of VRAM taken: 59.71%, Block Peak % of device VRAM: 67.00%, ΔTime: 00:00:32 [2025-11-13 07:10:32,346][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 07:10:32,348][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 07:10:32,350][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 07:10:33,189][__main__][INFO] - Iteration 575 took 1m 41s (64.24% Gen, 34.93% Train). Generation: 1m 5s, Training: 35s. Estimated remaining time: 75h 26m 52s. Estimated total time: 84h 27m 44s. Time estimates for 10 more iterations: 16m 53s, 100 more iterations: 2h 48m 55s, 500 more iterations: 14h 4m 37s. [2025-11-13 07:10:33,192][__main__][INFO] - Starting iteration 575. [2025-11-13 07:10:33,674][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 57 and human policies 1. [2025-11-13 07:10:33,675][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 07:10:54,364][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,367][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,387][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,388][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,389][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,391][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,393][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,394][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,396][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,398][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,400][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,403][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,404][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,407][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,409][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,411][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,413][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,415][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,417][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,419][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,425][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,427][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,428][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,429][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,430][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,431][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,432][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,435][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,441][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,443][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,444][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,446][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,448][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,449][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,451][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,456][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,457][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,459][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,460][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,461][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,462][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,465][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,466][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,471][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,472][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,474][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,476][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,477][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,478][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,479][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,480][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,481][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,482][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,482][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,483][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,484][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,486][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,487][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,488][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,489][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,490][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,491][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,493][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,493][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,494][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,496][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,500][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,501][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,502][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,503][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,505][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,507][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,509][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,510][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,512][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,513][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,513][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,515][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,517][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,517][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,519][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,520][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,522][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,523][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,525][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,526][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,527][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,528][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,529][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,530][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,531][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,532][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,534][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,535][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,536][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,537][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,541][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,542][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,543][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:54,698][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,701][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,717][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,720][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,722][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,725][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,726][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,727][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,729][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,729][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,731][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,732][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,733][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,734][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,736][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,738][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,740][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,742][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,744][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,745][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,747][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,749][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,751][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,753][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,755][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,757][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,758][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,760][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,764][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,766][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,768][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,770][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,772][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,774][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,776][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,777][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,779][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,780][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,781][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,782][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,785][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,787][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,788][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,790][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,791][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,792][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,793][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,796][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,799][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,800][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,802][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,804][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,806][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,808][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,814][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,815][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,816][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,817][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,819][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,820][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,822][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,824][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,825][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,826][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,827][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,829][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,830][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,832][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,833][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,834][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,835][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,837][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,838][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,840][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,842][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,843][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,844][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,846][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,847][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,848][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,849][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,856][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,857][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,858][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,860][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,861][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,862][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,863][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,863][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,864][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,865][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,866][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,867][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,868][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,869][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,870][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,870][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,871][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:54,871][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:55,013][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,019][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,028][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,033][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,038][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,042][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,050][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,052][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,055][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,056][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,058][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,061][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,064][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,067][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,069][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,071][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,074][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,076][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,079][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,083][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,086][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,088][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,090][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,092][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,095][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,098][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,100][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,102][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,106][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,108][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,110][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,113][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,114][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,117][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,119][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,121][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,124][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,127][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,129][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,132][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,134][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,137][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,139][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,141][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,143][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,146][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,148][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,150][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,152][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,156][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,158][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,161][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,163][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,165][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,167][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,169][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,172][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,174][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,176][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,179][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,181][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,183][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,185][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,188][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,190][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,192][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,194][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,197][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,199][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,201][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,203][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,206][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,208][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,212][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,214][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,216][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,218][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,220][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,223][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,225][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,227][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,230][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,232][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,234][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,236][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,238][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,242][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,244][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,247][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,249][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,251][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,254][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,255][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,258][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,259][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,261][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,263][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,265][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:55,269][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:56,386][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,389][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,391][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,409][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,411][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,413][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,417][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,419][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,422][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,423][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,432][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,435][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,437][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,440][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,441][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,442][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,443][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,446][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,448][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,457][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,459][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,461][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,463][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,465][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,466][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,482][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,483][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,484][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,486][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,488][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,490][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,492][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,494][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,496][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,497][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,538][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,541][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,544][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,546][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,548][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,551][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,553][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,555][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,556][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,586][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,589][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,591][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,593][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,595][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,597][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,599][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,601][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,603][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,604][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,604][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,605][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,633][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,635][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,636][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,638][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,639][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,641][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,644][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,646][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,647][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,649][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,651][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,653][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,655][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,656][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,658][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,682][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,683][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,684][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,685][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,686][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,687][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,688][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,689][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,690][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,692][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,693][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,694][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,735][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,737][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,738][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,740][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,742][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,745][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,747][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,749][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,782][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,783][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,785][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,787][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,789][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,791][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:56,936][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:56,938][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:56,941][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:56,956][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:56,959][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:56,960][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:56,963][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:56,965][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:56,967][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:57,003][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:57,006][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:57,009][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:57,011][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:57,014][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:57,017][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:57,020][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:57,022][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:57,024][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:57,031][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:57,034][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:57,036][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:57,038][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:57,039][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:57,041][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:57,044][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:57,121][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:57,203][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:57,364][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:57,366][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:57,454][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:57,828][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:57,835][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:57,842][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:57,936][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:57,986][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:57,988][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:57,989][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:57,991][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:57,992][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:57,995][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:57,997][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:57,999][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:58,000][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:58,002][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:58,080][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:58,083][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:58,087][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:58,137][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:58,141][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:58,144][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:58,146][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:58,151][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:58,153][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:58,155][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:58,158][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:58,160][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:58,162][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:58,164][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:58,166][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:58,168][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:58,170][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:58,183][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:58,186][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:58,187][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:58,188][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:58,188][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:58,189][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:58,191][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:58,192][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:58,193][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:58,195][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:58,196][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:58,198][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 books, 10 balls, 10 hats did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:58,208][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:58,209][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:58,210][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:58,212][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:58,215][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:58,218][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:58,220][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:58,221][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:58,222][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:58,224][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:58,225][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:58,227][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:58,228][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:58,232][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:58,235][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:58,236][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:58,238][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:58,239][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:58,241][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:58,242][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:58,243][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:58,256][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:58,258][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:58,259][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:58,260][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:58,261][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:58,262][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:58,431][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:58,434][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:58,437][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:58,450][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:58,452][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:58,476][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:58,479][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:58,482][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:58,484][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:58,493][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:58,495][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:58,497][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:58,499][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:58,501][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:58,503][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:58,513][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:58,515][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:58,516][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:58,518][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:58,520][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:58,521][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:58,521][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:58,522][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:58,524][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:58,526][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:58,528][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:58,530][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:58,531][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:58,532][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:58,533][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:58,536][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:58,537][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:58,537][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:58,538][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:58,538][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:58,579][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:58,581][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:58,583][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:58,586][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:58,588][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:58,590][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:58,591][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:58,593][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:58,594][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:58,595][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:58,597][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:58,605][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:58,606][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:58,607][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:58,608][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:58,609][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:58,609][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:58,610][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:58,610][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:58,611][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:58,611][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:58,614][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:58,616][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:58,627][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:58,629][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:58,630][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:58,631][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:58,633][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:58,634][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:58,636][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:58,637][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:58,677][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:58,678][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:58,679][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:58,681][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:58,682][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:58,683][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:58,684][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:58,685][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:58,686][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:58,688][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:58,696][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:58,697][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:58,698][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:58,699][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:58,700][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:58,701][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:58,856][mllm.models.large_language_model_local][WARNING] - Response Given the new per-item values, where you have high value for books and low value for balls, while Bob has the opposite, a strategic proposal would be to maximize your advantage in books and slightly adjust for hats and balls. Here’s the proposal: proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:58,876][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:58,878][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:58,881][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:58,896][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:58,899][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:58,901][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:58,904][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:58,906][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:58,915][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:58,917][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:58,919][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:58,921][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:58,933][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:58,935][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:58,937][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:58,939][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:58,941][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:58,942][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:58,944][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:58,952][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:58,954][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:58,957][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:58,959][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:58,961][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:58,963][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:58,965][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:58,969][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:58,973][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:58,977][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:58,984][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:58,987][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:58,990][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:58,994][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:58,997][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:59,000][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:59,003][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:59,007][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:59,010][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:59,013][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:59,015][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:59,023][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:59,026][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:59,029][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:59,032][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:59,034][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:59,037][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:59,040][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:59,043][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:59,045][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:59,048][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:59,051][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:59,053][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:59,055][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:59,058][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:59,061][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:59,063][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:59,066][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:59,069][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:59,071][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:59,074][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:59,077][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:59,078][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:59,080][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:59,083][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:59,089][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:59,091][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:59,094][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:59,097][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:59,099][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:59,101][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:59,103][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:59,105][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:59,107][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:59,108][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:59,109][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:59,110][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:59,111][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:59,114][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:59,116][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:59,118][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:59,120][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:59,122][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:59,124][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:59,127][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:59,129][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:59,132][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:59,136][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:10:59,137][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:59,139][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:59,142][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:59,144][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:59,146][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:59,147][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:59,302][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:59,306][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:59,323][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:59,327][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:59,330][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:59,332][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:59,334][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:59,345][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:59,347][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:59,351][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:59,355][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:59,365][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:59,369][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:59,372][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:59,375][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:59,379][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:59,382][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:59,387][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:59,390][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:59,392][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:59,394][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:59,396][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:59,398][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:59,401][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:59,489][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:59,492][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:10:59,578][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:59,726][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:59,729][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:59,731][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:59,732][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:59,817][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:10:59,975][mllm.models.large_language_model_local][WARNING] - Response Given the updated per-item values, you have a strong preference for books and Alice has a strong preference for hats. To maximize your points, you should propose to take all the books and divide the hats and balls proportionally. Proposal: 5 hats, 10 books, 5 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:00,018][mllm.models.large_language_model_local][WARNING] - Response Given the new per-item values, I should aim to maximize my points by securing the balls, as they have the highest value for me. I will propose to take all the balls and split the hats and books equally. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:00,180][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:00,186][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:00,191][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:00,218][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:00,279][mllm.models.large_language_model_local][WARNING] - Response Given the new per-item values, it's clear that both you and Bob value hats and books relatively low, and balls quite highly. Since we both value balls the same and there's a lot to gain from them, it makes sense to coordinate our proposals to ensure we both get a fair share of the balls. Here's a balanced proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:00,334][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:00,407][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:00,409][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:00,471][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:00,474][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:00,477][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:00,480][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:00,525][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:00,527][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:00,530][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:00,533][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:00,548][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:00,551][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:00,556][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:00,575][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:00,578][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:00,580][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:00,583][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:00,584][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:00,585][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:00,588][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:00,590][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:00,591][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:00,601][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:00,604][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:00,608][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:00,612][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:00,615][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:00,617][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:00,618][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:00,618][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:00,619][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:00,621][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:00,622][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:00,624][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:00,625][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:00,626][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:00,628][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:00,629][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:00,634][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:00,635][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:00,636][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:00,677][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:00,679][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:00,681][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:00,683][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:00,684][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:00,685][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:00,724][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:00,728][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:00,743][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:00,834][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:00,860][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:00,863][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:00,882][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:00,885][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:00,902][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:00,904][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:00,905][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:00,907][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:00,927][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:00,929][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:00,931][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:00,933][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:00,935][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:00,936][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:00,969][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:00,971][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:00,973][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:00,974][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:00,976][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:01,057][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:01,059][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:01,060][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:01,063][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:01,145][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:01,149][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:01,151][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:01,153][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:01,190][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:01,210][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:01,212][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:01,214][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:01,216][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:01,251][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:01,254][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:01,256][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:01,259][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:01,261][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:01,294][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:01,297][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:01,299][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:01,302][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:01,304][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:01,307][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:01,310][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:01,312][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:01,314][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:01,316][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:01,318][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:01,319][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:01,320][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:01,321][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:01,322][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:01,323][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:01,337][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:01,339][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:01,341][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:01,343][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:01,344][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:01,354][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:01,482][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:01,502][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:01,505][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:01,508][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:01,511][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:01,523][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:01,525][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:01,544][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:01,547][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:01,549][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:01,553][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:01,564][mllm.models.large_language_model_local][WARNING] - Response Given the new values, you have a significantly high valuation for books and balls compared to hats, while Bob has a similar valuation but prioritizes books. To maximize your points, you should propose to take more books and balls, and a smaller portion of hats compared to what Bob might propose. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:01,585][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:01,588][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:01,613][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:01,615][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:01,618][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:01,621][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:01,623][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:01,625][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:01,627][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:01,630][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:01,632][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:01,634][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:01,667][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:01,669][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:01,672][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:01,675][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:01,739][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:01,762][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:01,765][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:01,769][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:01,774][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:01,778][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:01,786][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:01,790][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:01,794][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:01,796][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:01,807][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:01,811][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:01,813][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:01,816][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:01,831][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:01,835][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:01,838][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:01,841][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:01,844][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:01,847][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:01,850][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:01,853][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:01,855][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:01,857][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:01,860][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:01,861][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:01,864][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:01,866][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:01,869][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:01,871][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:01,874][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:01,875][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:01,875][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:01,878][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:01,894][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:01,987][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:02,010][mllm.models.large_language_model_local][WARNING] - Response Given Alice's valuation of hats and balls higher than books, and your own similar but reversed preference, a cooperative approach might not be optimal. Here, proposing an allocation that captures more value for your higher valued items could be beneficial. Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:02,013][mllm.models.large_language_model_local][WARNING] - Response Given the updated per-item values where hats and books are less valuable for both of us, but balls are still highly valuable, I should focus on claiming the maximum number of balls to maximize my points. Here's the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:02,015][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:02,020][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:02,025][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:02,027][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:02,034][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:02,036][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:02,061][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:02,064][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:02,068][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:02,072][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:02,103][mllm.models.large_language_model_local][WARNING] - Response Given Bob's high value for balls and your own lower but significant values for books and balls, as well as hats, a strategic proposal would be to offer a split that takes into account the high value Bob places on balls while balancing the distribution for the other items. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:02,104][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:02,105][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:02,145][mllm.models.large_language_model_local][WARNING] - Response Given Bob's values for books and balls, it's clear that he values books and balls significantly more than hats. To maximize my points, I should try to get as many books and balls as possible while giving him at least one hat to ensure the proposal is not disproportionate. Proposal: 1 hat, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:02,148][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:02,150][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:02,151][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:02,152][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:02,153][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:02,154][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:02,156][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:02,160][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:02,163][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:02,201][mllm.models.large_language_model_local][WARNING] - Response Given that Bob values hats and books higher than I do, and I value balls higher than he does, a strategic approach would be to try to assign items in a way that both values the items highly are split based on their perceived worth. Here’s a more balanced proposal: Proposal: 10 hats, 10 books, 5 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:02,290][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:02,365][mllm.models.large_language_model_local][WARNING] - Response Given Bob's per-item values for hats=1, books=1, balls=10, it seems Bob values books and balls much higher than hats. To maximize my points, I should match Bob's valuation of books and balls to ensure I get a significant portion of those items. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:02,370][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:02,375][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:02,393][mllm.models.large_language_model_local][WARNING] - Response Given Bob's per-item values for hats=1, books=1, and balls=10, it seems he values books and balls more than hats. To maximize my points, I should propose keeping more books and balls, while taking a small number of hats to fill up the remaining items. Proposal: 3 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:02,418][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and Bob's strategy in the previous round, it seems Bob is valuing hats and books more highly than balls. To maximize my points, I should propose a division that takes advantage of this discrepancy while ensuring I get a substantial amount of high-value items for me. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:02,481][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:02,484][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:02,485][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:02,488][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:02,490][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:02,492][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:02,573][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:02,848][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:02,851][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:02,854][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:02,894][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:03,044][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:03,047][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:03,093][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric nature of the per-item values and the previous outcomes, it seems that splitting the items equally leads to a fair and beneficial outcome for both parties. Since my per-item values for hats and books are significantly higher than Alice's, and her per-item value for balls is higher, proposing an equal split might still yield a good result. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:03,097][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:03,099][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:03,106][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:03,118][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:03,119][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:03,121][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:03,123][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:03,125][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:03,127][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:03,129][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:03,152][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:03,155][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:03,176][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:03,178][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:03,219][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:03,245][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:03,289][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:03,292][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:03,295][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:03,316][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:03,319][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:03,322][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:03,323][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:03,325][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:03,327][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:03,340][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric values and knowing that both Alice and I value hats and books highly while valuing balls less, a strategic proposal would be to split the items in a way that maximizes the use of our high-value items while still considering the proportional distribution if the sums exceed the available items. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:03,364][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:03,366][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:03,432][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:03,434][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:03,437][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:03,440][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:03,536][mllm.models.large_language_model_local][WARNING] - Response Given the updated values, where your preference for balls is significantly lower compared to Bob, and your values for hats and books are relatively low, a more strategic proposal would be to focus on items you value more highly while ensuring you get a reasonable share of the lower-value items. Here's a strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:03,565][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:03,583][mllm.models.large_language_model_local][WARNING] - Response Given the values, you highly value hats and books, while Alice values them less. This suggests a competitive dynamic for hats and books, but you and Alice both value balls less. To maximize your points, you should propose to take all the hats and books, and all the balls, since both of you value balls the least. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:03,585][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:03,602][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:03,604][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:03,607][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:03,625][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:03,645][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:03,648][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:03,652][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:03,655][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:03,659][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:03,663][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:03,679][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:03,681][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:03,683][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:03,691][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:03,693][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:03,742][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:03,764][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:03,806][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:03,808][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:03,811][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:03,826][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:03,828][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:03,829][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:03,830][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:03,832][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:03,849][mllm.models.large_language_model_local][WARNING] - Response Given the pattern in Bob's valuations, it seems he values hats highly and balls less. To maximize my points, I should propose keeping more hats and balls, and fewer books, since I value books less than hats and balls, but Bob values hats very highly. Proposal: 10 hats, 10 balls, 0 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:03,851][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:03,855][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:03,857][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:03,859][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:03,860][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:03,861][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:03,871][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:03,873][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:03,889][mllm.models.large_language_model_local][WARNING] - Response Given the values, it seems that I should focus more on items where my value is higher compared to Bob's. Since hats and books have higher values for me, and balls have higher values for Bob, a strategic proposal would be to allocate more items that have higher value for me. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:03,893][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:03,894][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:03,935][mllm.models.large_language_model_local][WARNING] - Response Given the values, it appears that both you and Bob value hats and books more than balls. In this round, proposing an equal distribution might not be the best strategy. Instead, you should consider proposing to take more of the items you value more to maximize your points. Here’s a strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:03,972][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:03,978][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:03,980][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:03,983][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:03,985][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:03,995][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:04,025][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, the optimal strategy would be to maximize the items with the highest value for you, which are the balls. Here's the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:04,026][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:04,047][mllm.models.large_language_model_local][WARNING] - Response Given the values, it seems that both you and Alice place a high value on different items. Since you value hats and books more, and Alice values books and balls more, a cooperative approach might not be the best here. Instead, you should propose an allocation that maximizes your points based on your higher values for hats and books. Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:04,053][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:04,060][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:04,062][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:04,092][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both hats and books are more valuable to Alice, while balls are more valuable to you. A strategic approach would be to propose a split that recognizes these values but also aims to maximize your points. Since Alice values books and hats more, she is likely to propose giving you the balls. Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:04,098][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and equal per-item values for both hats and books between us, while balls have a significantly different value, a strategic approach would be to split the items fairly to maximize the points while considering the higher value of balls. Here’s a proposal that balances the items: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:04,142][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:04,144][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:04,220][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:04,222][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:04,229][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:04,287][mllm.models.large_language_model_local][WARNING] - Response Given the pattern in Bob's values and my own, it's clear that Bob values hats and books more, and I value balls more. In previous rounds, both of us proposed taking all items equally, leading to a fair but suboptimal outcome for both since we are not leveraging our differing valuations. To maximize points, I should focus on getting as many balls as possible, while Bob should focus on getting hats and books. In this round, I will propose taking all the balls, while letting Bob take all the hats and books. Proposal: 10 balls, 0 hats, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:04,291][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:04,333][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:04,338][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:04,342][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:04,373][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:04,377][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:04,380][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:04,384][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:04,388][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:04,462][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:04,465][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:04,467][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:04,469][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:04,552][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:04,559][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:04,640][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:04,644][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:04,792][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:04,796][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:04,798][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:04,815][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:04,836][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:04,880][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:04,883][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:04,943][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:04,947][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:04,949][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:04,951][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:04,954][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:04,969][mllm.models.large_language_model_local][WARNING] - Response Given the consistent values and the previous outcomes, it seems both of us value the hats most and the balls least. However, proposing to take all items might lead to equal splits as Alice might also try to maximize her points by taking as many hats as possible. To achieve a better outcome, let's try to allocate items in a way that maximizes our individual values while considering Alice's likely behavior. Proposal: 10 hats, 10 books, 10 balls This proposal might lead to a proportional split if Alice also takes a large portion. To mitigate this, we should consider proposing a more strategic split. Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:04,972][mllm.models.large_language_model_local][WARNING] - Response Given the values and the pattern of previous rounds, it seems that both Alice and Bob are proposing to take all items, which results in each receiving 60 points per round. Since the items are split proportionally if the sum exceeds the total quantity, and both are proposing to take all items, they are likely to end up with the same allocation as before. To maximize points, it might be optimal to mirror Bob's strategy while slightly adjusting to ensure a fair allocation. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:04,974][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:04,990][mllm.models.large_language_model_local][WARNING] - Response Given the new per-item values, where you have a high value for balls (10) and Bob values hats more (10), it's beneficial to consider proposing a split that maximizes your points by taking more of the items you value highly. However, since both of us value hats highly and we have proposed to take all items equally in previous rounds, let's propose a different allocation to see if we can gain an advantage. Proposal: 10 hats, 10 books, 10 balls This proposal keeps the分配均衡，但鉴于我们对帽子的高价值认知，可以考虑稍微调整一下。 Proposal: 8 hats, 10 books, 12 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:04,992][mllm.models.large_language_model_local][WARNING] - Response Given the high value of books and balls for both of us, it's important to secure as many books and balls as possible, while also taking hats, but not at full capacity since Alice values hats highly. A balanced approach would be optimal. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:04,994][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric and competitive nature of the per-item values, it makes sense to propose an even split to maximize the utilization of the items and ensure that both parties receive a fair share. Here is the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:04,996][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:05,029][mllm.models.large_language_model_local][WARNING] - Response Given that both Alice and Bob have high values for hats and books, and low values for balls, we should focus on claiming as many hats and books as possible while minimizing the balls. Here's a strategic split: Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:05,033][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:05,035][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:05,038][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:05,215][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:05,218][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:05,257][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:05,259][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:05,269][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that hats and books have relatively low value for both of us, while balls have high value for you and low value for Bob. My strategy should be to maximize the number of balls I get, while splitting hats and books evenly since their value is low for both of us. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:05,348][mllm.models.large_language_model_local][WARNING] - Response Given the nature of the values, it seems that the optimal strategy involves recognizing the disparity in the values each of us places on the items. Since your values for hats and books are much higher than Bob's, and his value for balls is much higher than yours, it might be beneficial to split the items in a way that aligns with these preferences. Proposal: 10 hats, 10 books, 10 balls This strategy might not fully leverage the disparity in values. Let's adjust the proposal to better reflect these values: Proposal: 10 hats, 10 books, 0 balls This way, you fully capture the higher value items that you care about more, while offering the lower-value items to Bob. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:05,352][mllm.models.large_language_model_local][WARNING] - Response Given Alice's valuation of books and balls much higher than mine, she would likely try to take as many balls and books as possible. To balance this, I need to ensure I secure the hats, which are valued the same for both of us, and split the books and balls in a way that maximizes my points. Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:05,355][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:05,359][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:05,417][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in values and the nature of the items, proposing a balanced distribution while respecting the highest individual values for each item will likely maximize the points. Since both you and Bob value balls the least and hats and books the most, splitting them reasonably can ensure fair distribution while maximizing points. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:05,421][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:05,427][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:05,434][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:05,484][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and the symmetry in the game, it seems the optimal strategy is to split the items evenly to ensure both parties receive a fair share. This approach maximizes the utility for both parties, as each will get items valued highly by the other. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:05,487][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:05,507][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Alice have the highest value for hats and the lowest for books, while the value for balls is slightly higher but not significantly so. The previous rounds showed that splitting the items equally leads to a distribution where each of you receives an equal share, which results in a lower total score for items with lower individual values. To maximize your points, you should try to secure as many high-value items as possible. Here is a proposed strategy: focus on taking all the hats and a significant portion of the balls, while splitting the books evenly, as they have the lowest value. Proposal: 10 hats, 5 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:05,510][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the high values for hats and books for both of us, and the lower but non-negligible value for balls, I will propose to split the items as evenly as possible to maximize the points for both hats and books, while also taking a share of the balls. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:05,529][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both hats and balls are more valuable to Alice than to Bob. To maximize my points, I should focus on getting as many hats and balls as possible, while conceding books to Bob, since he values them more. Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:05,532][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:05,535][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:05,554][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:05,557][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:05,560][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:05,575][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:05,596][mllm.models.large_language_model_local][WARNING] - Response Given that both you and Alice have similar valuations for different items, a strategic approach would be to recognize that proposing the maximum amount for each item might not always yield the best results, especially if Alice is likely to do the same. This could lead to over-proportional allocation of items, resulting in a lower value per item. A better strategy might be to propose a distribution that maximizes your points while acknowledging that Alice might do the same. Let's split the items as follows: - Hats: You value them highly, so keep 10 hats. - Books: You value them less than Alice, but still keep some to balance the points. - Balls: Alice values them highly, so you keep fewer balls. Proposal: 10 hats, 10 books, 5 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:05,598][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:05,621][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Alice have a strong valuation for hats and a weak valuation for balls. However, the proposals in the previous rounds resulted in each of you earning the same amount, suggesting that we might be splitting the items proportionally when the total proposals exceed the available items. To optimize our strategy, we should consider maximizing our points based on our higher-valued items, while also ensuring we don't overcommit to items that Alice values more. Here is a more nuanced proposal: Proposal: 10 hats, 10 books, 0 balls This proposal focuses on maximizing the high-value items while minimizing the one that Alice values the most. This should help in securing a higher share of the high-value items, potentially leading to better points for both of us. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:05,624][mllm.models.large_language_model_local][WARNING] - Response Given the symmetrical value distributions and the fact that both you and Bob value the hats highly, it's important to ensure that we both get a significant portion of the hats. Let's propose a slightly adjusted split to maximize our points, while still ensuring we both get a fair share of the items with high value. Proposal: 9 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:05,626][mllm.models.large_language_model_local][WARNING] - Response Given the values, you have a high valuation for hats and Bob has a high valuation for books and balls. To maximize your points, you should focus on getting as many hats as possible since they have the highest value for you. Here is a strategic proposal: Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:05,628][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:05,632][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:05,635][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:05,637][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:05,670][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:05,694][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, you have a strong preference for books and balls, while Alice prefers hats and books. In this round, both of you have significant value for all items. To maximize your points, it makes sense to propose a distribution that aligns with your high values for books and balls. Proposal: 10 hats, 10 books, 10 balls This proposal ensures that you get a fair share of all items, thereby maximizing your points based on your high values for books and balls. However, to be strategic, you might consider slightly underproposing books and balls to see if Alice might over-provide them, but given the symmetry and the high values involved, splitting equally is likely the best approach. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:05,697][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:05,699][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:05,702][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:05,705][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:05,708][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:05,745][mllm.models.large_language_model_local][WARNING] - Response Given that both you and Bob have high values for hats and books, and low values for balls, it's important to ensure that we both get a fair share of the items that have higher values. Here’s a proposal that aims to maximize the split of high-value items while ensuring we don't exceed the item quantities: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:05,766][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:05,770][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:05,792][mllm.models.large_language_model_local][WARNING] - Response Given the values, it would be strategic to maximize the items with higher individual values. Here, both you and Alice value books and balls highly, but you value hats lower than Alice. To maximize points, you should propose to take more of the items you value higher. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:05,796][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:05,799][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:05,802][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:05,805][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:05,808][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:05,816][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:05,819][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:05,822][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:05,835][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:05,838][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:05,877][mllm.models.large_language_model_local][WARNING] - Response Given the new values where both you and Bob highly value different items, a strategic approach would be to offer an allocation that maximizes your expected points based on the perceived utilities of both parties. Since you value hats and books but not balls, and Bob seems to value balls but not hats or books, you should propose an allocation that aligns with these values to ensure you receive items you highly value. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:05,881][mllm.models.large_language_model_local][WARNING] - Response Given the distribution of per-item values, it's clear that books and balls are highly valued by both players, while hats are valued less. Alice values books and balls highly, and I value hats and balls highly. To maximize points, it makes sense to propose a split that takes into account the high value of balls and the low value of hats. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:05,883][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it makes sense to maximize the items with higher personal values. Here, hats and balls are valued at 10 each, while books are valued at 10 for both of us. A strategic proposal would be to take all items that maximize your value. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:05,896][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the situation and the equal per-item values for both hats and books, and considering the low value for balls, a cooperative strategy might be optimal. However, proposing the majority of items could maximize my share of high-value items. Here is the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:05,918][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both Alice and Bob value balls the most and hats the least. Since our per-item values are quite different, and we want to maximize our points, we should propose to take as many of the items we value more, i.e., balls, and split the remaining items fairly to avoid any advantage going to the other player. Proposal: 10 hats, 10 books, 20 balls (since we can propose more than 10 for balls, we will propose to take all 20 balls) However, since we can only propose up to 10 items for each category and the total number of items available is 30, we need to adjust our proposal to ensure it does not exceed the available items. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:05,922][mllm.models.large_language_model_local][WARNING] - Response Given the new per-item values where both you and Alice value hats and books highly but differ in the valuation of balls, a strategic proposal would be to take the majority of the items that are valued highly by both of you to ensure maximum points. Here's the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:05,937][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both hats and balls are highly valued by me and Alice, respectively, while the books are valued similarly by both. To maximize my points, I should propose to take all the hats and balls, and split the books, as this would align with my higher per-item values. Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:05,940][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:05,945][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:05,954][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Alice place a higher value on different items, but both items (hats and balls) are valued more by you compared to Alice. Books are valued more by Alice. Since the allocations are proportional and the total items are exactly divided, it's strategic to propose an equal split to ensure both items are allocated to the agents who value them more. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:05,957][mllm.models.large_language_model_local][WARNING] - Response Given Alice's per-item values for books and balls are significantly higher than mine, it makes sense to allocate more of these items to her to maximize her earnings. Meanwhile, I should keep the hats, as my value for them is the highest. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:05,958][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:05,960][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:05,962][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:05,976][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Alice value the books and hats highly, but the balls are less valuable to you and more valuable to Alice. In this round, proposing a different allocation might yield better results. To maximize your points, you should take into account that Alice values the balls more. Thus, you should propose to take more of the items you value more (books and hats) and leave more balls for Alice. Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:05,977][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:05,979][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:05,983][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:05,985][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:05,999][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:06,021][mllm.models.large_language_model_local][WARNING] - Response Given the high value Alice places on books and balls, and your own high value on balls, a cooperative strategy might be beneficial to ensure that both of you get the items you value highly. However, proposing the maximum amount for each item might lead to a proportional distribution if Alice values the items differently. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:06,024][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:06,061][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:06,067][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:06,069][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:06,084][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:06,087][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:06,093][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:06,132][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:06,135][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:06,174][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:06,177][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:06,180][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:06,183][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:06,196][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:06,217][mllm.models.large_language_model_local][WARNING] - Response Given that both Alice and Bob have similar values for hats and books, and balls, and both of you are proposing to take all items equally, a cooperative strategy might yield the best results in terms of maximizing points. Here, we should ensure that we capture the high-value items while also balancing the distribution. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:06,220][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:06,226][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:06,236][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:06,241][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:06,261][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both hats and books are valuable to you but not to Alice, while balls are valuable to Alice but not to you. To maximize your points, you should propose to take all the items that you value highly while Alice values less, and share items that Alice values highly. Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:06,267][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:06,269][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:06,271][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:06,273][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:06,275][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:06,279][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:06,282][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:06,286][mllm.models.large_language_model_local][WARNING] - Response Given the current situation, it's clear that both you and Bob value books the most, and balls the least. However, proposing all items equally might not be optimal since the values are so uneven. Here’s a more strategic approach: - You value books the most (10 points each). - Bob also values books but highly (1 point each). To maximize your points, you should propose to take more books than Bob, while still ensuring you both receive some balls. Here’s a refined proposal: Proposal: 10 hats, 10 books, 7 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:06,287][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:06,290][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:06,333][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:06,352][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:06,355][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:06,394][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:06,398][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:06,418][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the low value items have for both of us, while Bob values the balls highly, it's still in my best interest to propose a fair split. Let's try to allocate the items in a way that maximizes the value for both of us. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:06,421][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:06,423][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:06,429][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:06,440][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the situation and knowing that both Alice and Bob have the same per-item values for the same types of items, a cooperative approach might yield the best results. However, to ensure that we maximally cover all items and avoid any risk of proportional allocation, we can propose: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:06,442][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:06,443][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:06,484][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:06,508][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:06,532][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:06,534][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:06,648][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:06,654][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:06,661][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:06,679][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:06,758][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:06,764][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:06,770][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:06,805][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:06,811][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:06,855][mllm.models.large_language_model_local][WARNING] - Response Given the values, it seems optimal to propose a split that maximizes the points based on our higher values for hats and balls, and Alice's higher value for books. Here's the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:06,899][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:06,902][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:06,936][mllm.models.large_language_model_local][WARNING] - Response Given the new per-item values, where you value books at 10 and balls at 1, and Bob values hats at 10, books at 1, and balls at 1, proposing an equal share might not be optimal. Here's the reasoning: - Hats: You value hats at 1, while Bob values them at 10. You would get 1 point for each hat you keep, and Bob would get 10 points. It's in your best interest to get as many hats as possible. - Books: You value books at 10, and Bob values them at 1. Dividing books equally would result in each of you getting 10 points for the books each owns. - Balls: You value balls at 1, and Bob values them at 1. Dividing balls equally would result in each of you getting 1 point for the balls each owns. Considering these values, an optimal proposal would be to take all items you value highly and let Bob take the items he values highly. Therefore, the proposal would be: proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:07,022][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it appears that both you and Bob place a high value on different items. You value hats and books highly (10), while Bob values balls highly (10). To maximize your points, you should try to secure more of the items you value highly. Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:07,025][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:07,102][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in values and the goal to maximize points, a strategic approach would be to propose a split that maximizes the benefit of your high values for hats and books while also considering the high value Bob places on balls. Here is a balanced proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:07,106][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it is clear that hats are highly valuable to you and Alice values books more than you do. To maximize your points, you should propose to take all the hats and as many books as possible, while dividing the balls proportionally. Proposal: 10 hats, 10 books, 5 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:07,109][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:07,112][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:07,184][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and the symmetry in the allocation, the best strategy would be to propose a fair division that maximizes the points considering the values. Since both of us value the same items, it's in our best interest to split them evenly. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:07,187][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:07,267][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:07,407][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:07,414][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:07,421][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:07,449][mllm.models.large_language_model_local][WARNING] - Response Given Bob's valuation of hats and balls, it's clear that he values books significantly less than both hats and balls. To maximize my points, I should try to claim as many books as possible since they have high value for both of us, and then split the remaining items fairly. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:07,452][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:07,696][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Bob value balls the most, although you value hats and books less than Bob does. Since both of you propose the same distribution and the items are split proportionally if the sum of proposed items exceeds the available items, it might be strategic to adjust the proposal slightly to ensure a better allocation, especially since you value balls the most. Proposal: 10 hats, 10 books, 9 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:07,699][mllm.models.large_language_model_local][WARNING] - Response Given the values, it appears that both you and Alice have complementary preferences for the items. You value hats and balls highly (10) while Alice values books highly (10). To maximize your points, it's strategic to offer a split that allows you to get the items you value most while still being acceptable to Alice. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:07,703][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:07,706][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:07,829][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:07,832][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:07,914][mllm.models.large_language_model_local][WARNING] - Response Given the new per-item values where you value hats and books at 10 and balls at 1, while Alice values hats and balls at 1 and books at 10, it makes strategic sense to propose a distribution that maximizes your value. Here, proposing to keep all the items would align with your higher valuations. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:07,922][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:07,979][mllm.models.large_language_model_local][WARNING] - Response Given the new per-item values, where you value hats at 1 and books at 10, while Bob values hats at 10 and books at 1, it's clear that books are highly valued by both of us. Here's a strategic proposal: Proposal: 0 hats, 10 books, 10 balls This proposal maximizes the value for books, which are highly valued by both of us, and splits the balls evenly. This way, we ensure we get a significant portion of the most valuable items. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:07,982][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:07,985][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:07,987][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:08,031][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:08,087][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:08,166][mllm.models.large_language_model_local][WARNING] - Response Given the consistent pattern in Alice's valuation and the random assignment of item values, it seems that the optimal strategy in this round is to mirror Alice's proposed distribution to ensure both parties receive a fair share and maximize points based on their individual valuations. proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:08,192][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:08,195][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:08,297][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, you should prioritize the items where your value is higher relative to Alice's. Here, hats and balls are valued higher by you, while books are valued higher by Alice. You should aim to secure more of the hats and balls while splitting the books more evenly or letting Alice take most of them. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:08,300][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and equal per-item values that maximize when both items are split equally, I will propose an allocation that balances the items to ensure we maximize our points. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:08,302][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:08,335][mllm.models.large_language_model_local][WARNING] - Response Given the values, it seems like the optimal strategy is to maximize the items with higher personal value while considering Alice's values. Since I value books and balls more than hats, and Alice values hats and books more than balls, the best proposal would be to take all the balls and split the hats and books proportionally. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:08,379][mllm.models.large_language_model_local][WARNING] - Response Given the updated per-item values, where my values for hats and books are higher, and Bob's values for hats and books are higher, a more strategic approach might be needed. To maximize points, I should propose an allocation that takes advantage of my higher values for hats and books while still leaving some value for balls. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:08,424][mllm.models.large_language_model_local][WARNING] - Response Given that both you and Alice value the books and balls similarly but have differing values for hats, a strategic proposal would be to suggest an allocation that aligns with your higher value for books and balls while ensuring you receive a fair share of hats. Here's a balanced proposal: Proposal: 5 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:09,098][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both you and Alice place the highest value on balls and the lowest on books. To maximize your earnings, you should propose to take all the items that you value highly, i.e., all the balls. Here is the proposal: Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:09,159][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and the strategy of maximizing points, it's important to consider the distribution that aligns with both your and Bob's preferences while ensuring the total proposed quantity does not exceed the available items. Here, your values for books and balls are much higher than hats, while Bob values hats and balls more. Let's allocate items in a way that maximizes your points. Proposal: 1 hat, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:09,196][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it seems advantageous to focus on the items where you have a higher value compared to Alice. Specifically, books and balls have higher values for you, while hats are valued more by Alice. A strategic proposal would be to maximize the items you value more. Proposal: 10 hats, 10 balls, 10 books This proposal ensures you get all the items you value highly, while Alice likely values the hats more. However, if the total exceeds the available quantity, the items will be allocated proportionally. Given the equal distribution and the nature of the values, this should work effectively. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:09,200][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Bob place high value on balls and low value on hats and books. Since your values for hats and books are lower than Bob's, you might want to consider a strategy that reflects this, such as proposing to take all the balls to maximize your points, while leaving the hats and books for Bob to take. Proposal: 0 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:09,207][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:09,210][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:09,213][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:09,216][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:09,218][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:09,235][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it is clear that both you and Alice prioritize balls and books over hats. However, to ensure a balanced and cooperative proposal, we can split the items proportionally based on their values. Here's a strategic proposal that balances the distribution of items: Proposal: 10 hats, 10 books, 8 balls for you and 10 balls for Alice This proposal ensures that you get a portion of the balls, which have the highest value for both of you, while still maintaining a balanced distribution of hats and books. This might lead to a more even split and a chance for both of you to maximize your points. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:09,238][mllm.models.large_language_model_local][WARNING] - Response Given the values, hats are highly valued by both of us, and balls are valued equally but with lower values. Books are valued more by Bob than by you. A good strategy here is to secure as many hats as possible since they provide the highest individual value for you. Here's a proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:09,241][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:09,257][mllm.models.large_language_model_local][WARNING] - Response Given Bob's per-item values, he highly values hats and balls while valuing books less. To maximize my points, I should propose to take all the books and split the hats and balls in a way that still gives me a respectable amount, considering my lower values for hats and balls compared to Bob's. Proposal: 5 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:09,260][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:09,266][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:09,268][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:09,271][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:09,299][mllm.models.large_language_model_local][WARNING] - Response Given the values, you have a strong preference for hats and balls, while Bob has a stronger preference for books. Here is a strategic proposal to maximize your points by ensuring you get the items you value highly: Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:09,344][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the low values of my per-item preferences compared to Bob's, I can see that proposing to take all items might not be the optimal strategy. Instead, I should consider how to allocate the items in a way that maximizes my points. Since my per-item value for books and balls is higher than for hats, it would be beneficial to secure more books and balls while ensuring I get some hats. Proposal: 5 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:09,348][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the situation and the values, proposing an equal split seems strategic. Since we both value hats and books highly and balls minimally, we can propose keeping all the items to maximize our points. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:09,366][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:09,389][mllm.models.large_language_model_local][WARNING] - Response Given the current situation, it's clear that both Bob and I value the balls the most, while we value the hats and books less. However, the low individual values for hats and books mean that splitting them equally might not yield much benefit but could still be necessary to avoid losing more valuable items. A strategic move here might be to maximize the value from the balls, while ensuring we don't lose too much value from the hats and books. I will propose to take more balls and share the hats and books more evenly to cover their lower but still non-negligible values. Proposal: 10 hats, 10 books, 9 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:09,424][mllm.models.large_language_model_local][WARNING] - Response Given the values, Alice places a higher value on hats and books, while you place a higher value on balls. In this round, distributing the items will likely lead to a fair allocation since the total value for books and hats is roughly even between both of you. However, balls are heavily valued by you, so you should propose to keep as many balls as possible while also considering the value Alice places on hats and books. Proposal: 10 hats, 10 books, 10 balls This proposal ensures you get the maximum value for balls while still considering the symmetric value for hats and books. Alice might propose a similar distribution, leading to a fair split. However, since balls are more valuable to you, you should attempt to keep as many as possible to maximize your points. Proposal: 10 hats, 10 books, 9 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:09,427][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:09,430][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:09,444][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:09,447][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:09,450][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:09,452][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:09,455][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:09,467][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and the symmetry in the situation, it appears that proposing to take all items is a balanced approach, as both you and Bob have high values for different items. To ensure fairness and maximize points, a reasonable proposal would be to split the items roughly according to their values. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:09,470][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:09,473][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:09,476][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:09,489][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Bob place high value on balls (10), and low value on hats (1) and books (1). If Bob is likely to take all the balls since his value for balls is significantly higher, it might be strategic to claim all the hats and books for yourself, while offering to share the balls in a way that maximizes your points. Proposal: 10 hats, 10 books, 5 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:09,491][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:09,498][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:09,511][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both you and Alice value balls the most at 10, followed by hats and books at 1. However, in previous rounds, proposing to take all items has resulted in equal distribution due to proportionality when the sum exceeds 10. To maximize your points, you should consider a strategy that takes into account the lower values of hats and books while maximizing the higher value of balls. A balanced approach would be to propose a split that slightly favors you, given your higher value for balls. Here's a proposal: Proposal: 10 hats, 10 books, 9 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:09,514][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:09,532][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both you and Alice place high value on books and low value on hats, while balls are valued relatively the same by both. To maximize your points, you should try to allocate more of the books and balls to yourself, while keeping at least some hats for balance. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:09,538][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:09,541][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:09,552][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:09,588][mllm.models.large_language_model_local][WARNING] - Response Given the values, it seems that both hats and books are more valuable to Alice, while balls are more valuable to you. To take advantage of this, you might want to propose a distribution that balances the items in a way that maximizes your points. Here's a proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:09,595][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:09,610][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:09,617][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:09,620][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:09,622][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:09,625][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:09,654][mllm.models.large_language_model_local][WARNING] - Response Given the values, you have a high value for balls (10) and Bob has a high value for hats (10), while both of you have a low value for books (1). To maximize your points, it's beneficial to allocate the items that you value highly and let Bob take the items he values more. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:09,677][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:09,680][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:09,683][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:09,686][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:09,692][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:09,695][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:09,711][mllm.models.large_language_model_local][WARNING] - Response Given Alice's high value for books and balls and your higher value for books and balls compared to hats, it is beneficial to ensure you receive a significant portion of the books and balls while also considering the hats. A balanced approach would be to propose taking a majority of the items that have higher individual values for either of you. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:09,713][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:09,718][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:09,721][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:09,727][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:09,732][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:09,762][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the high values for hats and books, it's beneficial to maximize the split for items with higher personal value while also considering Bob's preferences. Since both of us value hats and books highly and have low personal value for balls, a competitive and cooperative strategy would be to propose the maximum possible amount for hats and books, while leaving balls to be split in a way that avoids over-allocation. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:09,782][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:09,784][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:09,821][mllm.models.large_language_model_local][WARNING] - Response Given the new per-item values, where I value hats at 1 and books at 10, while Bob values hats at 10 and books at 1, it makes sense to propose a distribution that aligns with our respective values. I should keep the books and allow Bob to keep the hats. Here's the proposal: Proposal: 10 hats, 10 books, 10 balls However, since I value books more, I should propose keeping all the books and letting Bob keep the hats. This way, I maximize my points based on my values. Here’s the revised proposal: Proposal: 10 hats, 10 books, 10 balls But since we should focus on maximizing my points, I will propose: Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:09,824][mllm.models.large_language_model_local][WARNING] - Response Given the values, Alice values books and balls significantly more than hats, while I value hats and balls more than books. In this round, both Alice and I have similar values for hats, but I have a much higher value for balls compared to Alice. To maximize my points, I should ensure I get as many balls as possible, even at the cost of a few hats. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:09,826][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:09,829][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:09,841][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:09,848][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:09,855][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:09,858][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:09,866][mllm.models.large_language_model_local][WARNING] - Response Given Alice's per-item values, she will likely propose to take all the books and balls herself, leaving me with the hats. To maximize my points, I should propose to take all the hats for myself, as my value for hats is 1, which is the lowest, and taking books and balls would result in a lower total value for me. Proposal: 10 hats, 0 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:09,868][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:09,871][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:09,874][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:09,876][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:09,888][mllm.models.large_language_model_local][WARNING] - Response Given the values, a strategic approach would be to take the items that both of us value highly, and split the less valued items proportionally. Since both of us value hats the most, and books and balls secondarily, it might be wise to propose a split that reflects these values while ensuring we do not overpropose on any item. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:09,894][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:09,910][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Bob place significant value on different items. You value books and balls highly, while Bob values hats more and sees books and balls as having lower value. To maximize your points, you should propose a distribution that reflects your high value for books and balls, while acknowledging Bob's higher valuation of hats. Here’s a proposal that balances maximizing your points while considering the proportional values: Proposal: 10 hats, 10 books, 10 balls This proposal assumes that each item is split proportionally, which would be fair given the values, but we can refine this slightly to ensure we capture as much value as possible from books and balls. Proposal: 5 hats, 10 books, 10 balls This proposal gives Bob some hats (which he values highly), while ensuring you get the majority of the books and balls, which you value more. This should help maximize your total points over the rounds. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:09,913][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the equal distribution of values, splitting the items evenly seems to be the most rational strategy. However, since both you and Alice value the same items (balls) highly, there might be an incentive to try to secure as many of those as possible. Nonetheless, since the values are equal and we are splitting 10 of each item, the optimal proposal would be to keep as many of each item as possible while maintaining a fair split. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:09,929][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:09,959][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:09,961][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:10,010][mllm.models.large_language_model_local][WARNING] - Response Given the new valuation by Alice, where she values hats and books at 1 and balls at 1, and you still value hats at 10, books at 1, and balls at 1, it makes sense to propose a division that maximizes your value for hats and balls while ensuring that Alice gets at least some books, as it is her only highly valued item. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:10,016][mllm.models.large_language_model_local][WARNING] - Response Given the values, books and balls are highly valued by both you and Bob, while hats are somewhat valued by both. To maximize points, it makes sense to propose a split that maximizes the value of books and balls while also considering hats. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:10,051][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the results from previous rounds, where proposing to take all items results in an even split, it might be beneficial to consider a different strategy this time. Since we both value hats and books highly but differently, and balls not at all by Bob's value, a strategic split where we each take what we value more could be more beneficial. Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:10,055][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:10,083][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:10,085][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:10,135][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:10,142][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:10,148][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:10,161][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:10,164][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:10,166][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:10,169][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:10,203][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:10,263][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, the optimal strategy is to maximize the items with higher personal value. Here, books and balls have higher values for both of us, but since my value for books is significantly higher (10) compared to my value for balls (10), and the same applies to Bob, it makes sense to propose a split that captures more of the high-value items. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:10,270][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:10,304][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Alice place significantly higher value on books and slightly higher on balls compared to hats. To maximize your points, you should propose to take as many books as possible since they have the highest value for you. Here’s a proposal that maximizes your points: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:10,385][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:10,391][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:10,399][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:10,554][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:10,561][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:10,567][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:10,573][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:10,580][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:10,805][mllm.models.large_language_model_local][WARNING] - Response Given the values, it is clear that both you and Alice have a strong preference for balls, and less interest in hats and books. However, to maximize points, we need to propose a splitting that ensures we get a significant portion of the items we value highly, while also considering Alice's preferences to avoid proposing an excessive amount that could lead to a proportional split. Proposal: 10 hats, 10 books, 10 balls This proposal ensures you get all the items you value, maximizing your points. Since Alice values books and balls as well, she might propose a similar split to avoid a proportional split where she would receive fewer items overall. Thus, this proposal seems reasonable to maximize your points in this round. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:10,809][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:10,816][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:10,819][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:10,822][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:10,861][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:10,868][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:10,875][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:11,031][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the history of the game, it seems that proposing to take all items is a reasonable strategy, as both agents are likely to do the same. However, we should consider the potential for Bob to propose a different distribution, which could affect the proportional allocation if the total proposals exceed the item quantity. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:11,038][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:11,093][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:11,096][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:11,098][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:11,117][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:11,142][mllm.models.large_language_model_local][WARNING] - Response Given the values, the optimal strategy would be to maximize the items with higher personal value while still considering Bob's values to avoid proposing more than what's fair. Since hats and books have higher values for you, it makes sense to propose keeping more of these items. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:11,145][mllm.models.large_language_model_local][WARNING] - Response Given Bob's very different valuations, where he values hats and balls much less, it might be strategic to offer him a small portion of the hats and most of the books and balls. This would maximize our own points based on our valuations. proposal: 1 hat, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:11,168][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:11,175][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:11,181][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:11,184][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:11,216][mllm.models.large_language_model_local][WARNING] - Response Given the significant disparity in values for books and balls, I should focus on claiming the items with higher value for me personally. Here’s a strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:11,218][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:11,223][mllm.models.large_language_model_local][WARNING] - Response Given the values, it seems both you and Alice have a high valuation for balls and a lower valuation for hats and books. To maximize your points, you should propose to take all the balls since they have a high value for you, and share the hats and books fairly. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:11,228][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:11,281][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, your optimal strategy would be to propose a division that maximizes your points based on your higher values for hats and balls. Since Alice values books more, it's beneficial to allocate as many hats and balls as possible to yourself while making a fair offer for books. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:11,284][mllm.models.large_language_model_local][WARNING] - Response Given the high value of books and balls for both of us, and the low value of hats, a more strategic approach might be to ensure we get a significant portion of the books and balls. Here's a revised proposal: Proposal: 10 hats, 10 books, 9 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:11,286][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:11,378][mllm.models.large_language_model_local][WARNING] - Response Given the updated values, you have a high value for hats and books, while Bob values balls highly. To maximize your points, you should propose to take most of the hats and books, while leaving the balls for Bob. Here's the proposal: Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:11,489][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the history of the game, it seems that splitting the items equally results in the highest points for both players. Therefore, I will continue to propose an equal split. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:11,497][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:11,539][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both agents would benefit from a more strategic approach to maximize points. Since you value balls at 10 and Bob values them at 1, and similarly for hats and books, it might be optimal to propose splitting the items in a way that maximizes the points based on these values. Proposal: 10 hats, 10 books, 10 balls However, considering the previous rounds and the values, a better strategy might be to propose a split that aligns more closely with the values, ensuring that you get a better share of the items you value more. Proposal: 10 hats, 10 books, 10 balls To refine this, let's consider a more strategic split: Proposal: 10 hats, 10 books, 10 balls However, the strategic split should be: Proposal: 10 hats, 10 books, 30 balls - but since the total cannot exceed 30 balls, a balanced split might be: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:11,654][mllm.models.large_language_model_local][WARNING] - Response Given the high value Alice places on books and balls, and your own significant value for balls, it's important to coordinate a splitting strategy that maximizes the points for both participants while ensuring that the total quantity of items proposed does not exceed the available items. Here’s a proposed strategy: - You should aim to take all the balls, given their high value for both of you. - Split the hats and books more evenly to avoid unnecessary proportionality determinations that could reduce your earnings if Alice takes most of them. Thus, a balanced but strategic proposal would be: Proposal: 10 hats, 5 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:11,657][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric and potentially competitive nature of this round, where both you and Alice have similar and complementary valuations for the items, it might be beneficial to propose a fair distribution that maximizes the overall points. Since Alice may also propose a competitive split, a balanced approach could be optimal. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:11,660][mllm.models.large_language_model_local][WARNING] - Response Given Alice's higher value for books and balls, and your own higher value for books and balls, a more strategic approach might involve securing more of the items you value higher. Here’s a proposal that focuses on splitting the books and balls more favorably: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:11,664][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:11,668][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:11,671][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:11,673][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:11,717][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:11,719][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:11,828][mllm.models.large_language_model_local][WARNING] - Response Given the values, Alice and I both highly value hats and books, but both value balls equally. To maximize my points, I should try to secure as many hats and books as possible while still allowing for a fair distribution of balls. Here's a strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:11,834][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and the symmetry in Bob's and your valuations for hats and books, and balls, the optimal strategy involves proposing to take the maximum number of items to maximize the points. Here's the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:11,867][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:11,873][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:11,896][mllm.models.large_language_model_local][WARNING] - Response Given the pattern in Bob's values, it seems he values hats and books highly and balls less. To maximize my points, I should try to get as many balls as possible while still proposing a fair distribution to avoid having the proposals allocated proportionally. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:11,902][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:11,905][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:11,919][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:11,923][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:11,964][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:12,051][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:12,054][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:12,073][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:12,095][mllm.models.large_language_model_local][WARNING] - Response Given the values, it’s in your best interest to capture as much value as possible from the items you highly value more than Alice does. Here, hats and balls are more valuable to you, while books are more valued by Alice. A strategic proposal would be to keep more of the items you value higher. Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:12,098][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:12,101][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:12,118][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:12,121][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:12,124][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:12,127][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:12,130][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:12,139][mllm.models.large_language_model_local][WARNING] - Response Given the values, it seems that both you and Bob have differing preferences for the items, but both of you value books highly and balls less. To maximize your points, you should propose to take the items that you value more, specifically the books. Here’s a strategic proposal: Proposal: 10 hats, 10 books, 10 balls This proposal assumes that Bob will likely mirror your proposal to avoid splitting the books. However, if Bob deviates, you can adjust your proposal accordingly. This balanced approach aims to secure the high-value books while also claiming the hats and balls to avoid leaving them entirely to Bob. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:12,142][mllm.models.large_language_model_local][WARNING] - Response Given the pattern in the previous rounds, where both Alice and Bob are proposing to take all items, which results in each receiving the same amount of points, a strategic approach would be to mirror Bob's proposal to ensure a fair split and maximize points based on the values assigned. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:12,162][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:12,203][mllm.models.large_language_model_local][WARNING] - Response Given the new per-item values, where hats are valued at 1 by me and 10 by Alice, and both books and balls are valued at 10 by me and 1 by Alice, it makes sense to propose a split that maximizes the value for me while minimizing the value for Alice. Here's the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:12,209][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:12,216][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:12,223][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:12,230][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:12,232][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:12,247][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:12,250][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:12,258][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:12,262][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:12,268][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:12,279][mllm.models.large_language_model_local][WARNING] - Response Given the values where hats and balls are highly valued by one party each, a strategic proposal could be to maximize the value for both hats and balls, while also considering the books, which have middling value for both parties. Here’s a proposal: Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:12,281][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:12,283][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:12,317][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both hats and balls are of higher value to Alice compared to me, while books are of higher value to me compared to Alice. To maximize points, I should propose a distribution that reflects these values. However, since both of us will likely propose similar strategies, we need to consider the proportional splitting rule if our totals exceed the item quantities. To optimize, I will propose to keep all the items that are valuable to me (books and balls) and a proportionate share of hats. Given Alice's higher valuation of hats, I will propose to keep a lower amount of hats. Proposal: 5 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:12,323][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and the symmetry in the game, proposing an equal split might still be a reasonable strategy. However, since Alice values books and balls much more than hats, and you value books and balls more than hats, proposing a significant amount of hats and books could yield better individual items for you while still ensuring you get some valuable balls. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:12,341][mllm.models.large_language_model_local][WARNING] - Response Given the values, both you and Bob place high value on books and balls. To maximize points, it's important to secure a significant portion of these valued items. Here's a proposal that prioritizes securing the books and balls while ensuring you get at least some hats: Proposal: 5 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:12,344][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both hats and balls are highly valued by one of the players, while books are valued equally. A cooperative strategy might be effective here to maximize the total points while ensuring neither player receives significantly less than the other. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:12,359][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Bob value books and balls significantly more than hats. However, since your values for books and balls are the same as Bob's values for hats, and vice versa, a strategic proposal would be to allocate items in a way that maximizes the value for both parties while ensuring you receive a significant portion of the highly valued items. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:12,366][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both hats and books are more valuable to me than to Alice, while balls are more valuable to Alice than to me. To maximize my points, I should try to keep as many hats and books as possible, and let Alice take the balls. Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:12,369][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:12,372][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:12,374][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:12,389][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:12,394][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:12,397][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:12,412][mllm.models.large_language_model_local][WARNING] - Response Given the new per-item values, where both you and Alice value hats and books at 1 and balls at 10, it makes sense to propose an allocation that takes into account the higher value of balls. However, since the values are the same for hats and books, we should aim to allocate these items fairly while maximizing the allocation of balls. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:12,505][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both you and Bob value books the most, with values of 10 each. Balls are valued at 1 by you and 10 by Bob, while hats are worth 1 each. To maximize your points, it's crucial to secure the books and balls since they offer the highest value. A balanced approach might be to ensure you get the majority of the books and balls, while also securing at least a few hats. Given the symmetry and the structure of the values, a strategic proposal could be: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:12,508][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:12,511][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:12,525][mllm.models.large_language_model_local][WARNING] - Response Given the current values, Alice values books and balls much higher than hats, while you value hats and balls more than books. A strategic approach would be to allocate items in a way that maximizes your value, even if it means Alice gets more of the items she values highly. Here’s the proposal: Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:12,531][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:12,534][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:12,537][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:12,552][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:12,555][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:12,558][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:12,561][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:12,575][mllm.models.large_language_model_local][WARNING] - Response Given the values, you have a strong preference for books and balls, while Bob values hats and books highly. To maximize your points, you should propose to take all the items that you value highly and share only what Bob values. Here is a strategic proposal: Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:12,578][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:12,596][mllm.models.large_language_model_local][WARNING] - Response Given the current distribution of per-item values, where both you and Alice value hats and books at 1 and balls at 10, the optimal strategy is to propose taking all of the balls since they have the highest value for both of you. This maximizes the potential points from the valuable balls while ensuring you get your fair share of the less valuable items. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:12,603][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:12,615][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it is clear that both you and Alice place high value on books and balls, while hats have a relatively low value for both of you. To maximize points, it would be beneficial to allocate as many books and balls as possible since they have higher individual values. Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:12,673][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:12,680][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:12,702][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:12,705][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:12,707][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:12,715][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:12,717][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:12,801][mllm.models.large_language_model_local][WARNING] - Response Given the current incentives and the per-item values, we should attempt to maximize the points by recognizing that we and Bob value books the most, but in different items. Since books are valued the most by both of us, we should both try to keep as many books as possible, even if it means splitting the hats and balls fairly. A good strategy might be to aim for a fair split of hats and balls while maximizing the books. Proposal: 5 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:12,805][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the high value of hats and books for both of us, it's strategic to maximize the items with higher value. Here, hats and books are valued more by both of us, while balls have a much lower value. To balance the proposal and to potentially achieve a higher allocation of high-value items, I'll propose: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:12,810][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:12,858][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:12,861][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:12,864][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:12,902][mllm.models.large_language_model_local][WARNING] - Response Given the current values, you have a high value for hats and books, while a low value for balls. Bob, on the other hand, values balls highly and has low values for hats and books. To maximize your points, it's important to ensure you get a significant amount of items you value highly. A strategic approach would be to propose a distribution that leverages your values while also considering potential outcomes if there's a conflict. Here's a proposal: Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:12,909][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:12,915][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:12,925][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric nature of the values and the rounds so far, it seems that both Alice and Bob are proposing to take all of the items in every round. This approach ensures that both agents maximize their points based on their per-item values, as long as the other agent is also proposing the same. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:12,928][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:12,930][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:12,937][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:12,940][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:12,943][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:12,945][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:12,962][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:12,964][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:13,095][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:13,098][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:13,101][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:13,118][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:13,124][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:13,131][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:13,200][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:13,207][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:13,213][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:13,220][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:13,283][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both hats and balls are valuable to me, but books are only slightly valuable. Since Bob values books significantly more, proposing to take all the balls and hats while leaving the books for Bob could be a strategic move. Here's my proposal: Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:13,286][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:13,377][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:13,497][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:13,501][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:13,543][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:13,550][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:13,557][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:13,564][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:13,571][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:13,577][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:13,580][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:13,583][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:13,586][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:13,619][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:13,622][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:13,691][mllm.models.large_language_model_local][WARNING] - Response Given the values, it seems that proposing to take all items might not be the best strategy, as Alice values books and balls quite highly while you value books and balls relatively higher. A more strategic approach would be to consider the distribution that maximizes your points based on the values. Given your per-item values for hats, books, and balls are 1, 10, and 10 respectively, and Alice's are 10, 10, and 1, a balanced approach might be to maximize the items you value highly while ensuring you don't overcommit. Proposal: 10 hats, 10 books, 10 balls However, proposing all items might not be optimal due to the distribution of values. Let's try a more strategic split: Proposal: 10 hats, 10 books, 7 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:13,697][mllm.models.large_language_model_local][WARNING] - Response Given the values, you should try to exploit Alice's high value for books and your own high value for balls. A strategic proposal could be to offer just enough hats and books to meet the quantity, while taking as many balls as possible. Here’s a proposed allocation: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:13,704][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:13,710][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:13,716][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:13,741][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:13,744][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:13,749][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:13,755][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:13,758][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:13,760][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:13,832][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:13,969][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both hats and books offer significantly higher personal values to us compared to Alice, while balls have a much higher value to Alice. We should try to maximize our items with higher personal value while still accounting for Alice's valuation. Here’s the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:14,020][mllm.models.large_language_model_local][WARNING] - Response Given the new values, it seems that both you and Bob place high value on different items. You value hats and balls highly (10), while Bob values books highly (10). To maximize your points, you should propose to keep as many of the items you value highly as possible. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:14,064][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:14,139][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both hats and balls are more valuable to you than to Bob, while books are more valuable to Bob than to you. To maximize your points, you should propose a distribution that reflects these values and ensures you get a significant share of the high-value items. Here's the optimal proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:14,142][mllm.models.large_language_model_local][WARNING] - Response Given the high value of hats for you and the high value of balls for Alice, it's important to balance the distribution to ensure neither party receives too much of the less valuable items. A strategic proposal would be to keep a moderate amount of books and distribute hats and balls more evenly. Proposal: 7 hats, 10 books, 3 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:14,145][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:14,196][mllm.models.large_language_model_local][WARNING] - Response Given the new per-item values where hats and books have much lower value for both of us compared to balls, and considering Bob values hats more than books, a more strategic approach might be to allocate more hats and balls to yourself while leaving fewer books. Here’s a proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:14,203][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:14,284][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:14,287][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:14,309][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:14,312][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:14,315][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:14,397][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's important to consider the strategic allocation to maximize points. Since both you and Bob have high values for hats and books and low values for balls, proposing a close split that respects the high values for hats and books might be optimal. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:14,401][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:14,480][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:14,483][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:14,556][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:14,559][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:14,586][mllm.models.large_language_model_local][WARNING] - Response Given the values, both you and Bob place high value on different items. You value hats and books highly, while Bob values books and balls highly. To maximize your points, you should try to get as many of the items you value highly as possible. A strategic approach would be to propose taking all the items you value the most, even though it might lead to an overload and result in proportional distribution. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:14,588][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:14,633][mllm.models.large_language_model_local][WARNING] - Response Given the asymmetric per-item values, it's important to strategically allocate the items to maximize points. Here, the books and balls have significantly higher values for you compared to Alice, while hats have a much lower value for you but a higher value for Alice. Proposal: 10 hats, 10 books, 10 balls This proposal takes advantage of your high values for books and balls while giving Alice a significant portion of the hats, which she values highly. This way, you ensure you get the maximum value from the items you highly value, and Alice gets the items she values highly. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:14,640][mllm.models.large_language_model_local][WARNING] - Response Given the new values, where you have a high value for hats and Bob has a high value for balls, it makes sense to try to maximize the items where you have the higher value. Here's the proposal: Proposal: 10 hats, 10 balls, 0 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:14,644][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:14,651][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:14,657][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:14,689][mllm.models.large_language_model_local][WARNING] - Response Given the values, it seems that both of us have complementary preferences. You value hats and books highly, while I value balls highly. A cooperative strategy could maximize our points. Since the values are complementary, we can propose splitting the items in a way that satisfies both of our value structures as much as possible. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:14,692][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:14,694][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:14,696][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:14,698][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:14,820][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Alice place high value on balls and books. Since the values are relatively balanced, a cooperative strategy might yield the best results. We should each propose to take items such that the total number of items does not exceed the available quantity and fairly distribute the items based on their values. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:14,828][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that books are highly valuable to both of us, while hats and balls are less valuable. Since we have the same per-item values and the allocation is proportional, we should ensure that both of us get a fair share of the books to maximize our points. Here's a strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:14,844][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Alice value hats and balls similarly, but you and Alice value books very differently. To maximize your points, you should propose to take most of the books since you value them highly and Alice values them little. Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:14,870][mllm.models.large_language_model_local][WARNING] - Response Given the high value of balls (10) for both of us, and the lower values for hats and books, it's important to secure as many balls as possible while still proposing a fair distribution. Here’s a strategic proposal that maximizes the points while considering the value distribution: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:14,874][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:14,876][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:14,879][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:14,882][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:14,885][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:14,888][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:14,891][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:14,929][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:14,936][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:14,943][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:14,946][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:15,013][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:15,097][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:15,437][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:15,440][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:15,443][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:15,446][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:15,449][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:15,477][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:15,484][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:15,560][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Alice have significant value for books and Alice values balls more than you do. To maximize your points, you should try to secure as many books as possible since they have the highest value for you. Here's a proposal that prioritizes securing books while still trying to get some hats and balls: Proposal: 10 hats, 10 books, 10 balls This proposal ensures you get the maximum value from books and still have some hats and balls, which will help balance out the overall points. However, to refine this, you might want to consider slightly reducing the number of balls to increase the number of books, as you value books more highly. Proposal: 5 hats, 10 books, 5 balls This proposal still secures a significant portion of books and maintains a balanced distribution of hats and balls. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:15,564][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Bob have a strong preference for books and balls, respectively. To maximize your points, you should propose to take all the books and a significant portion of the balls while leaving the hats for Bob, who values them more. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:15,571][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:15,574][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:15,587][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:15,615][mllm.models.large_language_model_local][WARNING] - Response Given the current per-item values, it's strategically beneficial to maximize the items with higher value for both parties. Since you have a higher value for hats (10) and Alice values hats lower (10), and similarly for books (10 vs 10), and balls (10 vs 1), the optimal proposal would be to take all items to maximize your points. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:15,619][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's beneficial to propose a distribution that maximizes the benefit from items valued higher by you. Here, both you and Alice value "books" the highest at 10. To ensure you get as many books as possible, while also securing some hats and balls, let's propose to take all the books and share the other items proportionally. Proposal: 5 hats, 10 books, 5 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:15,652][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:15,655][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:15,657][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:15,659][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:15,662][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:15,667][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:15,674][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:15,677][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:15,679][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:15,686][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, the optimal strategy would be to propose a division that maximizes the total points. Since you value hats at 1, books at 10, and balls at 10, and Alice values them as hats:10, books:10, and balls:1, proposing an equal split would not be optimal. Instead, you should propose to take all items you value highly and let Alice take the items she values more. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:15,804][mllm.models.large_language_model_local][WARNING] - Response Given the values, the optimal strategy would be to propose splitting the items in a way that maximizes your points based on your higher values for books and balls. Here’s the proposal: Proposal: 10 hats, 10 books, 10 balls This proposal allocates all items to you, leveraging your high values for books and balls. Since both agents have similar values for hats, dividing them equally seems fair and rational. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:15,807][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:15,814][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:15,832][mllm.models.large_language_model_local][WARNING] - Response Given the utilities, it's clear that both you and Alice value hats and books less than balls. You value hats and books at 1, while Alice values books and balls higher. Given this, a strategic proposal might allocate more items of higher value (balls) to yourself and the rest proportionally. Proposal: 10 hats, 10 books, 9 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:15,853][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Bob value the balls highly, while you value the hats and books less, and Bob values the hats and books more. In the previous rounds, proposing to take all items resulted in both of you earning an equal amount of points, which was 60 points. However, this strategic approach might not always be optimal given the distribution of values. A more strategic approach would involve taking all of the items you value highly (balls) and sharing the others in a way that maximizes your score. Since you value books and hats less, you could propose taking most of the balls while sharing the hats and books in a way that still maximizes your points. Proposal: 10 hats, 10 books, 10 balls This proposal ensures you get all the balls, which are the items you value most. However, given the symmetry in the values, it might be beneficial to slightly reduce the number of hats and books you propose to take to avoid a proportional split that could reduce your earnings if Bob proposes to take some of those items. Proposal: 9 hats, 9 books, 10 balls This proposal means you're taking 9 hats, 9 books, and all 10 balls, which should maximize your points given your per-item values. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:16,008][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Alice value hats and books more than balls. To maximize our points, we should try to take as many high-value items (hats and books) as possible. Here's a strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:16,049][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:16,052][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:16,055][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:16,057][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:16,097][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:16,179][mllm.models.large_language_model_local][WARNING] - Response Given the new per-item values, it's clear that both hats and books are less valuable to Alice compared to balls. In contrast, balls are more valuable to you but less valuable to Alice. A strategic proposal would involve giving Alice most of the balls and splitting the hats and books more evenly. Proposal: 10 hats, 10 books, 9 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:16,186][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:16,189][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:16,192][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:16,195][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:16,197][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:16,262][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:16,265][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:16,268][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:16,271][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:16,346][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values where you assign high value to books and balls, and Alice assigns high value to hats, a strategic proposal would be to allocate items in a way that maximizes your points. Here’s a proposal that balances the allocation: Proposal: 10 hats, 10 books, 10 balls This ensures that you get a mix of items that you value highly. However, if you prefer to optimize based on the specific values, you might consider a more diverse distribution. Given your values and Alice's, spreading out the items could be a good strategy: Proposal: 10 hats, 10 books, 10 balls This proposal maintains a balanced approach, ensuring you get a mix of items you value. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:16,353][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:16,356][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:16,427][mllm.models.large_language_model_local][WARNING] - Response Given the current values, it seems that both hats and balls are valuable to you, while books are more valuable to Alice. However, to maximize your points, you should propose to take as many hats and balls as possible, as they have higher values for you compared to books. Here's a strategic proposal: Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:16,434][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:16,441][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:16,504][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:16,589][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the proposed strategy in previous rounds, it's clear that proposing to take all items leads to an equal split, which may not be optimal given the varying values each of us places on different items. A more strategic approach might involve trying to maximize the value based on our individual preferences. In this round, you value hats at 1, books at 10, and balls at 1, while Bob values hats at 1, books at 1, and balls at 10. This means you should aim to keep the books and balls while letting Bob take the hats, if possible. Here’s a proposal that reflects this strategy: Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:16,591][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Bob prefer balls the most, followed by books, and hats the least. Proposing to take all items might not be optimal because it could result in proportional allocation if the total proposed exceeds the item quantity. A more strategic approach would be to slightly reduce the items we propose to ensure we get a分配比例较高的项目。以下是提议： Proposal: 10 hats, 10 books, 9 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:16,592][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:16,667][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, hats are worth the least to both of us, while balls are worth the most. Books have moderate value. To maximize my points, I should try to keep more of the items I value highly (balls) and fewer of the items I value less (hats). Since Alice values hats more than balls, and I value balls more than hats, we are likely to propose different quantities for hats and balls. I will propose to keep all the balls, all the books, and a smaller number of hats to see if I can maximize my points. Let's propose: 5 hats, 10 books, 10 balls. Proposal: 5 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:16,669][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:16,670][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:16,670][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:16,671][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:16,750][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:16,753][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:16,756][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:16,758][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:16,760][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:16,765][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:16,770][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:16,774][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:16,775][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:16,777][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:17,021][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:17,116][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:17,119][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:17,154][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and the symmetry in the game, it makes sense to propose an allocation that accounts for both the high and low value items between both of us. Here's a strategic proposal: Proposal: 10 hats, 10 books, 10 balls This proposal aims to split the items equally, ensuring a fair distribution while maximizing the value for both high-value and low-value items. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:17,158][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:17,161][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:17,164][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:17,166][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:17,169][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:17,172][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:17,183][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:17,210][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:17,237][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both hats and balls have higher values for you compared to Alice, while books are valued more by Alice. To maximize your points, you should aim to claim more hats and balls, and fewer books. Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:17,285][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:17,288][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:17,291][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:17,376][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and Bob's strategy of valuing hats and balls more, it's important to balance our proposals to ensure we get a significant portion of the books, which have the highest value for us. Here's a revised proposal: Proposal: 1 hat, 10 books, 9 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:17,420][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in both our valuation patterns (hats and books are valuable to both of us, while balls are valuable only to Bob), it makes sense to try to maximize the items that are valuable to both of us. Here is the optimal strategy: propose to take all hats and books, and leave the balls for Bob, as he values them more. Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:17,427][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, you have a significant advantage with hats and balls compared to Alice. You should propose to take the majority of the hats and balls to maximize your points, while giving Alice a significant portion of the books to avoid any proportional allocation issues. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:17,508][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and the symmetry in the game, it's clear that proposing to take all items maximizes the point gain for both players since they will likely receive the same items regardless of the split. Therefore, to maximize points, I will propose to take all of the items. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:17,559][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:17,562][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:17,566][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:17,569][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:17,572][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:17,573][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:17,573][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:17,576][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:17,578][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:17,580][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:17,583][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:17,585][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:17,637][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:17,639][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:17,644][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:17,648][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:17,652][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:17,656][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:17,660][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:17,662][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:17,663][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:17,665][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:17,667][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:17,841][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both "hats" and "books" are highly valued by me more than by Bob, while "balls" are valued more by Bob. To maximize my points, I should allocate as many hats and books to myself and as many balls to Bob. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:17,844][mllm.models.large_language_model_local][WARNING] - Response Given the values, it is beneficial to propose a distribution that maximizes the points. Since both you and Alice value hats and books highly but Alice values balls highly, a fair and strategic split would be to keep the items in a way that both parties get a reasonable amount. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:17,847][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:17,925][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:17,928][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:18,009][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, the optimal strategy is to propose a fair and balanced distribution that maximizes the total points. Since both hats and books have high values for both players, and balls have low values, it makes sense to allocate balls in a way that distributes their value evenly. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:18,013][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:18,016][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:18,019][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:18,023][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:18,027][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:18,030][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:18,092][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both you and Alice have a higher value for balls compared to hats and books. However, since both of you propose equally splitting all items, it results in a fair distribution where no one gains a significant advantage. To maximize your points, you should try to secure more of the items you value higher, which are balls. Here is a strategy to consider: - You should propose keeping all balls (10 balls) to maximize your points from balls. - Since hats and books are less valuable to you, you can propose a small number for these items, or none at all. Proposal: 10 balls, 0 hats, 0 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:18,096][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:18,183][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:18,272][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Alice place significant value on different items. You value hats and balls highly (10 each), while Alice values hats and books highly (10 each). To maximize your points, you should propose an allocation that captures the majority of the items with high value for you. Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:18,275][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:18,359][mllm.models.large_language_model_local][WARNING] - Response Given Bob's per-item values for hats=1, books=1, balls=10, it makes sense to propose an allocation that keeps the high-value items for Bob and the low-value items for yourself. Here is a proposal: proposal: 10 hats, 10 books, 10 balls However, since the books and balls are valuable for you and Bob values books and balls highly, a more strategic proposal might be to ensure you get the maximum value from the items you value highly. A revised proposal might be: proposal: 10 hats, 10 books, 0 balls This way, you ensure you get all the books and balls, which are valuable to you, and share the hats with Bob, who values them less. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:18,363][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:18,367][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:18,370][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:18,533][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:18,536][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:18,688][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:18,713][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:18,717][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:18,737][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both you and Alice value hats and balls less, while Alice values books more, and you value books less. To maximize your points, you should propose to take as many books as possible since your value for books is lower, and you should split the hats and balls equally or in a way that balances the total value you receive. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:18,822][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:18,825][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:18,829][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:18,873][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:18,877][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:18,898][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the values assigned, it seems clear that the optimal strategy might involve splitting the items to align with Bob's values as much as possible. Since Bob values balls the most and hats the most, and since I value books the most and balls second, a strategic proposal might involve giving Bob more of what he values highly to achieve a balance. Proposal: 10 hats, 10 books, 10 balls / 2 = 5 balls This proposal gives Bob more of the items he values highly (hats and balls) while still keeping some books for myself. Let's refine this further to see if we can tweak it for better results: Proposal: 10 hats, 10 books, 9 balls / 2 = 5 balls This keeps the symmetry and ensures that both agents get a fair share of the items, but slightly favoring Bob with more balls and hats. Final Proposal: 10 hats, 10 books, 5 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:18,906][mllm.models.large_language_model_local][WARNING] - Response Given the current values, it's clear that both you and Alice value hats and books relatively high, but Alice values balls more than you do. To maximize your points, you should propose to take the items that you value higher to offset Alice's higher value for balls. A strategic proposal would be to take the items you value more and leave the balls for Alice. Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:18,912][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:18,915][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:18,919][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:18,924][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:18,929][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:18,931][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:18,933][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:18,937][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:18,942][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:18,945][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:18,948][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:18,949][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:18,950][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:18,952][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:18,961][mllm.models.large_language_model_local][WARNING] - Response Given the new per-item values, where I value hats at 1, books at 10, and balls at 10, while Alice values them as hats=10, books=1, and balls=10, it would be reasonable to propose a split that maximizes my value. I should keep as many books and balls as possible, while accepting a lower quantity of hats. proposal: 3 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:19,141][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:19,143][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:19,249][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:19,292][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:19,294][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:19,299][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:19,305][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:19,309][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:19,314][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:19,318][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:19,323][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:19,325][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:19,327][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:19,331][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:19,333][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:19,335][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:19,337][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:19,340][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:19,341][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:19,490][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:19,493][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:19,498][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:19,502][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:19,520][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:19,526][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:19,529][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:19,545][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:19,547][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:19,621][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's advantageous to cooperate and split the items evenly to avoid the risk of proportional allocation if our proposed amounts exceed the total items. Here, we can propose to take all the items, maximizing our points based on our high value for balls. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:19,625][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:19,629][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:19,632][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:19,636][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:19,638][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:19,640][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:19,642][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:19,643][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:19,668][mllm.models.large_language_model_local][WARNING] - Response Given the values, it is clear that both Alice and Bob place a high value on books and a low value on balls. To maximize points, it makes sense to propose a distribution that reflects these valuations while ensuring we get a fair share. Since we both value books highly, we should try to split them as evenly as possible to avoid one of us getting an overabundance of them, which could happen if we proposed taking all. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:19,672][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:19,674][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:19,708][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:19,710][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:19,799][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's important to maximize the points based on your high value for hats and Bob's high value for balls. Here's a strategic proposal: Proposal: 10 hats, 10 books, 10 balls -> Since your value for hats is 10 and Bob's value for balls is 10, you should allocate the items to maximize your points. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:19,806][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the values of the items, a strategic proposal would be to maximize the value based on the perceived intent of the other player. Since both of us have high values for different items, it's likely that we will both propose similar distributions to maximize our individual values. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:19,809][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:19,811][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:19,848][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:19,851][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:19,854][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:19,858][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:19,874][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:19,878][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:19,881][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:19,918][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:19,940][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:19,943][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:19,946][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:19,960][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:19,964][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:19,970][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:19,975][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:19,981][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:19,984][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:19,987][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:20,016][mllm.models.large_language_model_local][WARNING] - Response Given the items and values, proposing an equal distribution seems overly generous to Alice, who values books and balls highly. To maximize my points, I should propose keeping a higher number of items that I value more. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:20,017][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:20,019][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:20,237][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:20,241][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:20,244][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:20,251][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:20,263][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:20,267][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:20,273][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:20,332][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:20,340][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:20,493][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:20,496][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:20,501][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:20,505][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:20,509][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:20,513][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:20,575][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:20,578][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:20,580][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:20,583][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:20,585][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:20,586][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:20,588][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:20,590][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:20,591][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:20,592][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:20,594][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:20,666][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both you and Alice have different but complementary preferences. You value balls the most (10 points each), while Alice values hats the most (10 points each). To maximize your points, you should propose to take all the balls, as they provide the highest value to you. Here’s a strategic proposal: Proposal: 10 hats, 10 balls, 0 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:20,669][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Alice place high value on different items. You value hats and balls highly (10), while Alice values books highly (10). Books are the least valued item by both of you, so it might be strategic to try to capture as many books as possible to maximize your points. Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:20,672][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:20,674][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:20,757][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:20,764][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:20,767][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:20,834][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:20,835][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:20,917][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:20,920][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:20,923][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:21,086][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's beneficial to propose allocating the items in a way that accounts for both of our high-value items. Here, both you and Alice value books highly, and you both value balls highly as well. However, your values for hats and Alice's for books are much lower. To maximize your points, it's wise to propose taking the books and balls since they have the highest individual values for both players. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:21,090][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that books and balls are more valuable to Alice, while hats are more valuable to you. To maximize your points, you should propose an allocation that captures the majority of the high-value items (books and balls) while ensuring the total does not exceed the available items. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:21,097][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:21,109][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:21,117][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:21,132][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:21,161][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:21,168][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:21,175][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:21,181][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:21,184][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:21,187][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:21,214][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it makes sense to propose a split that maximizes the points based on our values: - Hats: You value 1, Alice values 1. - Books: You value 1, Alice values 10. - Balls: You value 10, Alice values 1. To maximize points, you should propose to take all items you value highly and split the ones of lesser value proportionally. In this case, you should take all the balls and split the hats and books proportionally. Proposal: 5 hats, 5 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:21,216][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both you and Bob value balls the least (1 point) and hats the most (10 points) for the current round. To maximize points, we should aim to get as many high-value items as possible. Since we have the same per-item values for books (1 point), let's consider a strategy that allocates items based on their value. Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:21,322][mllm.models.large_language_model_local][WARNING] - Response Given Alice's valuation of hats and balls, and your own valuation of books, it seems strategic to bid in a way that reflects these valuations while also considering the potential for Alice to propose larger quantities in items she values highly. Here’s a proposed strategy: - Alice values hats highly (10) and balls minimally (1), so she is likely to keep more hats and balls. - You value books highly (10) and hats minimally (1), so you might want to keep more books and fewer hats. - Balls are highly valued by both of you. A balanced proposal, keeping in mind these valuations, might be: Proposal: 5 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:21,485][mllm.models.large_language_model_local][WARNING] - Response Given the values, Alice places a high value on hats and books, while you place a high value on books and balls. To maximize your points, it's important to secure the items you value highly. Here's a strategic proposal: Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:21,505][mllm.models.large_language_model_local][WARNING] - Response Given the current per-item values and the previous round's dynamics, it's clear that we should allocate more value to items that we each highly value. Since I value books (10) and balls (10) highly, and Alice values hats (10) and books (10) highly, we should try to maximize our proposals for the items we value the most. Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:21,578][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:21,582][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:21,585][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:21,587][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:21,589][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:21,592][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:21,594][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:21,596][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:21,601][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:21,606][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:21,608][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:21,610][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:21,612][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:21,615][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:21,619][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:21,624][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:21,629][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:21,632][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:21,636][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:21,641][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:21,803][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:21,807][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:21,882][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:21,929][mllm.models.large_language_model_local][WARNING] - Response Given Bob's per-item values, he places a high value on balls and a low value on books. To maximize my points, I should try to take more of the items that Bob values less (books and balls) and leave him with more hats. Here's the proposal: proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:21,937][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:21,941][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:21,967][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:21,970][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:21,973][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:21,976][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:21,978][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:21,981][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:21,984][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:22,080][mllm.models.large_language_model_local][WARNING] - Response Given the values, it appears that both you and Bob have complementary preferences for the items. To optimize points, we should try to maximize the items that are most valuable to the other person while ensuring we still get a fair share of the items that are highly valued by us. Since you value books and balls highly (10 each), and Bob values hats and balls highly (10 each), a cooperative approach might be beneficial. Here's a strategic proposal: - You get most of the books and balls. - Bob gets most of the hats and balls. A balanced proposal could be: Proposal: 10 hats, 10 books, 10 balls However, to be more strategic, you might want to slightly adjust to ensure a better split: Proposal: 5 hats, 10 books, 10 balls This way, you get a fair share of the hats while maximizing the books and balls, and Bob gets a fair share of the hats while you take most of the books and balls. Submit: Proposal: 5 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:22,084][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:22,090][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:22,094][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:22,161][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it is clear that both you and Alice place high value on different items. You value hats and balls highly, while Alice values books highly. To maximize points, it would be beneficial to propose a split that accounts for these preferences and prevents Alice from getting too many books. Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:22,414][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:22,580][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:22,665][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:22,668][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:22,670][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:22,759][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:22,844][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:22,848][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:22,852][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:22,859][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:22,865][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:22,870][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:22,873][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:22,878][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:22,883][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:22,888][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:22,890][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:22,893][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:22,896][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:23,027][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the values, it seems that proposing to take all items would lead to a proportional allocation if Bob also proposes to take all items. However, to increase the likelihood of maximizing our points, we should consider a more strategic approach to ensure that we secure a higher share of the high-value items (books and balls). Proposal: 10 hats, 10 books, 10 balls This proposal aims to secure the maximum number of high-value items, ensuring that we maximize our points from books and balls, even if a proportional allocation occurs. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:23,111][mllm.models.large_language_model_local][WARNING] - Response Given the high value that both you and Bob place on hats and books respectively, while both the items (balls) are valued low by both, proposing an equal distribution might not be optimal. However, since both agents are likely to propose the same quantity, a strategic approach would be to slightly outbid each other to ensure getting more of the high-value items. Proposal: 9 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:23,115][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and the symmetry in Bob's values, it's clear that both hats and books are more valuable to Bob than to you, while balls are more valuable to you than to Bob. A competitive strategy would be to focus on the items that are least valuable to Bob to maximize your share. Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:23,123][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:23,196][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:23,199][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:23,285][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:23,368][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:23,375][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:23,450][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:23,453][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:23,620][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both you and Alice value hats very low (1), books are moderately valued (1 for you and 10 for Alice), and balls are highly valued (10 for you and 10 for Alice). A strategic approach would be to ensure you get the items you value the most. Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:23,880][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:24,042][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it appears that both you and Alice place a higher value on different items. You value hats and balls more, while Alice values books more. However, since the total value for books is relatively low for both of you, it might be beneficial to both to split the books more evenly and keep hats and balls mostly with you to maximize your points. ProposAl: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:24,290][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Alice value books and balls more than hats. However, to ensure a fair split and maximize points, we need to consider the percentages of the values each of us has for each item type. You have: - hats=1 - books=10 - balls=10 Alice has: - hats=10 - books=10 - balls=1 Since we have 10 of each item and both of our strategies should be balanced, a fair split would be to allocate items based on their values. Here's a balanced proposal: - Hats: Since Alice values hats more, she should get slightly more hats. A fair split would be 7 for you and 3 for Alice. - Books: You value books more, so you should get more books. A fair split would be 5 for you and 5 for Alice. - Balls: Alice values balls more, so she should get more balls. A fair split would be 5 for Alice and 5 for you. So, the proposal would be: Proposal: 7 hats, 5 books, 5 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:24,841][mllm.models.large_language_model_local][WARNING] - Response Given the updated per-item values where both you and Bob value hats at 1, books at 10, and balls at 10, it's clear that both items (books and balls) are highly valued by both parties. To ensure we maximize our points, we should try to secure as many books and balls as possible while sharing hats in a way that both feel reasonably satisfied. A balanced approach would be to propose an equal distribution of hats to avoid any imbalance that could lead to a proportional split. Here’s a proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:24,897][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Bob value books and balls more than hats. However, since we are proposing the split simultaneously and the values are symmetric in the sense that both of you value books and balls higher but hats slightly lower, a strategic approach would be to propose a fair division that maximizes the value while considering the potential for strategic complementarity. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:24,940][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:25,005][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:25,008][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:25,011][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:25,015][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:25,019][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:25,245][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Alice have a valuable preference for different items. You value hats and balls highly (10), while Alice values books highly (10), and both of you value the other items less. To maximize points, it would be beneficial to propose an allocation that respects these preferences and ensures you receive items you value highly. Here’s a strategic proposal: Proposal: 10 hats, 10 books, 10 balls This proposal ensures that you get all the hats and balls, which you value highly, and you take all the books, which you value next. Alice will likely get the books she values, and the balls and hats will be split proportionally since we are proposing to take all of them. However, since Alice values books less and the balls the same as you, she might not accept this proposal. To be more strategic, you might want to propose a slightly adjusted allocation that Alice might be willing to accept. Proposal: 10 hats, 8 books, 10 balls This ensures you get all the hats and balls, and you take 8 out of 10 books, leaving 2 books for Alice. This proposal respects both your and Alice's preferences and maximizes your points while leaving some value for Alice. Submission: Proposal: 10 hats, 8 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:25,249][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Alice have a higher value for books and a lower value for hats and balls. To maximize points, it's beneficial to propose a split that allows both parties to get a significant portion of the books, as they are valued highly by both of you. Here's a proposal that balances the values: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:25,252][mllm.models.large_language_model_local][WARNING] - Response Given the values and the symmetry in the situation, proposing an equal split seems to be the most reasonable strategy to maximize points. Here is the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:25,581][mllm.models.large_language_model_local][WARNING] - Response Given the new values, where I have a higher value for hats and balls compared to Bob, and Bob has a higher value for books, a strategic approach would be to propose more of the items I value highly to ensure I get a fair share of those items, while still proposing to keep some books to secure some points from that item as well. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:25,664][mllm.models.large_language_model_local][WARNING] - Response Given the new per-item values, where both you and Alice place high value on hats and balls, and books have moderate value for both, a strategic approach would be to maximize the items with the highest individual values. Here's the proposal: Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:25,688][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:25,692][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:25,696][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:25,772][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:25,775][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:25,778][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:25,781][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:25,784][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:25,787][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:25,790][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:25,793][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:25,796][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:25,800][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:25,802][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:25,803][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:25,803][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:25,804][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:25,869][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Alice place significant value on different items. You value books highly (10) and balls moderately (1), while Alice values books moderately (10) and balls very low (1). Given these preferences, it would be strategic to propose a distribution that maximizes your points by keeping items you value more. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:25,873][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:25,896][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:25,898][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:25,902][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:25,906][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:25,924][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:25,929][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:25,944][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:26,002][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:26,212][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:26,216][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:26,219][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:26,223][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:26,241][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:26,244][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:26,247][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:26,250][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:26,253][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:26,256][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:26,260][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:26,266][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the fact that both Alice and I have the same values for hats and books but different for balls, a strategic approach would be to ensure that we both get a fair share of the items. Let's split the hats and books equally and take all the balls since my value for balls is the highest. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:26,353][mllm.models.large_language_model_local][WARNING] - Response Given the values, you have a higher value for hats (1) compared to Alice, while Alice values books and balls more. To maximize your points, it's strategic to claim as many hats as possible since they have the highest value for you. Here is a proposal: Proposal: 10 hats, 10 books, 1 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:26,361][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:26,434][mllm.models.large_language_model_local][WARNING] - Response Given the values, it seems Alice places a high value on books and balls, while you value books and hats more. A strategic approach might involve proposing to take the items that align more closely with your values. Here's a proposal that aligns with this observation: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:26,682][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:26,685][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:26,765][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:26,769][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:26,772][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:26,775][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:26,779][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:26,786][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:26,789][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:26,793][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:26,796][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:26,798][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:26,802][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:26,805][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:26,808][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:26,812][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:26,844][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the values and the fact that both you and Bob have the same values for hats and balls, and books, it is rational to assume that both you and Bob will propose to take all items to maximize your points. Therefore, proposing to take all items aligns with the optimal strategy. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:26,848][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:26,855][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:26,869][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:26,872][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:26,875][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:26,890][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:26,912][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:26,915][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:27,159][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:27,248][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that balls are the most valuable for you, followed by hats, and then books. Alice values hats and books more than balls. To maximize your points, you should propose to keep all the balls, enough hats to match Alice's value, and all the books. Here’s a strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:27,252][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:27,255][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:27,257][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:27,261][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:27,270][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's in your best interest to propose a distribution that maximizes the total points, accounting for both your and Alice's valuations. Since you value hats and books less (1) compared to Alice, and you value balls highly (10), and Alice values books highly (10) but not balls, a strategy that focuses on splitting the books and balls carefully could be beneficial. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:27,278][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:27,546][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:27,550][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:27,553][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:27,556][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:27,559][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:27,562][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:27,623][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:27,630][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:27,637][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:27,644][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:27,647][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:27,650][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:27,652][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:27,657][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:27,659][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:27,664][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:27,667][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:27,671][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:27,675][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:28,376][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:28,379][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:28,419][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:28,426][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:28,433][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:28,467][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:28,474][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:28,620][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:28,779][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:28,783][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:28,791][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:28,798][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:28,809][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:28,816][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:28,876][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:28,880][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:29,043][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:29,046][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:29,050][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:29,125][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:29,129][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:29,132][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:29,135][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:29,211][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:29,298][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:29,301][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:29,304][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:29,311][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:29,317][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:29,320][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:29,323][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:29,326][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:29,336][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:29,390][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:29,392][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:29,469][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:29,599][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:29,621][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Alice place high value on books and balls, but you value hats more than Alice does. To maximize your points, you should try to get as many books and balls as possible, while ensuring that the total allocation does not exceed the available items. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:29,624][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:29,716][mllm.models.large_language_model_local][WARNING] - Response Given the current per-item values and the pattern of previous rounds, it's clear that both Alice and I have strong preferences for different items. To maximize my points, I should aim to secure as many of my highly valued items (balls) as possible while also keeping some balance to avoid splitting more than the available items. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:29,830][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:29,834][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:29,836][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:29,839][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:29,857][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:29,860][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:29,864][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:29,867][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:29,869][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:29,872][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:29,875][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:29,894][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:29,901][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:29,951][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:29,957][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:29,964][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:29,967][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:29,974][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:29,977][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:29,980][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:29,982][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:30,003][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:30,006][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:30,008][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:30,011][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:30,014][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:30,017][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:30,020][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:30,024][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:30,027][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:30,030][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:30,032][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:30,035][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:30,037][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:30,039][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:30,040][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:30,062][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:30,064][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:30,066][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:30,147][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:30,166][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:30,268][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the game and the values, proposing the same quantities as Bob would likely lead to a fair split. However, to ensure we maximize our points, we should consider our higher valued items. Since hats and books are valued more by us, and balls are valued more by Bob, a strategic proposal might involve slightly favoring ourselves in the items we value higher. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:30,271][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:30,310][mllm.models.large_language_model_local][WARNING] - Response Given the values, it appears that books are highly valuable to Alice and balls are highly valuable to her while hats are least valued to both. Since hats have the lowest value to you and Alice, you might want to maximize your points from books and balls. Here’s a proposal that leverages this insight: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:30,347][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:30,363][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:30,366][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:30,411][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:30,412][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:30,413][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:30,428][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:30,433][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:30,435][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:30,437][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:30,442][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:30,447][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:30,453][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:30,457][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:30,462][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:30,467][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:30,483][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:30,509][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:30,513][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:30,517][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:30,519][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:30,522][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:30,564][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:30,567][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:30,570][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:30,573][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:30,575][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:30,578][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:30,579][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:30,582][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:30,585][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:30,588][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:30,592][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:30,597][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:30,599][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:30,601][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:30,601][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:30,651][mllm.models.large_language_model_local][WARNING] - Response Given the values and the fact that both you and Bob value books highly and balls minimally, a strategic proposal might involve undercutting Bob's proposals for books and balls while securing as many hats and books as possible. Here’s a revised proposal: proposal: 10 hats, 10 books, 10 balls However, to ensure a higher allocation, let's adjust the proposal slightly to focus more on securing books and balls since they are valued more by one of the players: proposal: 10 hats, 9 books, 11 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:30,653][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:30,656][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:30,723][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:30,747][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:30,764][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:30,802][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:30,805][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:30,880][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:31,042][mllm.models.large_language_model_local][WARNING] - Response Given the values, it is advantageous to propose a split that maximizes the value based on our preferences. Since both of us value books the most and balls second, we should try to ensure we get as many books as possible while also securing some balls and hats. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:31,044][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:31,127][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:31,131][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:31,375][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:31,383][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:31,442][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:31,445][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:31,537][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:31,541][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:31,620][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:31,627][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:31,633][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:31,640][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:31,643][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:31,685][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:31,689][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:31,691][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:31,694][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:31,696][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:31,698][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:31,702][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:31,706][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:31,710][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:31,715][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:31,719][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:31,722][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:31,724][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:31,756][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:31,760][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:31,764][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:31,924][mllm.models.large_language_model_local][WARNING] - Response Given the current per-item values and Bob's values, a cooperative distribution could yield better results. Since you both have significantly different values, it might be optimal to allocate items based on their values to maximize utility. Here's a proposal: Proposal: 10 hats, 10 books, 10 balls This proposal assumes that we can rely on Bob to distribute books and balls according to his higher values for those items. If Bob allocates the books and balls optimally, this split could yield a higher total value for both of us. However, if you believe Bob might not allocate optimally, a more strategic split could be: Proposal: 10 hats, 10 books, 10 balls This ensures we both get items according to our high values. Given the symmetry and the fact that we've had good results with this strategy, I'll stick with the previous proposal. If you have any specific concerns or if you'd like to split the items differently, please let me know! did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:32,092][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric nature of the per-item values and the fact that both you and Bob value the items similarly but prefer different items, it's important to propose a fair split that maximizes the points while considering potential responses. Here's a strategic approach: - You value hats (10) and balls (10) highly, while Bob values books (10) and balls (10) highly. - Proposing a fair split could be seen as cooperative and might encourage similar behavior from Bob in the future. Proposal: 10 hats, 10 books, 10 balls This proposal ensures that both you and Bob receive an equal share of all items, considering their high values. Here is the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:32,331][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and the symmetric nature of the problem, a strategic proposal would be to maximize the items that have the highest combined value for both you and Alice. Here, both you and Alice have high values for hats and books, while the value for balls is relatively low for both. Proposal: 10 hats, 10 books, 10 balls This proposal will ensure that we don't miss out on items with high mutual value. However, we can refine this by considering the specific values: - Hats: 1 (you) vs 10 (Alice) - Books: 10 (you) vs 10 (Alice) - Balls: 10 (you) vs 1 (Alice) A more balanced approach might be to propose a bit less for hats and balls to avoid the possibility of proportional allocation in case of an imbalance: Proposal: 9 hats, 10 books, 9 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:32,339][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Bob place the highest value on the balls. You value them at 10, while Bob values them at 10. Hats and books are less valuable to you, with you valuing hats at 10 and books at 1, and Bob valuing books at 1 and hats at 1. This suggests a strategic approach where we each should try to secure as many of the balls as possible, as they are the highest value items for both of us. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:32,507][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:32,510][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:32,512][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:32,513][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:32,515][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:32,527][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:32,527][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:32,528][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:32,641][mllm.models.large_language_model_local][WARNING] - Response Given that both you and Alice have high values for hats and books respectively, and both assign a low value to balls, a cooperative approach would likely maximize points. Here, proposing to split the items equally would ensure that both you and Alice get a share of items they value highly. proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:32,679][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:32,683][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:32,686][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:32,689][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:32,696][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:32,699][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:32,720][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:32,723][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:32,727][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:32,792][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:32,796][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:32,798][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:32,809][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:32,813][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:32,844][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:32,847][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:32,885][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:32,888][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:33,003][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:33,006][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:33,013][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:33,020][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:33,022][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:33,026][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:33,033][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:33,039][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:33,042][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:33,048][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and the symmetric nature of the situation, it's strategic to propose an equal split of the items to ensure both parties receive a fair share. Here, since you value books and balls more than hats, and Alice values hats more than books and balls, splitting the items equally is a good strategy. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:33,051][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the game and the values, it seems both agents are proposing to take all items each round. However, to maximize points, we should consider the values and propose a split that maximizes our points while ensuring we don't oversubscribe the items. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:33,065][mllm.models.large_language_model_local][WARNING] - Response Given the values, I should propose to take the items that I value highly to maximize my points. Since I value hats and books more than balls, and Bob values books and balls more than hats, I should take the opportunity to get as many hats and books as possible. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:33,068][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:33,071][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:33,102][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Alice have different preferences. You value hats and books lowly, while Alice values books and hats higher, and both of you value balls similarly. Proposing to take all items might lead to a fair split but doesn't fully exploit the differential values. A strategic approach would be to consider the proportional values and ensure you get a fair share. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:33,118][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:33,135][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:33,138][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:33,152][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:33,157][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:33,172][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:33,175][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:33,178][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:33,181][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:33,222][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:33,225][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:33,237][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:33,240][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:33,253][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:33,256][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:33,269][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:33,273][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:33,286][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:33,289][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:33,291][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:33,316][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:33,346][mllm.models.large_language_model_local][WARNING] - Response Given the values, it would be more strategic to propose a split that aligns with each item's relative value to both you and Bob. Since you value hats and books more and Bob values books and balls more, proposing an even split might not be ideal. Instead, you might want to propose a split that reflects the higher value of hats and books to you and the higher value of books and balls to Bob. Proposal: 10 hats, 10 books, 10 balls However, a more strategic proposal could be: Proposal: 10 hats, 10 books, 10 balls This maintains an even distribution but since we need to consider the value, a more nuanced approach could be: Proposal: 10 hats, 10 books, 10 balls But to balance both values, a more strategic suggestion could be: Proposal: 7 hats, 7 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:33,350][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:33,356][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:33,368][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:33,371][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:33,382][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:33,389][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:33,392][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 books, 10 balls, 10 hats did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:33,418][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:33,435][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:33,451][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:33,467][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:33,491][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:33,493][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:33,495][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:33,497][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:33,499][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:33,504][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:33,610][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:33,616][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:33,687][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:33,693][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:33,765][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:33,768][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:33,842][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:33,848][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:33,918][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:33,921][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:33,928][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:34,234][mllm.models.large_language_model_local][WARNING] - Response Given the values, you have a high value for hats and balls, but Bob values books the most. To maximize your points, it's crucial to secure as many of the items you value highly (hats and balls) as possible. Here’s a strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:34,237][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:34,311][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:34,373][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:34,390][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:34,427][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:34,459][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, Alice values books and balls highly, while you value hats and books highly. To maximize your points, you should try to secure more of the items you value highly. Here, you should propose to take all the hats and books, and let Alice have all the balls. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:34,462][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:34,468][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:34,476][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:34,480][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:34,486][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:34,497][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:34,633][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the high value that both you and Bob place on the same items, it's best to propose an equal split to ensure that both of you receive the items you value highly. Here is a balanced proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:34,740][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:34,743][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:34,746][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:34,807][mllm.models.large_language_model_local][WARNING] - Response Given the high value of hats and books for you and Alice's high value for balls, a competitive strategy would be to propose an allocation that maximizes the high-value items first. Here's a proposal that focuses on the high-value items while considering the split for balls: proposal: 10 hats, 10 books, 5 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:34,854][mllm.models.large_language_model_local][WARNING] - Response Given the valuation of both you and Alice, a cooperative approach might be beneficial. Since both of you value the same items (hats and books) highly, it makes sense to address this simultaneously. Here's a proposal that evenly splits the items while maximizing the value: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:34,858][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:34,870][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:34,873][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:34,887][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that Bob values books and balls significantly more than hats, while you value books and balls less but still consider hats to be of some value. To maximize your points, you should try to secure more of the items you value highly (books and balls) while still getting some value from hats. A balanced approach would be to propose slightly more of the items you value highly while ensuring the total doesn't exceed the available quantity. Here's a proposal: proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:34,890][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:34,903][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the fact that both Alice and I have the same value for the same items, proposing an equal distribution will likely maximize the points for both of us. Therefore, I propose: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:34,918][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:34,921][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:34,934][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both you and Bob value hats and books more than balls. However, since the item quantities are the same and the values are such that both of you would prefer to take as many hats and books as possible, let's propose an allocation that maximizes the value for you. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:34,978][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:34,991][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:34,994][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:34,997][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:35,004][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:35,009][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both hats and balls are most valuable to you and Bob values books highly. To ensure we maximize our points, we should propose分配方式，尽量争取我们价值较高的物品。考虑到我对帽子和球的价值较高，可以尝试争取尽可能多的这些物品。 Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:35,012][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:35,014][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:35,041][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:35,044][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:35,057][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:35,060][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:35,071][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:35,085][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the fact that both you and Bob are likely to follow a similar strategy, the optimal proposal would be to allocate the items in a way that both parties feel the allocation is fair. Since your per-item values are similar to Bob's, a fair proposal would be to split all items evenly. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:35,130][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:35,137][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:35,146][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:35,157][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:35,172][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:35,186][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:35,189][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:35,201][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:35,242][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:35,256][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:35,259][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:35,270][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:35,276][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:35,279][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:35,287][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Alice value the balls highly, but you value them the most. However, proposing to take all 10 balls might not be the best strategy, as Alice might counter by taking all the hats and books, which she values more than you do. A more balanced approach might yield better results. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:35,316][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:35,319][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:35,332][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:35,344][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:35,474][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:35,567][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:35,576][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:35,583][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:35,592][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:35,611][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:35,660][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:35,666][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:35,693][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:35,762][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:35,765][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:35,816][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:35,819][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:35,822][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:35,834][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:35,864][mllm.models.large_language_model_local][WARNING] - Response Given the values: - You value hats at 1, books at 1, and balls at 10. - Bob values hats at 1, books at 10, and balls at 1. To maximize your points, it's beneficial to get as many items with high value to you as possible. Since you value books and balls more highly, and you are likely to receive a proportionate share if the total proposed amounts exceed the available items, the optimal strategy is to propose the maximum possible items for books and balls, and a minimum for hats. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:35,868][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both you and Alice place the highest value on books and balls, with you valuing hats the least. To maximize your points, you should focus on acquiring as many books and balls as possible, while ensuring the total quantity proposed does not exceed the available items. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:35,907][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:35,910][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:35,922][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:35,989][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:36,067][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:36,070][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:36,118][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:36,121][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:36,123][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:36,132][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:36,159][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:36,184][mllm.models.large_language_model_local][WARNING] - Response Given the values, Alice heavily values hats and books, while you value balls highly. To maximize your points, it's strategic to propose a distribution that reflects your high value for balls while still accounting for the potential proportionate split if the total exceeds the items available. Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:36,190][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:36,198][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:36,201][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:36,204][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:36,213][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:36,216][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:36,219][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:36,230][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:36,242][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:36,304][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:36,310][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:36,319][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Alice place high value on hats and balls but differ slightly on books. To optimize your score, you should claim your highest valued items. Here, both you and Alice value hats and balls similarly, and books are valued the same way by Alice but not by you. Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:36,356][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:36,363][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:36,370][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:36,378][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:36,427][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:36,442][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the values, it seems proposing an equal split is optimal. However, to ensure I maximize points, I should consider the values each item brings to both of us. Books are highly valuable to both of us, so I should propose to take more books to maximize my points, while still proposing a fair split for hats and balls to avoid any proportional distribution if Bob takes a similar approach. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:36,449][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:36,452][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:36,459][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:36,479][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:36,485][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:36,488][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:36,493][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:36,608][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:36,699][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values for this round, where both you and Alice value hats and books highly and balls less so, a competitive and strategic proposal would be to try to maximize the items that you and Alice both value. However, since you both propose at the same time and will likely want to secure items you value, it's important to anticipate Alice's likely move. A balanced strategy might be to propose slightly less than the total available to ensure a proportional share is maintained, while still attempting to maximize your points. Proposal: 9 hats, 9 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:36,729][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:36,755][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:36,772][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:36,776][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:36,786][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:36,793][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:36,799][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:36,861][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:37,164][mllm.models.large_language_model_local][WARNING] - Response Given the mismatch in valuations and the potential strategic advantage in claiming items highly valued by Bob, a smart proposal might involve taking the balls and some of the books to leverage Bob's higher valuation for those items. Here's the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:37,347][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:37,350][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:37,405][mllm.models.large_language_model_local][WARNING] - Response Given the pattern of the game, it seems that proposing to take all items is leading to an equal split because Alice is proposing the same thing, and the values are such that the total proposal does not exceed the item quantity. However, to try a different approach and potentially maximize the points, we should consider the value each item brings to both players. Since your value for hats and books is low (1), while your value for balls is high (10), and Alice's value for balls is also 1, the ball items are likely to be the most valuable to both players. Here's a strategy: you could propose taking all the balls and split the hats and books equally. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:37,522][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:37,525][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:37,528][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:37,530][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:37,600][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:37,603][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:37,612][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:37,634][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the fact that proposing to take all items is yielding the same points for both of us in each round, it seems optimal to continue this strategy. However, to explore the possibility of outmaneuvering Bob or adapting to his strategy, we might consider a slightly different approach. Let's try to split the items more evenly while still capitalizing on our higher values for books and balls. Proposal: 10 hats, 5 books, 15 balls (Note: Since we can only propose up to 10 of each item, we'll propose 10 balls instead of 15) So, the proposal is: 10 hats, 5 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:37,641][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:37,657][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:37,668][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:37,725][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:37,728][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:37,739][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:37,742][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:37,807][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:37,813][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:37,822][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:37,844][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and the symmetric nature of the game where both players have the same strategy, it makes sense to propose an even split as we did previously. However, let's consider the specific values to maximize utility: - You value hats at 1, books at 10, and balls at 1. - Bob values hats at 10, books at 1, and balls at 1. A strategic split would be to consider the proportional value each item brings to both of us. Since each of us has items valued at 10 for the other's less preferred items, it's beneficial to at least take a fair share. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:37,856][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:37,874][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:37,883][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:37,995][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:38,008][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:38,015][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:38,204][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:38,295][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:38,442][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:38,529][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:38,532][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:38,573][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:38,586][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:38,651][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:38,672][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:38,733][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:38,744][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:38,747][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:38,766][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:38,777][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:38,841][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:38,863][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:38,934][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:38,946][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:38,953][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:38,980][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:39,051][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:39,136][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:39,391][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:39,402][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:39,406][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:39,556][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both you and Alice have a significantly higher value for books compared to the other items. Since the values are symmetrical, it's likely that proposing all items will lead to an equal split. However, to maximize the points, we should consider proposing slightly less to avoid the items being allocated proportionally, which could reduce the total points. proposal: 9 hats, 10 books, 9 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:39,567][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:39,578][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:39,581][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:39,742][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:39,753][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:39,760][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:41,021][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both you and Alice value hats and books more highly than balls. A competitive and fair strategy would be to propose a split that reflects these values, ensuring neither of you gets significantly less than the other in terms of high-value items. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:41,022][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the situation and Bob's different valuation, let's try to leverage the difference in our valuations. Since Bob values hats and balls higher than books, and you value books higher than hats and balls, it might be beneficial to propose a split that allows us to take advantage of these valuations. Proposal: 10 hats, 10 balls, 10 books This proposal assumes that we both take all the items, as it's a fair split based on the per-item values. However, let's consider if we can devise a strategy that benefits us more by taking into account Bob's valuation. Given Bob's high valuation of hats and balls and our high valuation of books, we might want to propose a split where we take more books and Bob takes more hats and balls. Proposal: 10 hats, 10 books, 0 balls This would mean Bob gets all the balls, and we get all the hats and books. Let's calculate the earnings for this proposal: - You get 10 hats and 10 books. - Value of hats (10 hats * 1) = 10 - Value of books (10 books * 10) = 100 - Total value = 110 - Bob gets 10 balls. - Value of balls (10 balls * 10) = 100 This proposal would give you a higher score, so: Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:41,185][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:41,353][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:41,756][mllm.models.large_language_model_local][WARNING] - Response Given the specific values and Bob's strategy in previous rounds, it seems that he is trying to capture the majority of high-value items. To counter this and maximize my points, I should propose taking the high-value books and balls, while offering a minimal amount of hats. Here's a revised proposal: proposal: 1 hat, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:11:42,752][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, a strategic approach would be to propose in a way that maximizes your own value while considering Bob's likely strategy. Since you have a higher value for hats and balls compared to Bob, and a higher value for books compared to Bob, you might want to propose a split that aligns with these values while also considering that Bob might propose an equal split to ensure he gets some of the items he values. proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:11:43,152][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:44,397][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it is clear that both you and Bob value hats and balls relatively highly, but Bob values books more than you do. To maximize your points, you should try to get as many of your highly valued items (hats and balls) as possible while also securing a significant portion of the books since they have moderate value for both of you. Here is a strategic proposal: - You value hats and balls highly, so you should aim to take 10 of each. - Since Bob values books more, you should take fewer books, but still take enough to prevent Bob from getting all of them. A balanced proposal could be: proposal: 10 hats, 5 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:45,552][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and Bob's likely strategy, it seems that both of us are prioritizing the books and balls over hats. However, to maximize our utility, we should consider proposing a more strategic split that takes into account the relative values each of us places on the items. Here, I'll propose a split that values the items based on their respective worth to us. proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:46,402][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the fact that both agents are proposing to take all items, it appears that dividing the items equally maximizes the points for both agents. Therefore, I will continue to propose an equal distribution. proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:47,375][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric values and the previous rounds, it seems that proposing the entire allocation might not be optimal, as both agents are likely to maximize their points in the same way. Given the values, I will propose a strategy to ensure I get a fair share of the items based on their perceived value. proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:11:48,079][__main__][INFO] - Number of regex retries in iteration 575: 2481 [2025-11-13 07:11:48,080][__main__][INFO] - agents played in iteration 575 are Alice, Bob, Alice_buffer, Bob_buffer [2025-11-13 07:11:48,967][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.62%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 07:11:48,992][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.62%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 07:11:49,016][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.62%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 07:11:49,037][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation, ΔVRAM % (total): 0.00%, Current % of VRAM taken: 52.62%, Block Peak % of device VRAM: 19.53%, ΔTime: 00:00:00 [2025-11-13 07:11:49,038][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. [2025-11-13 07:11:49,038][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. [2025-11-13 07:11:49,770][mllm.training.trainer_common][INFO] - Processing mini-batch 0 of 64 [2025-11-13 07:11:50,218][mllm.training.trainer_common][INFO] - Processing mini-batch 1 of 64 [2025-11-13 07:11:50,711][mllm.training.trainer_common][INFO] - Processing mini-batch 2 of 64 [2025-11-13 07:11:51,212][mllm.training.trainer_common][INFO] - Processing mini-batch 3 of 64 [2025-11-13 07:11:51,702][mllm.training.trainer_common][INFO] - Processing mini-batch 4 of 64 [2025-11-13 07:11:52,195][mllm.training.trainer_common][INFO] - Processing mini-batch 5 of 64 [2025-11-13 07:11:52,692][mllm.training.trainer_common][INFO] - Processing mini-batch 6 of 64 [2025-11-13 07:11:53,186][mllm.training.trainer_common][INFO] - Processing mini-batch 7 of 64 [2025-11-13 07:11:53,690][mllm.training.trainer_common][INFO] - Processing mini-batch 8 of 64 [2025-11-13 07:11:54,187][mllm.training.trainer_common][INFO] - Processing mini-batch 9 of 64 [2025-11-13 07:11:54,686][mllm.training.trainer_common][INFO] - Processing mini-batch 10 of 64 [2025-11-13 07:11:55,201][mllm.training.trainer_common][INFO] - Processing mini-batch 11 of 64 [2025-11-13 07:11:55,702][mllm.training.trainer_common][INFO] - Processing mini-batch 12 of 64 [2025-11-13 07:11:56,204][mllm.training.trainer_common][INFO] - Processing mini-batch 13 of 64 [2025-11-13 07:11:56,712][mllm.training.trainer_common][INFO] - Processing mini-batch 14 of 64 [2025-11-13 07:11:57,213][mllm.training.trainer_common][INFO] - Processing mini-batch 15 of 64 [2025-11-13 07:11:57,719][mllm.training.trainer_common][INFO] - Processing mini-batch 16 of 64 [2025-11-13 07:11:58,218][mllm.training.trainer_common][INFO] - Processing mini-batch 17 of 64 [2025-11-13 07:11:58,720][mllm.training.trainer_common][INFO] - Processing mini-batch 18 of 64 [2025-11-13 07:11:59,225][mllm.training.trainer_common][INFO] - Processing mini-batch 19 of 64 [2025-11-13 07:11:59,729][mllm.training.trainer_common][INFO] - Processing mini-batch 20 of 64 [2025-11-13 07:12:00,229][mllm.training.trainer_common][INFO] - 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Processing mini-batch 32 of 64 [2025-11-13 07:12:07,638][mllm.training.trainer_common][INFO] - Processing mini-batch 33 of 64 [2025-11-13 07:12:08,141][mllm.training.trainer_common][INFO] - Processing mini-batch 34 of 64 [2025-11-13 07:12:08,643][mllm.training.trainer_common][INFO] - Processing mini-batch 35 of 64 [2025-11-13 07:12:09,147][mllm.training.trainer_common][INFO] - Processing mini-batch 36 of 64 [2025-11-13 07:12:09,643][mllm.training.trainer_common][INFO] - Processing mini-batch 37 of 64 [2025-11-13 07:12:10,144][mllm.training.trainer_common][INFO] - Processing mini-batch 38 of 64 [2025-11-13 07:12:10,643][mllm.training.trainer_common][INFO] - Processing mini-batch 39 of 64 [2025-11-13 07:12:11,142][mllm.training.trainer_common][INFO] - Processing mini-batch 40 of 64 [2025-11-13 07:12:11,641][mllm.training.trainer_common][INFO] - Processing mini-batch 41 of 64 [2025-11-13 07:12:12,139][mllm.training.trainer_common][INFO] - Processing mini-batch 42 of 64 [2025-11-13 07:12:12,638][mllm.training.trainer_common][INFO] - Processing mini-batch 43 of 64 [2025-11-13 07:12:13,136][mllm.training.trainer_common][INFO] - Processing mini-batch 44 of 64 [2025-11-13 07:12:13,634][mllm.training.trainer_common][INFO] - Processing mini-batch 45 of 64 [2025-11-13 07:12:14,133][mllm.training.trainer_common][INFO] - Processing mini-batch 46 of 64 [2025-11-13 07:12:14,631][mllm.training.trainer_common][INFO] - Processing mini-batch 47 of 64 [2025-11-13 07:12:15,128][mllm.training.trainer_common][INFO] - Processing mini-batch 48 of 64 [2025-11-13 07:12:15,621][mllm.training.trainer_common][INFO] - Processing mini-batch 49 of 64 [2025-11-13 07:12:16,118][mllm.training.trainer_common][INFO] - Processing mini-batch 50 of 64 [2025-11-13 07:12:16,613][mllm.training.trainer_common][INFO] - Processing mini-batch 51 of 64 [2025-11-13 07:12:17,115][mllm.training.trainer_common][INFO] - Processing mini-batch 52 of 64 [2025-11-13 07:12:17,614][mllm.training.trainer_common][INFO] - Processing mini-batch 53 of 64 [2025-11-13 07:12:18,107][mllm.training.trainer_common][INFO] - Processing mini-batch 54 of 64 [2025-11-13 07:12:18,601][mllm.training.trainer_common][INFO] - Processing mini-batch 55 of 64 [2025-11-13 07:12:19,107][mllm.training.trainer_common][INFO] - Processing mini-batch 56 of 64 [2025-11-13 07:12:19,602][mllm.training.trainer_common][INFO] - Processing mini-batch 57 of 64 [2025-11-13 07:12:20,099][mllm.training.trainer_common][INFO] - Processing mini-batch 58 of 64 [2025-11-13 07:12:20,595][mllm.training.trainer_common][INFO] - Processing mini-batch 59 of 64 [2025-11-13 07:12:21,092][mllm.training.trainer_common][INFO] - Processing mini-batch 60 of 64 [2025-11-13 07:12:21,598][mllm.training.trainer_common][INFO] - Processing mini-batch 61 of 64 [2025-11-13 07:12:22,100][mllm.training.trainer_common][INFO] - Processing mini-batch 62 of 64 [2025-11-13 07:12:22,601][mllm.training.trainer_common][INFO] - Processing mini-batch 63 of 64 [2025-11-13 07:12:23,100][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 10959 tokens. [2025-11-13 07:12:23,990][mllm.training.trainer_common][INFO] - For task: Apply reinforce step, ΔVRAM % (total): 14.19%, Current % of VRAM taken: 59.64%, Block Peak % of device VRAM: 62.44%, ΔTime: 00:00:34 [2025-11-13 07:12:24,645][mllm.training.trainer_common][INFO] - Saved main optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/policy_optimizer_state.pt [2025-11-13 07:12:24,647][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/critic_optimizer_state.pt [2025-11-13 07:12:24,649][mllm.training.trainer_common][INFO] - Saved trainer state to /scratch/m/muqeeth/llm_negotiation/2025_11/no_press_10_1_ties_ad_align_nocurrtimestep_seed1111/seed_1111/agent_trainer/trainer_annealing_state.pkl [2025-11-13 07:12:25,514][__main__][INFO] - Iteration 576 took 1m 51s (66.53% Gen, 32.70% Train). Generation: 1m 14s, Training: 36s. Estimated remaining time: 84h 9m 16s. Estimated total time: 93h 12m 0s. Time estimates for 10 more iterations: 18m 38s, 100 more iterations: 3h 6m 24s, 500 more iterations: 15h 32m 0s. [2025-11-13 07:12:25,518][__main__][INFO] - Starting iteration 576. [2025-11-13 07:12:26,021][__main__][INFO] - Inference policies count is regular policies 2 and buffer policies 57 and human policies 1. [2025-11-13 07:12:26,022][__main__][INFO] - Hard coded buffer agents are set to False with prob 0 [2025-11-13 07:12:46,280][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,283][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,304][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,306][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,309][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,311][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,314][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,316][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,319][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,321][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,323][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,324][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,330][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,332][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,333][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,335][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,336][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,338][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,339][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,341][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,342][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,344][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,345][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,347][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,350][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,352][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,360][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,362][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,364][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,366][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,368][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,369][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,371][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,373][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,375][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,377][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,379][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,381][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,382][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,384][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,386][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,388][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,389][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,391][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,393][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,395][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,397][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,398][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,401][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,402][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,404][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,406][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,408][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,409][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,420][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,422][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,424][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,425][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,426][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,426][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,429][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,431][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,432][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,434][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,435][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,437][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,438][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,439][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,441][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,442][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,444][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,445][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,447][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,448][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,450][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,451][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,453][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,454][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,456][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,457][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,459][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,460][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,462][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,463][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,465][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,466][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,468][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,469][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,471][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,472][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,472][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,475][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,477][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,479][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,480][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,482][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,483][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:46,598][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,600][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,618][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,620][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,623][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,625][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,627][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,630][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,633][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,641][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,643][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,646][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,648][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,650][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,653][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,655][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,657][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,660][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,662][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,665][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,667][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,669][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,672][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,684][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,686][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,688][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,690][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,693][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,695][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,697][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,699][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,701][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,704][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,707][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,709][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,711][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,713][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,715][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,717][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,719][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,722][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,737][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,739][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,741][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,742][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,744][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,746][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,748][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,749][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,751][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,753][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,755][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,757][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,759][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,761][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,763][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,765][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,767][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,769][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,771][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,773][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,775][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,777][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,779][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,781][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,783][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,785][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,787][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,789][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,791][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,793][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,795][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,796][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,810][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,812][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,814][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,815][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,817][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,819][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,821][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,822][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,824][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,826][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,827][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,829][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,831][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,833][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,835][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,837][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,838][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,840][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,842][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,844][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,845][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,847][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,849][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,850][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:46,878][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:46,914][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:46,933][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:46,938][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:46,943][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:46,949][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:46,954][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:46,959][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:46,964][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:46,969][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:46,974][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:46,979][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:46,984][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:46,989][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:46,993][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:46,998][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:47,002][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:47,006][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:47,022][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:47,026][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:47,030][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:47,034][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:47,038][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:47,042][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:47,045][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:47,049][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:47,053][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:47,056][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:47,060][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:47,063][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:47,067][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:47,071][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:47,074][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:47,077][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:47,081][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:47,084][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:47,087][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:47,091][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:47,094][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:47,097][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:47,101][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:47,112][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:47,115][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:47,118][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:47,121][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:47,124][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:47,127][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:47,130][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:47,133][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:47,136][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:47,139][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:47,143][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:47,146][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:47,149][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:47,152][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:47,154][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:47,157][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:47,160][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:47,163][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:47,166][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:47,168][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:47,171][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:47,174][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:47,176][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:47,179][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:47,182][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:47,185][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:47,188][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:47,190][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:47,193][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:47,196][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:47,198][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:47,201][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:47,209][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:47,212][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:47,214][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:47,217][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:47,219][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:47,222][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:47,224][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:47,227][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:47,229][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:47,232][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:47,234][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:47,237][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:47,239][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:47,242][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:47,244][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:47,247][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:47,249][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:47,251][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:47,254][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:47,256][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:47,259][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:47,261][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:47,264][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:47,266][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:48,128][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,194][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,196][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,220][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,223][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,226][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,228][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,247][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,250][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,252][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,255][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,267][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,269][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,272][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,274][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,290][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,313][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,316][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,318][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,321][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,323][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,326][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,341][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,344][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,347][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,350][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,359][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,361][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,362][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,363][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,365][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,389][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,391][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,394][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,396][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,399][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,401][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,404][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,418][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,421][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,424][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,426][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,446][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,448][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,451][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,453][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,471][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,474][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,477][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,479][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,481][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,483][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,485][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,487][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,490][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,492][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,501][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,503][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,505][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,507][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,508][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,510][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,512][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,522][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,524][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,526][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,528][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,530][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,531][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,534][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,536][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,544][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,547][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,548][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,550][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,552][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,554][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,556][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,558][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,567][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,569][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,571][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,572][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,573][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,575][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,577][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,579][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,581][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,582][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,584][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,586][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,591][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,593][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,595][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,596][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,598][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,599][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,601][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:48,605][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,624][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,626][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,640][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,642][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,644][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,646][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,656][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,658][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,661][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,676][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,678][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,680][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,682][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,684][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,690][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,709][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,711][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,714][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,716][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,718][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,720][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,727][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,730][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,732][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,734][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,748][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,750][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,752][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,755][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,758][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,767][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,769][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,772][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,773][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,774][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,775][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,776][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,787][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,790][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,792][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,794][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,804][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,807][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,809][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,811][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,822][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,825][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,826][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,827][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,831][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,832][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,834][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,840][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,842][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,844][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,846][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,847][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,849][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,850][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,852][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,854][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,860][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,862][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,864][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,866][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,868][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,870][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,872][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,873][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,879][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,882][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,883][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,885][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,887][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,889][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,890][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,892][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,904][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,905][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,907][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,909][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,911][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,913][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,915][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,916][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,924][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,926][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,928][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,930][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,932][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,933][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,935][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,937][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,944][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,946][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,948][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,950][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:48,951][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:48,960][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:48,965][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:48,979][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:48,983][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:48,987][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:48,991][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:48,998][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:49,002][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:49,006][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:49,014][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:49,018][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:49,021][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:49,034][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:49,038][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:49,042][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:49,053][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:49,058][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:49,062][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:49,065][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:49,073][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:49,077][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:49,081][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:49,084][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:49,088][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:49,091][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:49,097][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:49,101][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:49,104][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:49,108][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:49,119][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:49,123][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:49,126][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:49,130][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:49,134][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:49,137][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:49,141][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:49,144][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:49,150][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:49,153][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:49,157][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:49,160][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:49,165][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:49,169][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:49,172][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:49,175][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:49,186][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:49,190][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:49,193][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:49,207][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:49,210][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:49,214][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:49,217][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:49,220][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:49,224][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:49,227][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:49,231][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:49,234][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:49,240][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:49,243][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:49,246][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:49,249][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:49,252][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:49,255][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:49,259][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:49,262][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:49,265][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:49,270][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:49,274][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:49,277][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:49,280][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:49,283][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:49,286][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:49,289][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:49,292][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:49,295][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:49,298][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:49,301][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:49,308][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:49,314][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:49,318][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:49,321][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:49,324][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:49,327][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:49,332][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:49,335][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:49,338][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:49,341][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:49,343][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:49,346][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:49,349][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:49,354][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:49,357][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:49,359][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:49,362][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:49,365][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:49,368][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:49,371][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:50,302][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:50,303][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:50,305][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:50,475][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:50,478][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:50,481][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:50,501][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:50,504][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:50,506][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:50,509][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:50,530][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:50,533][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:50,536][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:50,554][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:50,579][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:50,581][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:50,584][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:50,586][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:50,589][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:50,591][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:50,603][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:50,606][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:50,609][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:50,612][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:50,615][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:50,631][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:50,634][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:50,637][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:50,639][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:50,642][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:50,644][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:50,655][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:50,658][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:50,661][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:50,663][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:50,682][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:50,684][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:50,687][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:50,690][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:50,692][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:50,702][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:50,705][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:50,707][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:50,709][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:50,711][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:50,724][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:50,727][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:50,747][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:50,750][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:50,753][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:50,755][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:50,770][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:50,773][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:50,775][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:50,778][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:50,780][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:50,783][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:50,786][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:50,788][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:50,790][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:50,799][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:50,801][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:50,804][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:50,806][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:50,809][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:50,811][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:50,813][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:50,816][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:50,818][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:50,820][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:50,823][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:50,825][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:50,833][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:50,836][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:50,838][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:50,840][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:50,842][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:50,844][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:50,847][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:50,849][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:50,856][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:50,858][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:50,861][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:50,863][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:50,870][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:50,872][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:50,874][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:50,876][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:50,878][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:50,880][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:50,882][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:50,884][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:50,886][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:50,908][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:50,928][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:50,931][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:50,933][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:50,944][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:50,947][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:50,950][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:50,962][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:50,980][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:50,983][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:50,985][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:50,988][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:50,997][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:51,000][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:51,002][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:51,005][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:51,007][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:51,010][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:51,018][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:51,021][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:51,023][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:51,025][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:51,027][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:51,040][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:51,042][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:51,045][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:51,048][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:51,060][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:51,063][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:51,065][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:51,078][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:51,080][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:51,083][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:51,086][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:51,088][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:51,091][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:51,093][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:51,100][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:51,103][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:51,112][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:51,115][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:51,117][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:51,119][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:51,132][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:51,134][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:51,137][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:51,139][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:51,141][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:51,149][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:51,151][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:51,153][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:51,155][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:51,157][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:51,159][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:51,161][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:51,168][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:51,170][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:51,172][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:51,174][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:51,176][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:51,178][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:51,180][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:51,182][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:51,191][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:51,193][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:51,195][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:51,197][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:51,199][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:51,201][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:51,204][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:51,206][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:51,212][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:51,214][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:51,216][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:51,219][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:51,221][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:51,223][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:51,232][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:51,235][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:51,237][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:51,239][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:51,241][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:51,246][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:51,251][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:51,260][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:51,272][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:51,277][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:51,282][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:51,290][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:51,294][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:51,298][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:51,306][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:51,328][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:51,332][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:51,336][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:51,344][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:51,349][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:51,353][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:51,356][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:51,366][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:51,370][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:51,374][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:51,378][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:51,382][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:51,385][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:51,392][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:51,396][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:51,400][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:51,413][mllm.models.large_language_model_local][WARNING] - Response Given the new per-item values, where my values for hats and balls are higher and Bob values books higher, a strategic approach would be to maximize the items where I have the highest value. Here's the proposed split: Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:51,414][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:51,418][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:51,422][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:51,426][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:51,432][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:51,436][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:51,459][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:51,462][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:51,466][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:51,470][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:51,474][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:51,480][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:51,484][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:51,488][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:51,491][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:51,495][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:51,502][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:51,505][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:51,509][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:51,512][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:51,527][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:51,530][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:51,534][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:51,538][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:51,541][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:51,545][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:51,552][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:51,555][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:51,559][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:51,562][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:51,565][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:51,569][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:51,572][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:51,578][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:51,581][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:51,585][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:51,588][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:51,591][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:51,594][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:51,598][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:51,603][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:51,606][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:51,609][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:51,612][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:51,615][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:51,618][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:51,621][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:51,624][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:51,627][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:51,632][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:51,635][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:51,638][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:51,641][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:51,644][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:51,696][mllm.models.large_language_model_local][WARNING] - Response Given the new values, where both you and Bob have high values for hats and books but lower values for balls, it's clear that we should prioritize these high-value items. Here's a strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:52,075][mllm.models.large_language_model_local][WARNING] - Response Given the new values, where you have a high valuation for books and Alice has a high valuation for hats, it is beneficial to propose a strategy that maximizes your points by focusing on the items you value more. Here’s a revised proposal: Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:52,078][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:52,117][mllm.models.large_language_model_local][WARNING] - Response Given the new per-item values, I can propose a more strategic allocation. Since both hats and books have higher values for Bob, and balls have the highest value for me, I should aim to take as many balls as possible while ensuring that I do not overvalue an item that Bob values more. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:52,205][mllm.models.large_language_model_local][WARNING] - Response Given the values, both you and Alice place high value on hats and books, while balls have lower values for both of you. Since we aim to maximize our points, we should propose a allocation that takes advantage of our higher values for hats and books, while still considering Alice's preferences. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:52,207][mllm.models.large_language_model_local][WARNING] - Response Given the updated values where both you and Alice have high values for books and balls, a strategic approach would be to cooperate and maximize the points for the items we both value highly. Here’s a proposal that splits the items fairly: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:52,570][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:52,573][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:52,662][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:52,665][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:52,712][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:52,716][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:52,738][mllm.models.large_language_model_local][WARNING] - Response Given the updated values where my preference for balls is now equal to Bob's and my value for hats and books is relatively lower compared to Bob's, a strategic approach would be to recognize that Bob values balls the most, and I should allocate them accordingly. Here's a revised proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:52,741][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:52,744][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:52,747][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:52,789][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:52,818][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:52,821][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:52,824][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:52,826][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:52,829][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:52,832][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:52,847][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:52,870][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in our per-item values and Bob's, proposing to take all items would likely result in a proportional split, maximizing the points for both of us. Here is my proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:52,877][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:52,880][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:52,882][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:52,897][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:52,900][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:52,903][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:52,905][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:52,908][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:52,910][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:52,922][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:52,925][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:52,947][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:52,949][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:52,955][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:52,958][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:52,974][mllm.models.large_language_model_local][WARNING] - Response Given the updated values, you have a high value for books and balls, while Alice values hats and balls more. To maximize your points, you should propose to take all items that you value highly while ensuring the total quantity of items you propose does not exceed 30 (10 hats + 10 books + 10 balls). Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:52,977][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:52,980][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:52,982][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:52,985][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:52,988][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:52,998][mllm.models.large_language_model_local][WARNING] - Response Given the new per-item values and understanding that both Alice and Bob value different items heavily, a strategic proposal would be to consider our relative valuations of the items. Since both Alice and Bob value balls the least, and we both value hats and books similarly, we can propose splitting the balls to maximize our points. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:53,001][mllm.models.large_language_model_local][WARNING] - Response Given the new per-item values, it seems that both Alice and Bob value balls the most, hats slightly more than books. To maximize points, it would be strategic to allocate more of the items that we value highly, which in this case are hats and balls. Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:53,003][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:53,006][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:53,021][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:53,024][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:53,026][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:53,029][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:53,031][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:53,034][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:53,037][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:53,045][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:53,048][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:53,050][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:53,053][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:53,055][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:53,057][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:53,068][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:53,071][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:53,073][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:53,075][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:53,077][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:53,080][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:53,082][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:53,084][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:53,092][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:53,094][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:53,097][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:53,099][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:53,101][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:53,104][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:53,109][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:53,112][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:53,114][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:53,124][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:53,129][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:53,142][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:53,144][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:53,164][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:53,170][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:53,172][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:53,175][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:53,223][mllm.models.large_language_model_local][WARNING] - Response Proposals often need to be strategic, taking into account the values of both agents. Given your per-item values for hats (1), books (10), and balls (10), and knowing that Bob values hats (10), books (10), and balls (1), it might be beneficial to propose an allocation that maximizes your points while accounting for the proportional distribution in case the total proposals exceed the item quantity. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:53,225][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:53,228][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:53,230][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:53,233][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:53,236][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:53,238][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:53,245][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:53,266][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:53,272][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:53,275][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:53,292][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:53,298][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:53,300][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:53,302][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:53,304][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:53,306][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:53,312][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:53,314][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:53,349][mllm.models.large_language_model_local][WARNING] - Response Given the updated values, it's clear that both you and Alice value hats and books highly, but Alice values balls more than you do. However, since the quantities and per-item values are symmetric and proportional, a fair and competitive approach would be to propose splitting the items evenly. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:53,355][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:53,362][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:53,428][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:53,431][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:53,433][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:53,507][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:53,509][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:53,512][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:53,518][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:53,524][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:53,527][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:53,535][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:53,537][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:53,540][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:53,542][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:53,545][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:53,556][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:53,559][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:53,561][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:53,564][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:53,566][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:53,569][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:53,571][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:53,574][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:53,581][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:53,584][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:53,586][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:53,589][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:53,603][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:53,606][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:53,609][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:53,611][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:53,614][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:53,625][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:53,628][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:53,631][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:53,633][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:53,636][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:53,638][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:53,641][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:53,652][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:53,669][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:53,673][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:53,689][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:53,735][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:53,741][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:53,836][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:53,838][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:53,843][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:53,848][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:53,861][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:53,865][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:53,870][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:53,874][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:53,935][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:54,016][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:54,022][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:54,155][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:54,158][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:54,160][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:54,165][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:54,171][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:54,242][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:54,248][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:54,265][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:54,271][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:54,278][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:54,284][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:54,290][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:54,296][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:54,322][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:54,326][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:54,331][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:54,343][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:54,347][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:54,352][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:54,356][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:54,361][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:54,365][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:54,370][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:54,374][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:54,378][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:54,383][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:54,387][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:54,391][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:54,400][mllm.models.large_language_model_local][WARNING] - Response Given the new per-item values, it's clear that both you and Bob place a high value on books and balls. To maximize your points, you should consider proposing a split that reflects these high values. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:54,402][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:54,406][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:54,410][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:54,413][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:54,417][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:54,421][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:54,428][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:54,430][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:54,449][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Bob place significant value on different items. You value books and balls highly, while Bob values hats and books highly. Here’s a strategic proposal that maximizes your points: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:54,503][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:54,859][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:54,862][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:54,899][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:54,902][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:54,904][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:54,906][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:54,907][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:54,908][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:54,909][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:54,975][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the other proposals, it makes sense to propose a similar distribution as in the previous rounds to maximize the points. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:54,978][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:54,981][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:55,064][mllm.models.large_language_model_local][WARNING] - Response Given the values, both you and Alice have a high valuation for hats and books, and a lower but similar valuation for balls. To maximize points, it might be beneficial to propose an allocation that reflects these values while preventing Alice from gaining an advantage by underproposing. A reasonable strategy is to propose an allocation that is close to the maximum while ensuring that the total does not exceed the available items. Since both of your valuations are similar and high for hats and books, it might be advantageous to slightly underpropose to avoid the proportional distribution scenario and secure more items. Proposal: 9 hats, 10 books, 9 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:55,186][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:55,189][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:55,192][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:55,321][mllm.models.large_language_model_local][WARNING] - Response Given the new per-item values, it seems that both you and Bob value balls the most, but Bob values books much higher than you do. To maximize points, you should propose to take all the balls and share the hats and books proportionally. Here's the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:55,324][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:55,380][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:55,382][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:55,384][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:55,385][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:55,479][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:55,568][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:55,571][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:55,621][mllm.models.large_language_model_local][WARNING] - Response Given that both you and Bob value books and balls highly, it's important to split these items in a way that maximizes your overall points. Since you value hats at 1 and Bob values books and balls more than you do, proposing to take all the books and balls and splitting the hats might be a good strategy. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:55,624][mllm.models.large_language_model_local][WARNING] - Response Given the updated per-item values, it appears that both you and Alice have a higher value for hats compared to the previous rounds. However, your values for books and balls are different. To maximize your points, you should propose to take more items that you value higher. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:55,657][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:55,660][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:55,732][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:55,735][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:55,738][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:55,741][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:55,744][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:55,821][mllm.models.large_language_model_local][WARNING] - Response Given the updated per-item values where both you and Alice have high values for different items, a strategic approach would be to maximize the items where you have a higher value. Here, you have a higher value for balls (10) compared to hats (1) and books (10 for Alice). Alice has a higher value for hats (10) and books (1) compared to balls (10 for you). A balanced approach could be to propose a distribution that maximizes your higher value item while considering Alice's values. A reasonable proposal might be to take all the balls and split the hats and books evenly, ensuring Alice gets the items she values highly. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:55,824][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:55,848][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the fact that both Alice and I have identical proposed allocations in the previous round, which resulted in equal earnings, I will maintain a similar strategy to maximize my points. Since we both have significantly higher values for books and balls compared to hats, and since hats and books have the same values for both of us, a balanced approach seems reasonable. Here’s my proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:55,851][mllm.models.large_language_model_local][WARNING] - Response Given the values, it seems like both you and Bob place high value on different items. You value hats and balls highly, while Bob values books and hats. Books are the most valuable item for Bob but least valuable for you. Here's a strategic proposal: Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:55,917][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:55,962][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:56,028][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:56,032][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:56,054][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:56,056][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:56,059][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:56,062][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:56,081][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, you have a significant preference for hats and books, and Bob has a similar preference but for hats and books as well. However, books are valued much higher by you compared to Bob, and hats are valued similarly by both. To maximize your points, you should offer a split that reflects the higher value of books while ensuring you get a fair share of hats. Here is the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:56,084][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:56,087][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:56,089][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:56,092][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:56,107][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:56,110][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:56,113][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:56,115][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:56,130][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:56,136][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:56,143][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:56,146][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:56,170][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:56,174][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:56,176][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:56,183][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:56,189][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:56,192][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:56,195][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:56,198][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:56,200][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:56,208][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:56,211][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:56,213][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:56,215][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:56,222][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:56,224][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:56,227][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:56,428][mllm.models.large_language_model_local][WARNING] - Response Given the new values, where both you and Alice value hats and books highly and balls less, a strategic proposal would be to ensure that we both get a significant portion of the items to maximize our points. Here's the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:56,454][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:56,457][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:56,515][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:56,518][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:56,572][mllm.models.large_language_model_local][WARNING] - Response Given the updated values where both you and Bob have a higher value for books and balls, a more strategic approach might be to propose a division that allocates the items in a way that maximizes your points, considering the higher mutual value for books and balls. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:56,579][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:56,581][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:56,584][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:56,587][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:56,589][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:56,598][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:56,642][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:56,645][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:56,664][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:56,671][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:56,678][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:56,685][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:56,693][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:56,695][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:56,698][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:56,701][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:56,716][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:56,719][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:56,721][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:56,724][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:56,739][mllm.models.large_language_model_local][WARNING] - Response Given the updated per-item values, where both you and Bob value books at 10 and balls at 1, but hats are valued at 10 by you and 1 by Bob, it seems like the items with the highest value for you (hats and books) are also highly valued by Bob (books). To maximize your points, you should propose to take a significant portion of the hats and books while ensuring you don't over-proportion the balls. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:56,742][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:56,745][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:56,769][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:56,772][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:56,790][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:56,796][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:56,799][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:56,802][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:56,870][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:56,873][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:56,875][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:56,878][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:56,881][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:56,952][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:56,959][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:56,965][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:56,968][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:56,971][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:57,123][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:57,127][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:57,130][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:57,218][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the previous outcomes, it seems that proposing to take all the items is beneficial since the allocation will be proportional and maximize the points based on our values. Therefore, I will propose to take all of the items. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:57,303][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:57,385][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:57,388][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:57,390][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:57,393][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:57,428][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:57,470][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:57,476][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:57,479][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:57,482][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:57,499][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:57,501][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:57,533][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:57,539][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:57,572][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:57,578][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:57,585][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:57,591][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:57,612][mllm.models.large_language_model_local][WARNING] - Response Given the new per-item values, where hats are worth 1 to you and 10 to Alice, and books and balls are worth 10 to you and 1 to Alice, it's clear that both of you have complementary preferences for the items. To maximize points, we should try to get as many of the items that we value highly. Proposing: 10 hats, 10 balls, 10 books This proposal will likely result in a proportional分配，考虑到我们的值评估，如果我们能得到所有的帽子、球和书，可以最大化我们的得分。这样的分配虽然公平，但可能不会被Alice接受，因为她更偏向于书和书本。 考虑到这种互补的趋势，可以稍微调整提议以确保双方都能接受，同时最大化个人得分。 Proposing: 10 hats, 10 balls, 10/2 = 5 books 这样你将获得所有帽子和所有球，并获得一半的书，确保总价值最大化。 did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:57,615][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric nature of the game and the values Alice and I have for each item, it appears that proposing to take all items is a logical and maximaizing strategy, as it aligns with the highest values each of us has for the items. Therefore, I will continue to propose the following: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:57,617][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:57,622][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:57,628][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:57,634][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:57,647][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:57,649][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:57,653][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:57,658][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric nature of the per-item values and the fact that both Alice and I value the items in a similar manner, the best strategy is to propose equal splits to ensure we both receive a fair share and maximize our points. Therefore: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:57,661][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:57,663][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:57,695][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:57,697][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:57,699][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:57,770][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:57,774][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:57,778][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:57,823][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:57,830][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:57,836][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:57,852][mllm.models.large_language_model_local][WARNING] - Response Given the new per-item values, it seems that both Alice and you value books significantly more than the other items. However, you value balls highly as well, while Alice does not. To maximize points, you should try to capture more of the items you value highly while allocating some to Alice to avoid getting a proportional share in return. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:57,855][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:57,858][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:57,861][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:57,870][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:57,905][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:57,907][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:57,910][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:57,930][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric and competitive nature of the game, and considering that Bob and I have identical values for hats and books, while having opposite preferences for balls, it makes sense to maximize our points by ensuring we get the items we value highly. In this round, both of us will likely propose the same items to ensure we both get our high-value items. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:57,933][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in our per-item values and Alice's, and the fact that proposing to take all items ensures equal sharing (since the total sum of our proposals equals the total item quantity), I will again propose to take all items to maximize my points. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:57,940][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:57,943][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:57,945][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:57,947][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:57,949][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:57,951][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:57,955][mllm.models.large_language_model_local][WARNING] - Response Given the values, it is clear that both you and Bob value balls the least (1 point each), while hats and books are highly valued by both of you. In the previous rounds, proposing to take all items led to a proportional split that was beneficial due to the high values. However, in this round, proposing to take all items might not be as effective since both parties value hats and books highly, and thus might propose similarly. A more strategic approach might involve proposing a middle ground to ensure a fair and potentially higher individual share. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:57,955][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:57,977][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:57,999][mllm.models.large_language_model_local][WARNING] - Response Given Bob's per-item values, he values books the most. To maximize my points, I should focus on securing as many balls as possible since I value them the most at 10 points each. Here’s a strategic proposal: Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:57,999][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:58,002][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:58,003][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:58,004][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:58,096][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the high value of balls for both Alice and yourself, it's crucial to secure as many balls as possible since they are very valuable for both parties. However, you also need to consider the other items. A reasonable strategy would be to propose a split that maximizes your points while ensuring you don't overcommit to items that Alice values highly, especially books and hats. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:58,137][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values for this round, it's clear that both hats and balls are highly valued by me, while books are valued less by both of us. However, since Bob values books more than I do and balls less than I do, it might be beneficial to propose keeping the majority of valuable items for myself to maximize points. Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:58,139][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and equal per-item values in this round, proposing to take all of each item seems like the optimal strategy to maximize points. Therefore: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:58,160][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:58,162][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:58,164][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:58,166][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:58,201][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric nature of the game and the consistent valuation patterns, it seems strategically wise to propose a fair split that reflects the values each of us attributes to the items. Since we each value the balls the least and the hats and books more, proposing a split that recognizes these values could lead to a stable and fair outcome. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:58,205][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:58,282][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:58,285][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:58,289][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:58,292][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:58,295][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:58,362][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both hats and books are more valuable to Alice, while balls are more valuable to you. To maximize your points, you should propose to keep as many balls as possible, while splitting the hats and books in a way that minimizes Alice's advantage in those items. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:58,365][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:58,369][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:58,372][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:58,375][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:58,378][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:58,608][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the values and the nature of the game, where both Alice and I have similar and competitive valuations, the optimal strategy would be to propose an even split to avoid any potential for proportional allocation in case our combined requests exceed the available items. This ensures we each get a fair share based on the values we attribute to the items. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:58,612][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:58,620][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:58,623][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:58,630][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:58,686][mllm.models.large_language_model_local][WARNING] - Response Given the values, you have a high value for hats (10) and a lower but positive value for books (1), but a very low value for balls (1). Meanwhile, Bob values hats and balls at 1, but values books at 10. Since the items are split proportionally and both of you have the same expected per-item value, it's in your best interest to maximize the items you value highly. Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:58,690][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:58,766][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:58,770][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:58,853][mllm.models.large_language_model_local][WARNING] - Response Given the new per-item values where I value hats and balls higher than Bob, and Bob values books higher than I do, I should propose a distribution that takes advantage of these differences. Here’s my reasoning: - For hats: I value them the same as Bob, so splitting equally is reasonable. - For books: I value them much less than Bob, so I would want to minimize my share. - For balls: I value them more than Bob, so I should take more of these. A proposed distribution could be: - Hats: 10 (split equally) - Books: 0 (to minimize my loss) - Balls: 10 (take all to maximize my benefit) So, my proposal is: Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:58,861][mllm.models.large_language_model_local][WARNING] - Response Given the values where both you and Alice have high affinity for different items, a strategic proposal might involve splitting the items to maximize individual gains. Since you value hats and books highly and Alice values balls highly, we can propose to take most of the hats and books and leave the balls for Alice. Proposal: 10 hats, 10 books, 10 balls However, to further optimize, we can consider taking more of the items we value more and leave the items we value less to Alice. Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:58,868][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:58,874][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:58,934][mllm.models.large_language_model_local][WARNING] - Response Given the new per-item values, where you value hats highly and Bob values books highly, it makes sense to propose an allocation that maximizes your value. Here's the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:58,938][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:58,944][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:58,951][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:58,954][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:59,105][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:59,113][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:59,117][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:59,120][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:59,357][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:59,424][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:12:59,428][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:59,448][mllm.models.large_language_model_local][WARNING] - Response Given the new per-item values, where you value hats at 1, books at 10, and balls at 10, and Alice values them at hats=10, books=10, and balls=1, a more strategic proposal would be to maximize your value for items you highly value. Here’s a proposed allocation: Proposal: 10 hats, 10 books, 10 balls However, since books and balls are highly valued by both of us but in different ways, and we have the same valuation for hats, let’s try to optimize around the items that can maximize your points: Proposal: 10 hats, 10 books, 10 balls - This keeps both items with high individual value for you. Since this proposal might still lead to proportional allocation due to the high valuation of books and balls by both of us, let’s try to slightly tweak the proposal to maximize your points slightly more: Proposal: 10 hats, 10 books, 9 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:59,455][mllm.models.large_language_model_local][WARNING] - Response Given the new values where you have a high value for hats (10) and Bob has a high value for books (10), it might be beneficial to propose a distribution that captures the higher value items. Here's a proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:59,545][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:59,547][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:59,731][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that you and Bob have complementary preferences for the items. You value hats and books more, while Bob values balls more. To maximize your points, you should propose to take all the items that you value higher, while leaving the balls to Bob. Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:59,734][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:59,788][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, the best strategy might involve focusing on the items that have higher personal value. Since both you and Bob value balls the least, allocating all balls to yourself would maximize your points. Here’s the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:12:59,880][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:59,884][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:59,887][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:12:59,970][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both you and Alice value hats the least, and balls the most. To maximize points, it would be wise to propose keeping most of the items that you value highly, while still allowing Alice to get some value from items she values highly. Here's a proposal that maximizes your points: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:00,088][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the fact that both you and Alice have the same per-item values for hats, and Alice values books and balls more than you do, a strategic proposal would be to try to capture more of the items we both value the most, which are books and balls. However, since we are equally matched in valuing hats, a balanced approach might still be effective. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:00,092][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:00,095][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:00,098][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:00,106][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:00,113][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:00,119][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:00,124][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:00,127][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:00,130][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:00,157][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:00,269][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:00,276][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:00,294][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:00,926][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:00,930][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:00,933][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:00,936][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:00,996][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:01,021][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:01,024][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:01,027][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:01,031][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:01,073][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Alice place high value on different items, with hats being highly valued by Alice and books highly valued by you. Balls are valued equally low by both of you. Given this setup, a cooperative approach might yield better results as it aligns with maximizing the overall points. However, since the items are to be split proportionally if the total proposals exceed the available items, we need to propose a fair split that maximizes our individual gains while considering the proportional distribution in case of excess. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:01,077][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:01,097][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:01,125][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:01,129][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:01,132][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:01,150][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the previous outcomes where both Alice and Bob propose to take all items in each round, it seems that proposers are receiving the items they propose. Therefore, to maximize points, it is logical to propose taking all items again. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:01,154][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:01,157][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:01,159][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:01,179][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:01,182][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:01,225][mllm.models.large_language_model_local][WARNING] - Response Given that both you and Bob have similar values for hats and books, and very different values for balls, the optimal strategy is to maximize the points from the item you both value less but you value significantly more. Here, balls are worth 1 to Bob and 10 to you. To maximize your points, you should propose to keep all the balls yourself, even though this means you won't get any hats or books if Bob doesn't agree. Proposal: 0 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:01,228][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:01,232][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:01,235][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:01,248][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:01,269][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:01,273][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:01,276][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:01,279][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:01,282][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:01,284][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:01,287][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:01,288][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:01,298][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:01,301][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:01,303][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:01,320][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the nature of the game, it seems both you and Alice have similar incentives to propose the same allocation. Given this, a reasonable approach is to propose an equal split to avoid any potential conflict. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:01,323][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:01,325][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:01,328][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:01,334][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:01,336][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:01,354][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:01,357][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:01,359][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:01,401][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the fact that both you and Alice value the hats highly and the balls less, but the distribution of values is such that each of you highly values different items, a cooperative approach might not yield better results. However, proposing an allocation that maximizes your value based on your higher valuation for hats and books, while still considering the proportional distribution for any excess claims, seems reasonable. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:01,425][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:01,428][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:01,451][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:01,453][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:01,455][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:01,472][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:01,557][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:01,564][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:01,571][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:01,574][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:01,638][mllm.models.large_language_model_local][WARNING] - Response Given the current values, it's important to consider the high value of balls for you and the high value of books for Alice. A strategy that takes advantage of these differences could be beneficial. Here's a proposal that maximizes your expected gain from the balls while still ensuring you get a good portion of the books and hats: Proposal: 10 hats, 10 books, 7 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:01,719][mllm.models.large_language_model_local][WARNING] - Response Given the recent rounds and the values, Bob values hats more while you value books and balls more. However, since both of you value hats quite highly, it suggests that proposing to take all items might still be a valid strategy. Here's a potential proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:01,727][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:01,730][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:01,803][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:01,889][mllm.models.large_language_model_local][WARNING] - Response Given Bob's per-item values are hats=1, books=10, balls=10, and your per-item values are hats=10, books=10, balls=1, it makes sense to maximize your points by focusing on items you value highly. Since you value balls the most and Bob values them the least, it's strategically beneficial to claim all the balls. You should also claim the books since you and Bob value them equally, and you should claim as many hats as possible without splitting them, as you value them more than Bob. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:01,893][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:01,900][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:01,907][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:01,972][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:01,975][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:01,977][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:02,061][mllm.models.large_language_model_local][WARNING] - Response Given the current per-item values and Bob's strategy, it seems that splitting the items evenly is a good approach to maximize points. Here, your per-item values are hats=1, books=1, and balls=10, while Bob's per-item values are hats=10, books=1, and balls=1. Your optimal strategy would be to propose a split that maximizes your points. Since you value balls the most at 10, and Bob values hats the most at 10, you should allocate balls mostly to yourself and split the hats and books evenly. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:02,064][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and equal values in this round, proposing an equal distribution seems optimal. Here is the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:02,066][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:02,070][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:02,074][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:02,142][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the previous round outcomes, it appears that both you and Bob are propose equally to split the items. This strategy seems to maximize points for both parties in each round. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:02,146][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:02,227][mllm.models.large_language_model_local][WARNING] - Response Given that both you and Bob have similar valuations for hats and books, and balls are much less valued by both but you value them higher, a strategic approach would be to suggest a more balanced split that takes into account the differing valuations. Proposal: 10 hats, 10 books, 5 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:02,231][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:02,321][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that hats and books are very valuable to Bob, while balls are very valuable to you. To maximize your points, you should propose to keep all the balls and share the hats and books with Bob in a way that both of you still benefit. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:02,329][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:02,332][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:02,335][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:02,338][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:02,412][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:02,413][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:02,416][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:02,420][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:02,423][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:02,426][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:02,504][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric nature of the game and the values of the items, it seems that proposing to take all of the items is the optimal strategy, as both you and Bob have high values for the same items. This is because both you and Bob will likely propose to take all of the items, leading to proportional splits if the total proposals exceed the item quantity. However, to ensure a fair and symmetric start, we can proceed with the assumption that both you and Bob will propose to take all of the items. Given your per-item values and Bob's per-item values, proposing the following should be a rational choice: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:02,507][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Alice have the highest value for different items. To maximize your points, you should propose to take the items that you value highly and let Alice take the items she values more highly. Here's a strategic proposal: Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:02,510][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:02,595][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:02,603][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:02,606][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:02,609][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:02,612][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:02,682][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:02,755][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:02,759][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:02,781][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:02,784][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:02,800][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both of us value hats and books highly, but balls are not as important to either of us. To maximize our points, we should focus on proposing to keep all the items, as splitting them proportionally would result in each of us getting a smaller share of the high-value items. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:02,807][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:02,844][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's strategic to propose a split that accounts for both of our valuable items. Since both of us value hats and balls relatively high, and we want to ensure we get a fair share of the books as well, a balanced proposal might be prudent. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:03,074][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the fact that both Alice and I value hats and books highly and balls less, it's in my best interest to maximize the items I receive. Let's propose the distribution where I get as many hats and books as possible. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:03,175][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the previous round outcomes, it seems that proposing to take all items would result in a proportional split, which maximizes the points considering both agents' values for the same items. Thus, the proposal would be: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:03,243][mllm.models.large_language_model_local][WARNING] - Response Given the new per-item values, it's clear that both Alice and Bob value hats and balls more heavily compared to books. To maximize points, it's beneficial to propose a strategy that takes advantage of our respective higher values for hats and balls while still leaving some value for books. Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:03,377][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's beneficial toClaim the items that have higher individual values for you, while also taking into account Bob's valuations to avoid under-proposing and getting a proportional split. Here, hats and balls are valued more by you, and books are valued more by Bob. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:03,441][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in Bob's and my per-item values for this round, it appears that proposing the same allocation as Bob would likely result in a fair split. To maximize points, we should aim to allocate items in a way that maximizes their value. Since both of us value hats and books equally and balls the least, a proportional split might be the fairest and most effective strategy. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:03,446][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:03,450][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:03,453][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:03,457][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:03,461][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:03,464][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:03,467][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:03,526][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:03,618][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:03,624][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:03,650][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:03,708][mllm.models.large_language_model_local][WARNING] - Response Given the new per-item values, it makes sense to propose a distribution that maximizes the benefit of the items with higher personal value. Since both you and Bob value balls highly, it's crucial to secure as many balls as possible while also considering the other items. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:03,711][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:03,714][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:03,717][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:03,806][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:03,810][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:03,814][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:03,833][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:03,903][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:03,907][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:03,944][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:03,999][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:04,003][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:04,006][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:04,075][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:04,078][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:04,082][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:04,144][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:04,147][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:04,151][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:04,153][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:04,230][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:04,233][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:04,235][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:04,238][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:04,242][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:04,314][mllm.models.large_language_model_local][WARNING] - Response Given the new per-item values, it's clear that both you and Alice place the highest value on balls and the lowest on hats. To maximize your points, you should propose to take as many of the most valued items (balls) as possible, while also considering the distribution of hats and books. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:04,317][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both you and Alice value hats and books significantly more than balls. The optimal strategy would be to maximize the items that both of you value highly to avoid potential splits that could reduce your points. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:04,321][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:04,324][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:04,327][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:04,401][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:04,404][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:04,407][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:04,410][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:04,417][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:04,423][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:04,429][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:04,432][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:04,435][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:04,438][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:04,455][mllm.models.large_language_model_local][WARNING] - Response Considering the values, books and balls are much more valuable to both of us, but Alice values hats more. To maximize my points, I should try to get as many of the high-value items (books and balls) as possible while also taking some hats. Here is a balanced proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:04,474][mllm.models.large_language_model_local][WARNING] - Response Given that both our per-item value distributions are symmetric and that proposing to take all items is seen to yield the maximum points when we share similar values, I will continue to propose the allocation of all items to maximize points. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:04,506][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:04,553][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:04,650][mllm.models.large_language_model_local][WARNING] - Response Given that both you and Alice value hats and books highly (10) and balls less (1 for you, 10 for Alice), it's beneficial to propose a split that respects the high value items. However, since Alice might propose a similar split, we need to consider an adjustment to ensure we maximize our share. Proposal: 10 hats, 10 books, 5 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:04,766][mllm.models.large_language_model_local][WARNING] - Response Given the new per-item values where I value hats and balls at 10 and Alice values books and balls at 10, it makes sense to propose an allocation that maximizes both our values. Here's the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:04,926][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the fact that both agents have the same strategy of proposing to take all items, it seems like any proposal that matches the items available will result in a proportional split when the proposals exceed the available items. To maximize my points, I will propose to take all items as Alice did in the previous rounds. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:04,929][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and the observed behavior of Alice, it seems strategic to balance the allocation to maximize personal benefit. Since Alice values books and balls highly and may propose to take all of them, it's wise to ensure that I get some of these items as well. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:04,983][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:04,986][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:04,987][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:04,991][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:04,993][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:04,995][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:05,075][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, hats are worth 1 to you and 10 to Alice, books are worth 10 to you and 1 to Alice, and balls are worth 10 to both of us. Since our values for the items are the same, a fair split would likely benefit both of us. However, to maximize my points, I should aim to get the items I value the most. My strategy is to take all the books and balls, as they are worth more to me, and split the hats evenly to ensure I get a share. Proposal: 5 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:05,082][mllm.models.large_language_model_local][WARNING] - Response Given that both you and Bob have complementary valuations, proposing to take all the items ensures that we maximize our individual points based on our per-item values. Since our combined proposals will always sum to the total number of items, we will receive all the items we propose. Here's the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:05,085][mllm.models.large_language_model_local][WARNING] - Response Given the new per-item values, where you value hats at 1, books at 10, and balls at 10, and Alice values them at 10, 1, and 10 respectively, a cooperative strategy seems beneficial since the values align for most items. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:05,087][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:05,090][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:05,094][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:05,160][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the fact that both you and Alice have similar valuations for the items, the optimal strategy is to propose an equal split to avoid any advantage for either party. This ensures you maximize your points relative to your valuations. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:05,163][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both hats and books have higher value for you compared to Bob, while balls have a higher value for Bob. To maximize your points, you should propose to take all the hats and books, and share the balls proportionally. Proposal: 10 hats, 10 books, 5 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:05,250][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:05,253][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:05,342][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and equal per-item values for each item type after observing Bob's strategy in the last round, it's clear that both Alice and Bob propose to take all items. To maximize the points, we should aim to get the maximum allocation possible. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:05,346][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:05,349][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:05,372][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:05,400][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the high value of balls for both of us, let's focus on maximizing our points by securing a higher number of balls while still proposing a fair share of hats and books. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:05,469][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the values, proposing a fair split that considers both value and quantity seems optimal. Here's the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:05,510][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:05,554][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the fact that both you and Alice have highly valued balls, it's crucial to secure as many balls as possible while also splitting the hats and books to ensure a balanced and optimal allocation. Here’s a strategic approach: - You have a high value for books and balls. - Alice has a high value for hats and balls. A balanced proposal would be to allocate the items such that no one gets an overwhelming advantage, but you secure the items with the highest combined value. Proposal: 5 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:05,556][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:05,558][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:05,592][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, hats=10, books=10, balls=1 for you, and hats=1, books=10, balls=10 for Bob, it's clear that both you and Bob value the balls the same but you both value hats and books very differently. Since you have a higher value for hats and books compared to Bob, you should propose to take more of these items to maximize your points. However, since Bob values books highly and you value balls highly, it might be worth considering a strategy that allocates books and balls in a way that minimizes the risk of Bob taking all the items you value less. Here's a proposal that tries to balance this: Proposal: 8 hats, 8 books, 10 balls This proposal gives you a significant portion of the items you value highly (hats and books) while still taking all the balls, which you also value but not as highly as hats and books. This should help you maintain a high earning per round while minimizing the risk of Bob taking all your high-value items. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:05,594][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:05,596][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:05,599][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:05,640][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:05,678][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Alice place high value on books and balls, and low value on hats. However, since you and Alice have opposing values for hats and books, proposing to take all of each type might not be advantageous if the total proposed amount exceeds the available items. A strategic approach would be to propose a distribution that maximizes your value based on the likely scenario of proportional allocation. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:05,682][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:05,684][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:05,720][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:05,724][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:05,758][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:05,761][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:05,765][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:05,831][mllm.models.large_language_model_local][WARNING] - Response Given Alice's per-item values, she values hats and balls the same as me, but books much more. This knowledge can be used to our advantage. If we propose to take all the books, we can maximize our points from the books while ensuring we get the maximum value from the balls and hats as well. Here's the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:05,836][mllm.models.large_language_model_local][WARNING] - Response Given the dynamic values and the symmetry in both players' valuations for the items, a strategic proposal would be to recognize that splitting the items equally is likely to maximize the points for both parties. Since both players have the same expected value for each item, proposing an even split would ensure that neither player gains an unfair advantage. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:05,872][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the fact that both Alice and I value books and balls highly, while assigning low value to hats, a strategic proposal would be to各自的提议应当充分考虑到对方的低值物品偏好，尽量减少对方对这些物品的获取，同时最大化自己的收益。考虑到对手的策略，提出一份可以确保自己在每种物品上都能获得一定比例的提议可能会更稳健。 Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:05,876][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:05,881][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:05,883][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:05,950][mllm.models.large_language_model_local][WARNING] - Response Given the values, the optimal strategy would be to propose a split that maximizes the value for both hats and balls, since they have the highest individual values. Here's the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:05,996][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:05,998][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:06,001][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:06,005][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:06,050][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:06,053][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:06,055][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:06,132][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:06,134][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:06,138][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:06,143][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:06,225][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:06,228][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:06,230][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:06,408][mllm.models.large_language_model_local][WARNING] - Response Given the new valuations, I should focus on claiming as many of the items with high value to Bob (books and balls) as possible, while still proposing a fair share for hats. Here’s a strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:06,411][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in per-item values and the equal likelihood of proposals leading to proportional allocation, it is strategically sound to propose the same division as Alice to ensure that we receive a fair share based on our values. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:06,414][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:06,465][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:06,487][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both hats and balls are highly valued differently by each player. To maximize points, it's beneficial to propose items that align with your higher value items while considering Alice's lower valuation of these items. Here's the optimal proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:06,514][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric and strategic nature of the game, it's important to consider both the per-item values and the potential allocation strategy of Alice. In this round, both you and Alice have similar valuations for hats but opposite preferences for books and balls. To maximize your points, you might want to propose a distribution that accounts for the high value of books and balls you have. Here's a strategy where you propose to take all items that you value highly: Proposal: 10 hats, 10 books, 10 balls This proposal ensures that you get the maximum value from your highly valued items, while Alice may opt for the items she values more, thus preventing any proportional allocation that might split the items unevenly. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:06,562][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and the symmetrical setup, dividing the items equally seems like a fair and effective strategy. Here is the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:06,650][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's important to balance the proposal to maximize the points while considering Bob's likely strategy. If Bob values hats and balls low and books high, we should try to secure as much value as possible, especially from books, while not completely giving up hats and balls. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:06,652][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and Bob's proposed strategy from the previous rounds, it seems optimal to propose the following to maximize your points: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:06,931][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in our per-item values and Alice's, and knowing that both sets of values are additive, a fair proposal would be to split the items evenly. This strategy aims to maximize the utility while accounting for the randomness in item assignments. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:07,058][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:07,061][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:07,064][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:07,133][mllm.models.large_language_model_local][WARNING] - Response Given that my per-item values for hats, books, and balls are 1, 10, and 10 respectively, and Bob's per-item values are 10, 1, and 10, proposing to split the items equally might not maximize my points since Bob values books and balls more than hats, and I value hats the most. I should propose to take all items that I value highly to ensure I get the maximum points. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:07,140][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:07,142][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:07,143][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:07,216][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and the symmetry in Alice's and your per-item values for hats and books, respectively, it makes sense to propose an allocation that equally considers both of your strengths. Here, you value hats and books more, while Alice values balls more. We should try to maximize the points by focusing on the items we value higher. Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:07,341][mllm.models.large_language_model_local][WARNING] - Response Given the values, it seems both you and Alice have complementary valuations for the items. You highly value hats and balls, while Alice highly values books. Splitting the items 50-50 in proportion to the values would likely maximize the points for both of you. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:07,342][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:07,387][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:07,390][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:07,392][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:07,394][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:07,395][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:07,397][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:07,399][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:07,400][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:07,448][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:07,450][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:07,513][mllm.models.large_language_model_local][WARNING] - Response Given the new per-item values where I value hats at 1, books at 10, and balls at 1, and Alice values them as hats at 10, books at 1, and balls at 1, a cooperative strategy would be beneficial. Since books are highly valued by both of us, we can ensure we both get a fair distribution of books while splitting the other items. Proposal: 10 hats, 10 books, 10 balls This proposal splits all items evenly, ensuring that both of us get books, which are highly valued by both of us. This should maximize our combined points. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:07,517][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:07,539][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the proposed strategy in previous rounds, it appears that proposing to take all items is a fair and strategic approach, as both you and Alice seem to be maximizing points in a proportional allocation. However, to ensure a balanced distribution and to account for the possibility of slight variations in the allocation, a slightly adjusted proposal might be beneficial. Proposal: 8 hats, 10 books, 10 balls This proposal gives you a significant share while also considering the possibility that Alice might slightly outbid you for certain items. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:07,543][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:07,545][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:07,587][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that hats and balls are highly valued by both Alice and Bob, while books are valued less. However, since both Alice and Bob seem to propose the same amounts, we can infer that they are both maximizing their total points by proposing equal shares. To ensure we continue to maximize our points, we should propose an equal distribution of all items. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:07,590][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric and high value distribution of the items, it seems that proposing an equal share maximizes the benefit for both parties. Here, I'll propose an equal split again: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:07,594][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:07,596][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:07,645][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:07,648][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:07,651][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:07,654][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:07,658][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:07,660][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:07,672][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:07,676][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:07,717][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:07,719][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:07,722][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:07,725][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:07,741][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:07,744][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:07,747][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:07,767][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:07,769][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:07,772][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:07,791][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:07,793][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:07,795][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:07,814][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:07,818][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:07,820][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:08,007][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:08,009][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:08,030][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:08,037][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:08,071][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both hats and balls have higher values for the other player, while books have a balanced value. To maximize my points, I should try to get more books and balls since they have higher values for Bob, and fewer hats since they have higher values for me. Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:08,074][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the observation that both you and Alice have the same expected value for each item, a fair and competitive strategy would be to propose an equal split of the items. This ensures that both parties receive a proportional share based on their valuation. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:08,075][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:08,078][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:08,080][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:08,083][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:08,088][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:08,092][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:08,095][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:08,158][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:08,246][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:08,254][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:08,329][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:08,333][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:08,413][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:08,421][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:08,423][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:08,427][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:08,498][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:08,501][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:08,504][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:08,508][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:08,510][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:08,672][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:08,676][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:08,679][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:08,753][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:08,756][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:08,759][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:08,834][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:08,838][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:08,840][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:08,924][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:08,928][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:08,932][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:08,935][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:09,008][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:09,012][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:09,016][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:09,096][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:09,099][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:09,182][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:09,187][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:09,190][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:09,194][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:09,372][mllm.models.large_language_model_local][WARNING] - Response Given Alice's new per-item values, where she values hats and books much higher than balls, and you value all items at the same level, it makes sense to ensure you maximize your points by taking full advantage of your valuation of the books. Here's a strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:09,375][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's beneficial to propose a distribution that reflects the high value you place on balls (10) and Bob's high value on books (10). Here’s a strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:09,464][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's beneficial to try to maximize the split of items where the per-item value is high for either you or Bob. Since both of us have the same per-item value for books (1 and 10 respectively), and balls (10 and 10 respectively), splitting these should be ideal. Proposal: 10 hats, 10 books, 10 balls This proposal aims to secure the highest value items for both of us. Given the symmetry and the fact that we have identical preferences for books and balls, this distribution ensures we both get a maximum value. However, to be more strategic, we could slightly adjust the distribution to see if we can gain an advantage. Let's try to secure more of the high-value items for ourselves. Proposal: 10 hats, 10 books, 9 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:09,493][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, I should aim to maximize the points by acquiring the items that I value more. Since I value hats and books significantly more than balls, and Bob values balls more, a strategic proposal would be to allocate more hats and books to myself and keep some balls. Here’s a balanced yet strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:09,526][mllm.models.large_language_model_local][WARNING] - Response Given the values, I should focus on getting as many high-value items (hats and books) as possible while still considering Bob's values. Here's the optimal proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:09,531][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:09,701][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric and complementary nature of the per-item values between you and Bob, it's strategic to propose an allocation that maximizes the value for both of you while ensuring neither of you can gain an unfair advantage. Since the values are largely complementary, an equitable split will likely yield the best outcomes for both players. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:09,747][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the fact that both you and Alice value hats the least and balls the most, it's optimal to propose equally splitting all items to ensure we each receive a fair share based on our per-item values. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:09,767][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the previous outcomes where both you and Alice propose to take all items, it appears that neither of you values the distribution process differently and both are content with taking all items. However, to explore a strategy that might yield a different result and ensure a fair split, we can propose a different distribution. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:09,854][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:09,856][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:09,857][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:09,859][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:09,860][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:09,864][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:09,866][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:09,868][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:09,871][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:09,874][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:09,876][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:09,878][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:09,881][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:09,883][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:09,885][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:09,887][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:09,889][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:09,890][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:09,893][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:09,895][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:09,897][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:09,920][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:09,921][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:09,922][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:09,962][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric and sequential nature of the game, and considering that both you and Alice have similar values for different items, proposing a fair split that maximizes your points based on your values would be a strategic move. Here, you value hats and books at 1 and balls at 10, while Alice values hats and books at 10 and balls at 10. Proposing a balanced split across the items seems optimal. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:10,015][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both you and Bob place high value on hats and books, and both of you value balls less. Here is the proposal that maximizes the distribution based on the assigned values: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:10,018][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:10,020][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:10,041][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the equal sharing of items, it is optimal to propose the same distribution as Alice to ensure both agents receive a fair share. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:10,043][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:10,070][mllm.models.large_language_model_local][WARNING] - Response Given the values, hats are more valuable to me than books and balls, and balls are more valuable to Bob than books and hats. Proposing to take the majority of hats and books while leaving a few balls for Bob might be a strategic choice to maximize my points. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:10,074][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:10,075][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:10,078][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:10,095][mllm.models.large_language_model_local][WARNING] - Response Given Bob's new valuations, he values balls much higher than before, while you still value hats and balls relatively high. Given this, a strategic proposal should aim to take advantage of Bob's misalignment in valuations. Since you value balls highly and Bob values them low, proposing to take all the balls and sharing the hats and books proportionally could be effective. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:10,097][mllm.models.large_language_model_local][WARNING] - Response Given that both you and Alice have similar valuations for hats and books but different valuations for balls, a tit-for-tat strategy seems reasonable. Since we have performed well by proposing equal shares in previous rounds, let's continue with that approach to maintain fairness and cooperation. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:10,127][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:10,156][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:10,159][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:10,162][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:10,164][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:10,206][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:10,208][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:10,245][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:10,270][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:10,293][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:10,296][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:10,318][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:10,323][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:10,325][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:10,340][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and the symmetric nature of the situation, it's important to propose a fair split that maximizes the points for both parties. Here’s a strategy that balances the items based on their high values for both of us: Proposal: 10 hats, 10 books, 10 balls This proposal gives each of us 10 hats, 10 books, and 10 balls, which should lead to a fair allocation based on our respective values. Let's submit this proposal. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:10,343][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:10,384][mllm.models.large_language_model_local][WARNING] - Response Given Alice's per-item values are hats=1, books=1, balls=10, she will value balls the most and hats the least. To maximize my points, I should try to secure the balls while also getting some hats and books. Here is my proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:10,386][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:10,388][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:10,393][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:10,398][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:10,441][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:10,445][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:10,448][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:10,582][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:10,584][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:10,587][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:10,667][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the values and the outcomes of previous rounds, where both Alice and Bob receive the same points when proposing to take all items, it might be optimal to propose taking all items to maximize the points in this round as well. Therefore: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:10,670][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:10,673][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:10,689][mllm.models.large_language_model_local][WARNING] - Response Given that we both value the balls highly (10 each), and we have similar values for the hats and books (1 and 10 respectively), it makes sense to maximize our points by taking most of the items we value highly. Here's the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:10,694][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:10,699][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:10,701][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:10,716][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:10,718][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:10,756][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:10,841][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both hats and balls are much more valuable to me than to Alice, while books are more valuable to Alice. A strategic proposal should maximize my points by taking advantage of this valuation difference. Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:10,845][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:10,849][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:10,852][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:10,886][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:10,889][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:10,971][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:10,975][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:11,138][mllm.models.large_language_model_local][WARNING] - Response Given the new per-item values, you have a strong preference for books and balls, while Alice has a strong preference for hats and balls. To maximize your points, you should propose to take the items that you value more. Proposal: 10 hats, 10 books, 10 balls However, since taking all the hats would exhaust the total quantity, you should adjust your proposal to make it likely that the items will be distributed proportionally if the total proposed amount exceeds 30. Proposal: 10 hats, 10 books, 9 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:11,222][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:11,307][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:11,311][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:11,391][mllm.models.large_language_model_local][WARNING] - Response Given the consistent high value that both you and Bob place on hats and books, and the lower value on balls, a strategic proposal would be to maximize your shares in hats and books while ensuring you do not over-propose on balls. Here's a balanced proposal that aims to secure a good share while avoiding over-proposing: Proposal: 10 hats, 10 books, 5 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:11,395][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:11,403][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:11,562][mllm.models.large_language_model_local][WARNING] - Response Given the updated per-item values, where you value hats and books at 10 and balls at 1, and Bob values hats and balls at 1, and books at 10, it makes sense to focus more on items where you have a higher valuation compared to Bob. Here, hats and books are valued equally by both, but the added emphasis on books from your perspective suggests prioritizing books over balls. Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:11,565][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:11,569][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:11,575][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:11,646][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:11,681][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:11,685][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:11,687][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:11,690][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:11,693][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:11,704][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:11,707][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:11,710][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:11,736][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:11,742][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:11,747][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:11,761][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:11,764][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:11,808][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:11,814][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:11,819][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:11,880][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the equal per-item values for the items, it makes sense to propose a fair split to maximize the points. Therefore, the proposal should ensure that both Alice and Bob receive a fair share of the items. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:12,057][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in our per-item values and Alice's, proposing an equal split seems to be the most rational strategy to maximize points. Therefore, I will propose: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:12,175][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:12,209][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:12,212][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:12,215][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:12,231][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:12,253][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:12,256][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:12,258][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:12,346][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:12,348][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:12,349][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:12,349][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:12,436][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both you and Alice place high value on different items. You value hats and books highly, while Alice values books and balls highly. Given this, a cooperative approach might be beneficial, but since Alice proposes to take all balls in the previous round when you value them highly, it suggests a more aggressive strategy could yield more points. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:12,439][mllm.models.large_language_model_local][WARNING] - Response Given the values, I should propose to take more of the items I value highly (books and balls) while ensuring the total quantity of items I propose does not exceed the available items. Here's my proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:12,443][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:12,446][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:12,515][mllm.models.large_language_model_local][WARNING] - Response Given the values, it seems that both you and Bob have complementary preferences. You value hats and books highly, while Bob values balls highly. A cooperative strategy might yield better results than an aggressive one, as splitting the items more evenly could lead to a higher total value for both of you. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:12,599][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:12,602][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:12,606][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:12,678][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:12,681][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:12,759][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the values each of us places on the items, proposing an equal share seems to be the most rational strategy. This will ensure that both you and Alice receive items that are valued highly by the other, leading to higher points. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:12,763][mllm.models.large_language_model_local][WARNING] - Response Given the new values, where you have a higher value for balls and Alice has a higher value for hats and books, a strategic split would be to take more of the items you value higher. Here's the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:12,840][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:12,922][mllm.models.large_language_model_local][WARNING] - Response Given the consistent values and the symmetry in the problem, the optimal strategy is to propose a fair split that maximizes the total points for both parties. Since the per-item values and the distribution of items are symmetric, a fair split will likely lead to the best outcome. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:12,926][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:12,929][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:13,004][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the values, it seems that proposing an equal split is beneficial when both Alice and Bob have similar or complementary preferences. To maximize points, we should consider proposing a split that balances both our values and Bob's. Here, proposing an equal split might still be effective. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:13,094][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:13,097][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:13,142][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the fact that both you and Bob have the same values for hats and books, but different values for balls, a cooperative strategy might be beneficial. We can propose to split the items equally, which might lead to a fair outcome and higher total points. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:13,146][mllm.models.large_language_model_local][WARNING] - Response Given the symmetrical nature of the problem and the equal distribution of item values between you and Alice, proposing an equal share of the items is a reasonable strategy to maximize points. Here is the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:13,149][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:13,166][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:13,188][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:13,190][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:13,213][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:13,215][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:13,217][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:13,254][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and the symmetry in the situation, it's important to propose a fair distribution to maximize your points while considering Bob's potential strategy. Since we both have a significant value for balls and only a minor value for books, it's a good idea to allocate as many balls as possible to maximize our points. Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:13,262][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both you and Bob value hats and books much less than balls. Since the per-item values are similar for hats and books, and much lower for balls, it might be strategic to propose keeping more of the items with higher individual values. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:13,266][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:13,278][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the values of the items, a reasonable strategy is to propose an equal split of all items to ensure both parties receive items proportional to their values. This approach can help avoid overproposing on lower-value items while ensuring that higher-value items are also allocated fairly. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:13,281][mllm.models.large_language_model_local][WARNING] - Response Given the consistent values and outcomes, it seems that proposing to take all items is leading to equal distribution due to the symmetry in the game. To explore other possibilities and ensure we're maximizing our points, we should consider proposing a split that reflects the difference in per-item values. Proposal: 10 hats, 10 books, 5 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:13,284][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:13,286][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:13,297][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:13,347][mllm.models.large_language_model_local][WARNING] - Response Given the high value of hats and books for both of us and the low value of balls, I should propose to maximize the distribution of hats and books while still considering Alice's valuation of balls. Here's a balanced proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:13,459][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values between you and Alice, the optimal strategy would be to propose a fair split to ensure that neither party gains an unfair advantage. Since the values are distributed such that we both have hats and balls valued highly by the other, a strategic proposal would be to propose an equal split of the items. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:13,481][mllm.models.large_language_model_local][WARNING] - Response Given our per-item values and Bob's values, we should propose to take the items that we value the most to maximize our points. Since we value hats and balls highly and Bob values books more, a strategic proposal would be to take as many hats and balls as possible while leaving some books for Bob. Here’s a balanced and strategic proposal: Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:13,705][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:13,728][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Alice value the items differently. You value hats the least, books moderately, and balls the most, while Alice values hats the most, books moderately, and balls the least. To maximize your points, you should propose a split that aligns with your values while also considering what Alice might propose based on her values. Given that you value books and balls the most and Alice values books and balls similarly, you should allocate items in a way that maximizes the items you value the most. A good strategy might be to propose keeping all the books and most of the balls, and splitting the hats equally. Proposal: 5 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:13,733][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:13,736][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:13,741][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:13,744][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:13,747][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:13,760][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:13,761][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:13,762][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:13,764][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:13,768][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:13,770][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:13,773][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:13,775][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:13,776][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:13,808][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's beneficial to split the items in a way that maximizes the weighted sum, considering both your and Bob's valuations. Here, hats and balls are highly valued by one of the players, making a strategic split crucial. Proposal: 10 hats, 10 books, 10 balls This proposal assumes that we will divide the books equally since they have moderate value for both of us. This strategy aims to maximize our points by focusing high-value items on one side and sharing lower-value but more numerous items equitably. However, given the symmetry and that both of us have the same valuations, it's optimal to split the books and balls equally to avoid any dissatisfaction or gain advantages from one another. Proposal: 10 hats, 5 books, 5 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:13,812][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the previous outcomes, a fair and optimal strategy would be to propose an equal split of each item. This ensures that both parties receive a reasonable share, maximizing the likelihood of receiving close to their true value for each item. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:13,814][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:13,816][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:13,895][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:13,898][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:13,905][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:13,982][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:14,068][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:14,070][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:14,158][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:14,162][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:14,166][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:14,240][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:14,242][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:14,334][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:14,338][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:14,424][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both you and Alice place high value on different items. You value hats (10) and balls (10) highly, while Alice values books (10) and hats (1) highly. In the previous rounds, proposing to take all items worked well because it maximized the allocation in your favor due to your high values for hats and balls. However, proposing to take all items might not always be the best strategy, especially if Alice anticipates your strategy and adjusts her own proposals accordingly. To optimize this round, a balanced approach might be more strategic. Proposal: 10 hats, 10 books, 10 balls This proposal ensures that both you and Alice get an equal share of the items, which could help avoid a situation where Alice decides to take all the books and balls in future rounds if she anticipates you always taking everything. This could lead to more stable and potentially higher total points over multiple rounds. So, the proposal is: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:14,428][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:14,432][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:14,435][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:14,517][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Alice have a high valuation for different items. To maximize points, you should focus on claiming the items you value more while preventing Alice from getting items she values highly. Here's a proposal that tries to balance this: Proposal: 10 hats, 10 books, 10 balls This proposal keeps the distribution relatively even, ensuring that you get the books and balls that you value highly, while also leaving some hats for Alice since she values hats slightly more than you do. This approach should help maintain a fair split while maximizing your points. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:14,522][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:14,607][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:14,611][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:14,862][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:14,866][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:14,883][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the game and the per-item values, it seems that proposing to take all of each item is leading to equal outcomes where both Alice and Bob receive the same points. This might not be the optimal strategy in the long term since it doesn't account for the differential values each of us places on the items. In this round, let's try a different approach to see if we can maximize our points. We should consider the values more strategically: - Hats: Both Alice and Bob value hats at 10, so it's a tie. - Books: Alice values books at 10 and Bob at 10, so it's a tie again. - Balls: Alice values balls at 1 and Bob at 10, so Bob values balls much higher than Alice. Given this, a more strategic approach would be to propose keeping all the balls, which Bob values significantly higher, and splitting the hats and books in a way that maximizes our value slightly better than Bob's perceived valuation. Proposal: 5 hats, 5 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:14,890][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both you and Bob value hats and books highly, but have different preferences for balls. You value balls the highest (10), and Bob values hats the highest (10). This suggest that both of you might try to secure as many items as possible early on, but given the value asymmetry, there's an opportunity to split the balls fairly to maximize points. A reasonable strategy is to propose a split that reflects the respective values for each type of item. Here’s a proposal that balances the high-value items while ensuring you get a fair share of the balls: Proposal: 10 hats, 10 books, 5 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:14,904][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:14,948][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:14,950][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:14,952][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:14,954][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:14,956][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:14,958][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:15,018][mllm.models.large_language_model_local][WARNING] - Response Given the values, you have a higher affinity for hats and books, while Bob has a higher affinity for balls. Proposing an equal distribution might not be the most optimal strategy in this round. Instead, you should try to negotiate a fair distribution that maximizes your points based on your higher value for hats and books. Proposal: 10 hats, 10 books, 10 balls However, a more strategic proposal might be to allocate more hats and books to yourself and more balls to Bob, recognizing the high mutual values. Let's try: Proposal: 10 hats, 10 books, 10 balls But to adjust for the mutual values, we might want to propose slightly less balls and more hats and books: Proposal: 8 hats, 8 books, 14 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:15,100][mllm.models.large_language_model_local][WARNING] - Response Given the values, it seems that both you and Alice place a high value on different items. To optimize your earnings, you might want to consider proposing a split that takes advantage of Alice's lower valuation on "balls" and your own higher valuation on "books". Here’s a proposal that maximizes your points: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:15,388][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric nature of the per-item values and the fact that both Alice and Bob will likely follow a similar strategy, it's beneficial to propose a fair split that maximizes the points for both parties. Since the per-item values are symmetrical in a way that we both value hats and balls highly and books less, a reasonable proposal would be to divide the items equally. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:15,620][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:15,623][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:15,667][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the high value of hats and books for both parties, and the lower but still significant value of balls, the best strategy is likely to propose an equal split to maximize points. Here is the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:15,669][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:15,671][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:15,674][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:15,680][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:15,683][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:15,751][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:15,893][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:15,897][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:15,923][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:16,014][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's beneficial to consider the distribution of items that both you and Alice value highly. Since hats are valued at 10 by you and 1 by Alice, and balls are valued at 1 by you and 10 by Alice, a cooperative approach might still give us an edge. Proposal: 10 hats, 10 books, 10 balls This proposal ensures that both you and Alice get the items they value the most, and we will split the books, which have middle value for both of us, evenly. However, given the symmetry and the values, let's slightly adjust the proposal to see if we can maximize our points without exceeding the item quantities. Proposal: 10 hats, 9 books, 11 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:16,059][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:16,061][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:16,064][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:16,065][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:16,111][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Alice value books and balls significantly more than hats. Since your values for books and balls are the same as Alice's for hats, and the items are split equally, a fair proposal would be to split them equally to avoid any risk of losing points from poorly allocated items. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:16,113][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:16,116][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:16,202][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:16,204][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:16,208][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:16,209][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:16,210][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:16,211][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:16,295][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both you and Alice value hats and books much more than balls. However, to maximize points, it's important to propose a distribution that is as close as possible to the true value of each item while ensuring the total does not exceed the available items. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:16,299][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 1 books, 1 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:16,383][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:16,471][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:16,475][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:16,477][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:16,480][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:16,555][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:16,559][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:16,646][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the fact that both you and Alice have similar valuations for different items, it's important to ensure that you propose a fair split to maximize your points. Here, you both have a strong valuation for hats and books, and a weaker valuation for balls. However, since you have a higher valuation for books and a similar valuation for hats, it might be strategic to slightly adapt your proposal to account for these differences. Proposal: 10 hats, 10 books, 9 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:16,654][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:16,658][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:16,660][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:16,662][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:16,666][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:16,668][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:16,671][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:16,674][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:16,676][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:16,736][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:16,991][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:16,995][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:17,002][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:17,005][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:17,009][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:17,016][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:17,022][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:17,155][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:17,162][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:17,237][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:17,241][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:17,324][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:17,328][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:17,331][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:17,408][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:17,412][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:17,416][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:17,496][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's beneficial to focus on the items where both you and Bob have high values to ensure a higher allocation. In this case, both of you value hats and books highly, and balls are also valued, but not as highly by either of you. To maximize points, I would propose dividing the items as follows: - Hats: Since both you and Bob value hats highly, it makes sense to split them equally. - Books: Similarly, split them equally. - Balls: Split them equally as well, though the value is lower, it's better to avoid leaving balls to Bob entirely. Proposal: 5 hats, 5 books, 5 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:17,503][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric nature of the per-item values and the fact that both you and Alice have the same values for hats and balls but different values for books, a fair strategy would be to propose equally to avoid any advantage one might gain from imbalance. Here is a balanced proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:17,507][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:17,513][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:17,831][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:17,914][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:17,918][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:18,002][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:18,083][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:18,173][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:18,228][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:18,232][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:18,250][mllm.models.large_language_model_local][WARNING] - Response Given the high value of balls for both Alice and Bob, and the similar per-item values, a cooperative strategy where we both end up with a fair distribution of items seems optimal. Since we have identical valuations, splitting the items evenly would ensure that each agent maximizes their points. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:18,257][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:18,269][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:18,272][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:18,273][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:18,274][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 1 books, 1 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:18,302][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:18,348][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:18,368][mllm.models.large_language_model_local][WARNING] - Response Given the values, it appears that both you and Alice have a high valuation for hats and books, and a low valuation for balls. The optimal strategy would be to compete for the items valued most highly by both of you. Here, proposing to take all of the hats and books, while leaving the balls for Alice, could be a reasonable approach. Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:18,389][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:18,411][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:18,414][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:18,417][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:18,421][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:18,424][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:18,440][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:18,444][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:18,447][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:18,450][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:18,453][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:18,467][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Bob place high value on hats and books, while balls have relatively low value. However, since the values are equal and both of you are likely aiming to maximize your own value, the optimal strategy might be to mirror Bob's proposal to ensure a fair split. This way, you both avoid the allocation being split proportionally, which could result in a lower earning for you. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:18,470][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:18,473][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:18,515][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:18,517][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:18,519][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:18,520][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:18,556][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:18,560][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:18,566][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:18,603][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:18,606][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:18,643][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:18,646][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:18,653][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:18,655][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:18,658][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:18,662][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:18,664][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:18,667][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:18,670][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:18,673][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:18,677][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:18,706][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:18,709][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:18,711][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:18,714][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:18,726][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:18,741][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Alice have significant value for hats and books, while balls have less value for Alice compared to you. Based on previous rounds, it seems Alice is likely to propose keeping all the balls for herself. Given this, you might want to propose keeping as many hats and books as possible to maximize your points. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:18,746][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:18,750][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:18,787][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric and competitive nature of the per-item values between you and Alice, proposing to take all items ensures that we maximize our points if our proposal is accepted. Here's the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:18,843][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:18,849][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:18,855][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:18,862][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:18,872][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:18,905][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:18,945][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the fact that both agents have equal chances of receiving different items, proposing to take all items (10 hats, 10 books, 10 balls) ensures that we maximize our potential earnings in each round. Therefore, I propose: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:18,949][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:18,966][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:18,988][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:18,995][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:19,001][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:19,005][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:19,008][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:19,012][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:19,015][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:19,017][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:19,019][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:19,045][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:19,047][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:19,049][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:19,052][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:19,078][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:19,084][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:19,103][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:19,128][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:19,151][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:19,154][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:19,195][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:19,198][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:19,218][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:19,224][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:19,230][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:19,236][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:19,241][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:19,247][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:19,252][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:19,260][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:19,262][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:19,265][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:19,267][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:19,270][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:19,272][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:19,276][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:19,282][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:19,284][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:19,286][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:19,303][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:19,411][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric value structures and the goal to maximize points, it's important to consider the strategic distribution of items. Since both you and Bob value hats and books highly and balls less, a balanced proposal that takes both your value and Bob's value into account might be optimal. Here's a strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:19,416][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:19,419][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:19,482][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:19,489][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:19,543][mllm.models.large_language_model_local][WARNING] - Response Given Bob's valuation, he will likely propose to take all the books and balls for the high value he places on books and the medium value he places on balls. To maximize my points, I should propose to take all the hats and books, leaving the balls for Bob. Here’s my proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:19,547][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:19,591][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:19,593][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:19,597][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:19,796][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:19,799][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:19,803][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:19,833][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's important to propose a distribution that reflects the high value you place on books and balls, while also considering Bob's valuation. Here's a strategic proposal: Proposal: 10 hats, 5 books, 5 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:19,836][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:19,843][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:19,886][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:20,614][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:20,694][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:20,698][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:20,700][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:20,703][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:20,706][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:20,714][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:20,717][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:20,787][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:20,791][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:20,794][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:20,797][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:20,799][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:20,962][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in per-item values between you and Alice, a strategic approach would be to propose an allocation that reflects the true value each of you places on the items. Since you value hats and balls at 10 and books at 1, and Alice values books and balls at 10 and hats at 1, proposing a fair split that maximizes the total value for both parties could be beneficial. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:20,966][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:20,969][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:21,049][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:21,052][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:21,055][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:21,058][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:21,060][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:21,269][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:21,272][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:21,316][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:21,319][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:21,322][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:21,366][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:21,373][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:21,403][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the values and the previous rounds' outcomes, it seems proposing the entire set of items might still be the optimal strategy, as it matches Bob's valuation of these items and maximizes the points for both parties proportionally. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:21,409][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:21,416][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:21,419][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:21,465][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:21,499][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that hats and books are less valuable to you but very valuable to Bob, while balls are highly valuable to you but less valuable to Bob. To maximize your points, you should focus on ensuring you get a significant portion of the balls while also strategically grabbing some hats and books to avoid Bob getting all of them. Proposal: 10 hats, 10 books, 10 balls This proposal ensures that you get the maximum value from the balls and also secures some hats and books, preventing Bob from getting all of them. However, given Bob's high value for hats and books, it might be beneficial to slightly adjust the proposal to balance the distribution and ensure you receive a fair share of the valuable items. Proposal: 10 hats, 0 books, 10 balls This proposal ensures that you get all the balls (worth 10 points each, so 100 points) and all the hats (worth 1 point each, so 10 points), and zero books (worth 1 point each, so 0 points). This maximizes your points while still giving Bob something of value (books). So, the adjusted proposal is: Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:21,502][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Alice have high value for books and balls, and low value for hats. Since the items are split proportionally, we need to propose a distribution that maximizes our value while balancing the likelihood of Alice agreeing to it. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:21,518][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:21,521][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:21,524][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:21,526][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:21,533][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:21,539][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:21,545][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:21,571][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:21,592][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both you and Bob place high value on different items. To maximize your points, you should propose a split that aligns with your higher valuation of books. Here's a strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:21,597][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:21,600][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:21,602][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:21,682][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:21,685][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:21,687][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:21,690][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:21,693][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:21,695][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:21,779][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:21,786][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:21,792][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:21,861][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:22,071][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:22,074][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:22,077][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:22,092][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both hats and balls have high values for one of the players. To maximize points, we should try to ensure we get the items that are highly valued by us. Here's the optimal proposal: Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:22,099][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:22,105][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:22,108][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:22,115][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and the history of the game, it's clear that proposing to take all items will result in a proportional split if Bob doesn't propose to take all the items himself. To maximize my points, I should propose to take all the items: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:22,158][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:22,161][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:22,175][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both hats and balls are highly valued by both of us, while books are less valued. To maximize points, we should try to capture as many balls as possible since they provide the highest points per item. Here's a strategic proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:22,182][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:22,188][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:22,264][mllm.models.large_language_model_local][WARNING] - Response Given the dynamic in this round where both you and Bob have strong preferences for different items, it's crucial to consider a strategy that maximizes your points. Since you value hats and books more than balls, and Bob values balls more, it makes sense to propose a split that reflects these values while still potentially leading to a proportional allocation. Proposal: 10 hats, 10 books, 10 balls This proposal ensures that both you and Bob get a fair share of the items, considering your respective valuations. However, let's consider a slightly refined proposal to maximize your points: Proposal: 6 hats, 6 books, 8 balls This proposal prioritizes getting the high-value items (hats and books) for you, while still receiving a significant number of the balls, which you value but not as highly as hats or books. So, the refined proposal is: Proposal: 6 hats, 6 books, 8 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:22,267][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and equal worth in this round, proposing an equitable distribution seems optimal. Since your per-item values are much higher for balls and you both have similar values for hats and books, a balanced distribution would be: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:22,273][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:22,285][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:22,288][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:22,291][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:22,306][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:22,309][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:22,421][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:22,450][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:22,474][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:22,476][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:22,481][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:22,506][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:22,552][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:22,638][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:22,735][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:22,737][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:22,757][mllm.models.large_language_model_local][WARNING] - Response Given the values, you have a high value for hats (1) and Alice has a high value for balls (10). To maximize your points, you should take as many hats as possible while also taking some books to ensure you get a significant part of the value from them. Since the value of balls is low for you, you can give most of the balls to Alice. Proposal: 10 hats, 10 books, 0 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:22,764][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, the optimal strategy would be to maximize the items with higher personal value. Since "books" have a value of 10 for you and "balls" have a value of 10 for Alice, it's crucial to secure as many books as possible while also considering the distribution of balls. Here's the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:22,825][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:22,843][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:22,849][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:22,856][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:22,874][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:22,877][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:22,892][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:22,895][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:22,977][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:22,980][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:22,983][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:23,081][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:23,084][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:23,095][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:23,099][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:23,114][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the previous round results, it seems that proposing to take all items is a reasonable strategy, as both players are likely to value different items differently. However, if Alice is also proposing to take all items, it might lead to a proportional distribution if the total proposals exceed the available items. To maximize points and considering the previous rounds where splitting is fair, I will propose to take all items to ensure I get the highest possible score based on my per-item values. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:23,117][mllm.models.large_language_model_local][WARNING] - Response Given that both you and Bob have significant values for different items, a cooperative strategy might maximize the total points in each round. Since both values are aligned in a way that if we split the items evenly, neither of us would receive a lower total value than what we propose, a fair split could be optimal. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:23,120][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:23,123][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:23,126][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:23,162][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:23,165][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:23,168][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:23,171][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:23,202][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:23,357][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both you and Bob value hats and books more than balls, but in different ways. You value hats and books more relatively, while Bob values books and balls more. Proposing to take the majority of the items might be a strategic choice. However, to ensure a fair and maximized outcome, we can propose an equal split that respects the value each of us places on the items. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:23,361][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:23,364][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:23,367][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:23,374][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:23,377][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:23,528][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it is clear that both you and Alice value hats and balls less than books. However, since both of you value books equally low (1), we need to consider the distribution that maximizes the points for the items we value more. Here's the breakdown: - You value hats (10) and balls (10) highly, and books (1) not so much. - Alice values books (10) highly and hats (1) and balls (10) not as much. To maximize your points, you should propose to claim as many hats and balls as possible since they have higher values for you. Here is a strategic proposal: Proposal: 10 hats, 10 books, 10 balls This proposal ensures that you get all the items, which will maximize your points given your per-item values. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:23,532][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:23,535][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:23,546][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:23,553][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:23,556][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:23,566][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:23,569][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:23,654][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:23,657][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:23,671][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:23,688][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:23,695][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:23,701][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:23,704][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:23,736][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:23,738][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:23,741][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:23,756][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the values, proposing an equal distribution seems fair and strategic. Here’s the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:23,758][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:23,761][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:23,763][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:23,848][mllm.models.large_language_model_local][WARNING] - Response Given the similarity in values and the symmetry in the game, proposing to take all items ensures that we maximize our potential earnings if we are able to get them. Here is the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:23,865][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:23,872][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:23,882][mllm.models.large_language_model_local][WARNING] - Response Given the asymmetrical values, it makes sense to try allocating items based on their respective values to maximize points. Since I value hats the least (1 point each) and Alice values them the most (10 points each), I should claim all hats to maximize my points, and let Alice take the books and balls which we both value highly. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:23,885][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:23,888][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:23,891][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:23,894][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:23,928][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:23,931][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:24,000][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:24,007][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:24,021][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:24,028][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:24,038][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:24,079][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:24,096][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric nature of the per-item values and the equal treatment of items in each round, it makes sense to propose an equitable distribution to maximize points. Here's the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:24,099][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:24,134][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:24,141][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:24,148][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:24,171][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:24,176][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:24,181][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:24,183][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:24,185][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:24,187][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:24,192][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:24,195][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:24,198][mllm.models.large_language_model_local][WARNING] - Response Given the values, you have a strong preference for balls and Bob has a strong preference for hats. Since both of you have a low value for books, proposing to take all the balls and none of the hats or books would maximize your points. Proposal: 10 hats, 0 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:24,269][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:24,305][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:24,308][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:24,314][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:24,321][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:24,337][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:24,341][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:24,346][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:24,349][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:24,464][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:24,536][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:24,584][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric and equal distribution of values in this scenario, proposing to take all items appears to be a fair and strategic move. This ensures that we maximize our potential earnings based on our high values for hats and books, and our moderate value for balls. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:24,606][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:24,610][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:24,613][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:24,617][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:24,620][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:24,623][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:24,630][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:24,637][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:24,775][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:24,790][mllm.models.large_language_model_local][WARNING] - Response Given the values, I should focus on taking more of the items I value highly (hats and books) and less of the item Bob values highly (balls). Here's the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:24,794][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:24,801][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:24,804][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:24,819][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:24,826][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:24,829][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:24,933][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:24,937][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:24,952][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:24,959][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:24,962][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:25,007][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and understanding that Bob values hats highly, it's beneficial to claim as many balls as possible to maximize your points. Here’s the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:25,022][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, you should propose a distribution that takes advantage of the high value of hats and books for you and the high value of balls for Alice. Here is a proposal that maximizes your value: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:25,056][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:25,059][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:25,072][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:25,075][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:25,078][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:25,081][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:25,087][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:25,095][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:25,098][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:25,101][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:25,103][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:25,119][mllm.models.large_language_model_local][WARNING] - Response Given the new per-item values, where you value hats at 10, books at 1, and balls at 1, and Alice values hats at 1, books at 10, and balls at 1, it's beneficial to propose a distribution that maximizes your value. Since you value hats much higher than books or balls, and Alice values books higher than hats, a strategic proposal would be to take as many hats as possible to maximize your points. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:25,124][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, it's clear that both you and Alice place high value on different items. You value books and balls highly, while Alice values hats and books. Since the items are to be split proportionally and both of you propose the same amounts, the best strategy is to mirror Alice's proposal to ensure that the split remains fair and maximizes the points for both of you. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:25,164][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and the strategic considerations, the optimal proposal would be to allocate the items in a way that maximizes your total points. Here, you value hats and books more than balls, while Bob values balls more than hats and books. Therefore, you should propose to take more of the items you value higher and divide the less preferred items more equitably. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:25,196][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:25,200][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:25,202][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:25,205][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:25,208][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:25,247][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:25,250][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:25,300][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the values at stake, a fair and optimal strategy is to propose a split that respects the value each item holds for both parties. Here, books and balls are particularly valuable for one party each, while hats have equal value to both. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:25,307][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:25,319][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:25,322][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:25,337][mllm.models.large_language_model_local][WARNING] - Response Given the values, it's clear that both you and Alice value hats and balls very highly, but Alice values books more than you do. To maximize your points, it's important to offer a splitting that meets your high value for hats and balls while considering Alice's higher valuation for books. Proposal: 10 hats, 10 balls, 10 books did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:25,405][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:25,436][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:25,439][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:25,477][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:25,484][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:25,494][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:25,497][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:25,500][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:25,512][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:25,515][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:25,518][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:25,520][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:25,551][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:25,591][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:25,607][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:25,614][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:25,620][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:25,623][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:25,640][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:25,643][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:25,648][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:25,651][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:25,713][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:25,728][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:25,735][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:25,745][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and the symmetric nature of Alice's strategy, it seems optimal to propose a distribution that maximizes your benefit given your higher value for balls and Alice's for hats. Here is a proposal that aims to balance the distribution: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:25,749][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:25,751][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:25,767][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in per-item values and the previous outcomes where proposing to take all items results in a fair split and maximum points, I will propose to take all items again. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:25,821][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:25,838][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:25,844][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:25,875][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:25,882][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:25,889][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:25,908][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:25,911][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:25,914][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:25,920][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:25,928][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:25,931][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:25,972][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:26,008][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:26,043][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:26,050][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:26,057][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:26,072][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:26,078][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:26,085][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:26,088][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:26,132][mllm.models.large_language_model_local][WARNING] - Response Given the values, proposing to take all items seems to be a strategically optimal move since the distribution is likely random, and you want to maximize your points. Here's the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:26,167][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:26,174][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:26,250][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:26,334][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:26,337][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:26,344][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:26,356][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:26,359][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:26,394][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values, you should propose a split that maximizes your expected payoff. Since your values are high for hats and Alice values hats very low, you should keep all the hats. You also should keep most of the books, to capture their high value for you, while allowing Alice to take some balls to capture their low value for her. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:26,401][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:26,486][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:26,488][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:26,490][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:26,505][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:26,511][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:26,524][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:26,573][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:26,587][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:26,589][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:26,630][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:26,633][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:26,644][mllm.models.large_language_model_local][WARNING] - Response Given the dynamic of the game, where both Alice and Bob are likely to propose the full allocation of items to maximize their points based on their individual valuations, a cooperative strategy might not be optimal. Here, Alice values hats and books highly and balls moderately, while Bob has the opposite valuation. To secure as many points as possible, we can propose a split that maximizes our individual value while ensuring the total allocation does not exceed the available items. Proposal: 10 hats, 10 books, 1 balls This proposal gives Alice all the hats and books, which aligns with her high valuations, while taking one ball, which still provides a significant value. This proposal should maximize Alice's points given the items and valuations. did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:26,646][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:26,687][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:26,702][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:26,705][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:26,718][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:26,720][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:26,734][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the fact that both you and Alice have the same per-item values for hats and balls, and different but complementary per-item values for books, a fair and strategic proposal would be to allocate the items according to their values. Here, both you and Alice value hats and balls the same, and the books are valued differently but not enough to cause conflict in terms of splitting. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:26,737][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:26,739][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:26,830][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:26,836][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:26,848][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:26,851][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:26,914][mllm.models.large_language_model_local][WARNING] - Response Given the high value of balls for both of us, and the relatively lower value for hats and books, it's important to ensure we get a significant portion of the balls. Here's a balanced proposal that maximizes the points for both hats and balls: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:26,917][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:26,920][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:26,959][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:26,971][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:27,012][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:27,025][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the situation and the per-item values, proposing an equal share can still be a strategic approach. However, since balls are highly valued by one player and hats and books are valued equally by both, dividing the items evenly might not maximize the potential points for the balls. One might consider slightly favoring the allocation of balls to the player with higher valuation. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:27,042][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:27,045][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:27,059][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:27,065][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:27,068][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:27,071][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:27,074][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:27,106][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:27,110][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:27,153][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:27,160][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:27,163][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:27,171][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:27,174][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:27,216][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:27,219][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:27,265][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:27,280][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:27,340][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:27,356][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:27,360][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:27,363][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:27,370][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:27,380][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:27,387][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:27,390][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:27,391][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:27,427][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:27,429][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:27,460][mllm.models.large_language_model_local][WARNING] - Response Given the values, it seems optimal to focus on items where both you and Bob have higher values, to maximize the points earned. Here, "books" and "balls" are the items with higher values for both of you, while "hats" have lower values but are still valuable to you. Proposal: 10 hats, 10 books, 10 balls However, to potentially be more strategic and balance the allocation, consider splitting the "books" and "balls" more evenly while keeping "hats" to the max. Proposal: 10 hats, 9 books, 11 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:27,486][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:27,494][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:27,501][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:27,504][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:27,548][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:27,588][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and the symmetry in the situation, a fair and strategic proposal would be to split the items equally to maximize the points based on our respective values. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:27,616][mllm.models.large_language_model_local][WARNING] - Response Given the high value of books and balls for both you and Bob, and the similar values for hats, a cooperative approach seems beneficial. Let's split the items as equally as possible to maximize the total value for both of us. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:27,690][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:27,694][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:27,697][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:27,699][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:27,738][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:27,741][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:27,834][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:27,838][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:27,864][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry and the equal per-item values between hats and books for both of us, and the significantly different values for balls, a strategic proposal would be to split the items as evenly as possible while maximizing the value of books and balls. Here's a proposal that aims to exploit the value differences: Proposal: 10 hats, 10 books, 10 balls This proposal evenly distributes all items, which might lead to a proportional distribution based on our values. However, given the values, we can propose to take all books and balls to maximize our score. Here is a revised proposal: Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:27,962][mllm.models.large_language_model_local][WARNING] - Response Given the high value you place on hats and books, and considering Alice values them similarly, it's beneficial to propose a distribution that captures the full value of these items. Here's the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:27,974][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:28,026][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:28,027][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:28,039][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:28,040][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:28,042][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:28,043][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:28,044][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:28,045][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:28,176][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:28,179][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:28,203][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:28,206][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:28,344][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:28,347][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:28,358][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:28,361][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:28,367][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:28,370][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:28,373][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:28,375][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:28,377][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:28,382][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:28,384][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:28,479][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:28,483][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:28,509][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:28,512][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:28,553][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:28,566][mllm.models.large_language_model_local][WARNING] - Response Given the symmetric nature of the per-item values and the potential for proportional allocation, a strategic proposal would be to mirror Bob's values in a way that maximizes the benefit of high-value items while ensuring a fair split. Here’s a revised proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:28,570][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:28,617][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:28,628][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:28,635][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:28,646][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the equal distribution of items, a rational approach would be to propose an equal split to ensure that both you and Alice receive a fair share. This strategy maximizes the likelihood of receiving a value close to the maximum possible in each round. Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:28,649][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:28,655][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:28,661][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:28,667][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:28,670][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:28,671][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:28,674][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:28,750][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:28,757][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:28,779][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:28,820][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:28,834][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:28,840][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:28,914][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:28,951][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:28,958][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:28,965][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:28,971][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:28,974][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:29,042][mllm.models.large_language_model_local][WARNING] - Response Given the per-item values and considering that both you and Bob have high preferences for different items, a strategic proposal would be to try to allocate the items in a way that maximizes the total value for both parties. Here, you value hats and books more highly and Bob values balls more highly. Let's propose an allocation that balances our values: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:29,113][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:29,125][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:29,163][mllm.models.large_language_model_local][WARNING] - Response Given the symmetry in the per-item values and the fact that both you and Alice value the same items in different ways, a fair and optimal approach would be to propose an even split that maximizes the value for both of you. Here’s a balanced proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:29,188][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:29,191][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:29,229][mllm.models.large_language_model_local][WARNING] - Response Proposal: 0 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:29,241][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:29,300][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:29,304][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:29,390][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:29,415][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:29,418][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:29,533][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:29,546][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:29,648][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:29,652][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:29,662][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:29,665][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:29,784][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:29,895][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:29,909][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:29,912][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:29,990][mllm.models.large_language_model_local][WARNING] - Response Given the current round's item values, where you value hats at 1, books at 10, and balls at 1, and Alice values them as hats at 10, books at 1, and balls at 1, it's beneficial to propose an equal split that maximizes your value. Here's the proposal: Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:30,116][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:30,119][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:30,510][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:30,513][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 1/3 [2025-11-13 07:13:30,703][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:30,704][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 2/3 [2025-11-13 07:13:30,898][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3 [2025-11-13 07:13:30,900][mllm.models.large_language_model_local][WARNING] - Response Proposal: 10 hats, 10 books, 10 balls did not match regex: (?i)Proposal:\s*((?:\s*(?P(10|[0-9]))\s*(?Phats?|books?|balls?)\s*,?)+), retry 3/3